JP4218669B2 - Manufacturing workplace evaluation method - Google Patents

Description

  The present invention relates to the ease of occurrence of defects in manufacturing workplaces (capabilities) in manufacturing workplaces that are manufactured by assembling or processing parts such as home appliances and OA products, and the causes of defects that cause defects in the workplace environment. The present invention relates to a manufacturing workplace evaluation method for evaluating room for improvement.

  The conventional technique is mainly a technique for estimating the cause of occurrence from a defect phenomenon or failure phenomenon that has actually occurred. Patent Documents 1 and 2 are known examples of techniques for estimating the cause of a defect from the contents of a defect phenomenon that has occurred in the manufacturing stage. In Patent Document 1, a cause of failure is set in correspondence with a combination of appropriate and inappropriate items after the workpiece is inspected, and the degree of correlation between the combination of appropriate and inappropriate items and the cause of the failure is corrected. A work reworking method is described in which the cause is evaluated based on the result of the above, and the cause of the failure is estimated based on the degree of correlation among the failure causes corresponding to the combination of appropriate and inappropriate items, and the cause of the failure is resolved by reworking. Prior art 2 compares the quality results input in each process of printing, mounting, and soldering for mounting electronic components by printed circuit board, and shows the possibility that defects may occur in relation to each process. There is described a mounting process failure factor analysis method for calculating and predicting the degree of influence of each process on the final failure with reference to failure related rules.

  Examples of using the same method for failure diagnosis include Patent Document 3 and Patent Document 4. In Patent Document 3, for each malfunction event, a phenomenon group representing the situation of the malfunction event is compared with a cause group that causes the malfunction event, and a related value indicating the degree of relation to the cause associated with each phenomenon is obtained. Create a defined relationship table, determine the phenomenon that occurred in association with the failure event, calculate the integrated value of each cause of the relationship table by integrating the related value for the determined phenomenon, weighted in a predetermined way, There is described a cause estimation method of a failure event in which the cause of the maximum integrated value is the cause of the failure phenomenon. Prior Art 4 uses knowledge data that uniquely describes the cause of failure and the failure phenomenon, searches for the failure phenomenon, generates a hypothesis, and then theoretically analyzes the causal relationship between the failure phenomenon and the cause of failure. A failure diagnosis apparatus that verifies the hypothesis using knowledge data configured as described above is described.

  The above Patent Documents 1, 2, 3, and 4 are all for quickly and accurately performing repairs and repairs based on the contents of the phenomena that actually occurred when a failure or failure occurs. There is a technique for estimating the direct cause based on past events.

  On the other hand, FMEA (Failure Mode Effect Analysis) mainly used in the product design stage (described in Non-Patent Document 1) is a method for evaluating the quality of a product to be manufactured before a defect or failure actually occurs. .)It has been known. In this method, the evaluator himself estimates “a failure phenomenon that can occur in each part of the product” and summarizes the failure phenomenon for each part in a table format. As a result, the evaluator himself can estimate “what effect will the product have when it occurs”, and quality design without omission becomes possible.

  Also, in FMEA (Failure Mode, Effect & Criticalty Analysis), FMEA gives the probability (failure rate) of failure phenomenon of individual parts estimated by the evaluator, and further, it is caused by failure of the individual parts There is also a method for inferring the importance of an estimated product failure and inferring the importance of each component failure or failure.

Further, as a method for evaluating the high quality level of products manufactured by factories, subcontract factory inspection check sheets prepared by various companies for the purpose of subcontract factory inspection are generally known. In addition, “factory diagnostic apparatus” (Patent Document 5), “mounting factory diagnostic system” (Patent Document 6), and the like are known as methods for evaluating the productivity of a factory.
Japanese Patent Laid-Open No. 1-167631 JP-A-6-196900 JP-A-7-13617 Japanese Patent Laid-Open No. 7-271587 JP-A-9-62309 JP 10-79599 A Nikkan Technology Reliability Engineering Series 7 “Utilization of FMEA and FTA”

  However, the techniques for estimating the direct cause based on the above-mentioned past events, FMEA and FMECA, both need to grasp most of the failure phenomena that can actually occur, so the potential of the product has defects. Cannot be estimated with high accuracy.

  Therefore, at present, many manufacturing defects due to omission of examination occur, which is a factor of quality deterioration.

  In addition, the subcontracting factory examination check sheet evaluates the quality of the system and system for development, manufacturing, quality assurance, etc. of the factory to be evaluated, and quantifies the degree of defect occurrence at the factory to be evaluated from the evaluation results. Cannot be evaluated. In addition, the conventional techniques 5 and 6 for evaluating the productivity of the factory cannot evaluate the degree of occurrence of defects in the manufacturing workplace.

  Thus, none of the conventional techniques related to the manufacturing workplace is sufficient for quantitatively evaluating the degree of occurrence of defects in the manufacturing workplace. For example, it is difficult to evaluate two factories and evaluate which factor has a high degree of defect occurrence.

An object of the present invention is to solve the above-described problems by performing manufacturing work such as assembly or processing in a manufacturing workplace (including a factory) where the product is to be manufactured at a pre-manufacturing stage such as a design stage or a manufacturing process planning stage. Evaluation category that is estimated to be likely to cause defects in the manufacturing workplace by estimating the likelihood of occurrence of defects in the manufacturing workplace when performing the inspection, and is expected to be relatively effective by taking countermeasures in advance It is another object of the present invention to provide a manufacturing workplace evaluation method for assisting a user of a system that implements the method of the present invention to select a work condition bad influence item .

In order to achieve the above object, the present invention evaluates the ease of occurrence of defects in a manufacturing workplace to be evaluated using an evaluation apparatus including a storage means, an input means, an evaluation means, and an output means, and there is room for improvement. Provide an evaluation method for manufacturing workplaces that presents large cause-of-failure items. That is, a plurality of workplace condition defect effect items related to an evaluation category consisting of preset workers, production equipment, production conditions, production physical environment, and management, and each workplace condition defect effect item at least at the standard workplace level level A defect creation coefficient that is a defect generation suppression ability for a standard manufacturing operation, a defect handling time coefficient that is a countermeasure capacity when a defect occurs, and a defect extraction degree coefficient that is a defect detection capability are set, and the above-mentioned workplace condition defect influence items A workplace database storage process in which a workplace evaluation database in which a relative weighting coefficient between items is set is created in advance and stored in the storage means, and the workplace conditions are poor in the manufacturing workplace to be evaluated using the input means. The workplace level input process for inputting at least the workplace level for the impact item, and the evaluation method Is input in the workplace level input process based on the set values of the defect creation coefficient, defect handling time coefficient, and defect extraction degree coefficient for the standard manufacturing work at least at the standard workplace level in the workplace evaluation database. Corresponding to the workplace level level for each of the workplace condition defect affecting items, and by multiplying the relative weight coefficient between items in each workplace condition defect affecting item, an index of the degree of defect creation, an index of the degree of defect handling time, And the degree of defect extraction, and for each workplace condition defect influence item, a defect occurrence degree that is a sum of a defect creation degree index, a defect handling time degree index, and a defect extraction degree index; An index of room for improvement is calculated based on the difference from the degree of failure occurrence in the ideal workplace, and the effect of reducing the degree of failure occurrence in each workplace condition failure effect item in the manufacturing workplace to be evaluated. An evaluation process for evaluating the severity, and a presentation process for supporting the determination of measures to be addressed by presenting the plurality of work condition defect effect items in the descending order of the room for improvement using the output means. It is a manufacturing workplace evaluation method.

Further, in the workplace database storing process, the workplace evaluation database further corresponds to each workplace condition bad influence item in the workplace evaluation database, and reduces the degree of failure occurrence at a level where the workplace level does not reach the ideal workplace. Information indicating advice or a comment indicating a countermeasure plan is prepared and stored in advance, and in the presentation process by the output means, the information is read from the workplace evaluation database, attached to each of the work condition adverse effect items. It is characterized by presenting advice or a comment indicating a countermeasure plan for reducing the degree of defect occurrence.

The present invention also provides a manufacturing workplace evaluation method that evaluates and estimates the likelihood of occurrence of defects in a manufacturing workplace to be evaluated using an evaluation device that includes a storage means, an input means, an evaluation means, and an output means. That is, a plurality of workplace condition defect effect items related to an evaluation category consisting of preset workers, production equipment, production conditions, production physical environment, and management, and each workplace condition defect effect item at least at the standard workplace level level A defect creation coefficient that is a defect generation suppression ability for a standard manufacturing operation, a defect handling time coefficient that is a countermeasure capacity when a defect occurs, and a defect extraction degree coefficient that is a defect detection capability are set, and the above-mentioned workplace condition defect influence items A workplace database storage process in which a workplace evaluation database in which a relative weighting coefficient between items is set is created in advance and stored in the storage means, and the workplace conditions are poor in the manufacturing workplace to be evaluated using the input means. The workplace level input process for inputting at least the workplace level for the impact item, and the evaluation method Is input in the workplace level input process based on the set values of the defect creation coefficient, defect handling time coefficient, and defect extraction degree coefficient for the standard manufacturing work at least at the standard workplace level in the workplace evaluation database. Corresponding to the workplace level level for each of the workplace condition defect affecting items, and by multiplying the relative weight coefficient between items in each workplace condition defect affecting item, an index of the degree of defect creation, an index of the degree of defect handling time, And for each of the evaluation categories, the sum of the indicators of the degree of defect creation, the sum of the indicators of the degree of defect handling time, the sum of the indicators of the degree of defect extraction, and the total thereof are calculated. Using the output means, a workplace evaluation process for calculating and evaluating the likelihood of occurrence of defects in the manufacturing workplace subject to evaluation for each evaluation category, An evaluation method of manufacturing workplace and having a presentation step of supporting the presentation to measures decided to tackle the failure of every categories.

  As described above, according to the above configuration, the degree of occurrence of defects during product manufacture is determined by the manufacturing structure conditions of the product and the workplace conditions in the manufacturing workplace where the product is manufactured.

  In particular, based on information on factors that affect the probability that humans cannot reliably perform manufacturing work operations such as assembly or processing (hereinafter referred to as uncertainty), estimate the defect rate estimated by assembly or processing. It was decided to calculate. Factors that affect the probability of humans not being able to reliably perform manufacturing work operations (hereinafter referred to as uncertainties) include two types of factors: difficulty of manufacturing work of the product structure and workplace environment.

  Therefore, in the present invention, first of all, to what extent the defect rate of a product is increased depending on the structural condition of the evaluation target product, information on the operation content of the manufacturing operation at the time of product manufacture, and the part to be manufactured Secondly, the degree of the manufacturing defect rate is increased depending on the workplace environment conditions of the workplace where the product is manufactured, with respect to one or more workplace environment items causing the defect. Estimate based on information indicating what level the workplace is in, and also estimate the defect rate when the product is manufactured at the manufacturing workplace based on the first and second estimation results above. It was.

  Specifically, a method for estimating how much the manufacturing defect rate is increased depending on the structural condition of the evaluation target product is specifically information on the operation content of the manufacturing operation (part assembly operation in the case of assembly), and the property of the part. The estimated value of the product defect rate was calculated based on the information regarding the product.

  Also, in the case of assembly, the type of operation necessary to express the operation content of the part assembly work is determined (downward movement operation, lateral movement operation, etc .; referred to as standard assembly operation), and the determined standard assembly For each operation, when performing the standard assembling operation under the “predetermined worker conditions, certain parts conditions, certain work workplace conditions” (referred to as reference conditions), ensure that the standard assembling operation is performed. It was decided to set a numerical value (referred to as the defective rate coefficient by standard assembly operation) indicating the magnitude of the probability of failure. In addition, the usability of the user interface has been improved by expressing the evaluation target as a combination of the preset standard assembly operation elements. Further, in order to further increase the accuracy of estimation of the assembly failure rate, in addition to the standard assembly operation element expressing the assembly operation content of the component assembly operation described above, it affects the uncertainty of the assembly operation. The properties of the parts (the parts to be assembled and the parts to be assembled) are expressed by the following component condition correction factors, and an estimated value of the assembly failure rate is calculated based on the expressed component condition correction factors. That is, a factor (hereinafter referred to as a component condition correction factor) that affects the uncertainty of the assembly operation performed by humans among the properties of the component is determined, and the influence factor is determined for each determined influence factor. A numerical value (hereinafter referred to as a component condition correction coefficient) indicating the degree of influence on the assembly operation is determined, and the assembly operation content is related to the standard assembly operation with respect to the component assembly operation subject to assembly failure rate estimation. In addition to the above-described combination, the component condition correction factor is selected from the preset component condition correction factors and applied to the property of the component in the component assembly work.

  According to the present invention, in the pre-manufacturing stage such as the design stage or the manufacturing process planning stage, the manufacturing workplace when the manufacturing work such as assembly or processing is performed in the manufacturing workplace (including the factory) where the product is to be manufactured. It is possible to evaluate and estimate the easiness of occurrence of defects, and to evaluate and estimate the quality of work defects, etc. of products manufactured by a series of manufacturing operations at the manufacturing workplace with high accuracy. Each department of quality assurance can accurately prevent and detect defects, and can greatly improve the reliability of shipped products.

  In addition, according to the present invention, since the estimated value of the defect rate can be estimated with high accuracy for each manufacturing operation of the product before product production at the product design stage, the manufacturing process planning stage, etc., the production with a high defect rate coefficient The work can be easily extracted, and as a result, by improving them, it is possible to efficiently and effectively reduce the defect rate of the product, and it is possible to design and manufacture the product with high reliability.

  In addition, according to the present invention, a manufacturing workplace in the case where manufacturing work such as assembly is performed in a manufacturing workplace (including a factory) where the product is to be manufactured at a pre-production stage such as a design stage or a manufacturing process planning stage. It is possible to evaluate and estimate the easiness of occurrence of defects with high accuracy and to improve the manufacturing workplace in advance.

Embodiments of manufacturing workplace evaluation according to the present invention will be described below with reference to the drawings.
By quantitatively evaluating using the manufacturing workplace evaluation method according to the present invention, it is possible to estimate the ease of occurrence of a failure in the workplace (defect occurrence rate: defect occurrence rate) even without actual defect data. In other words, the present invention has found that if quantitative evaluation is performed using an evaluation method in a manufacturing workplace, it substantially matches the actual result data of defects.

  Ease of occurrence of defects in the manufacturing workplace (defect occurrence level), that is, the capability of the manufacturing workplace, can be expressed as [defect generation suppression force] × [defect generation response capability] × [defect detection capability].

  [Defect occurrence suppression power] is a workplace power that does not produce defective products, and can be expressed as defect creation level = (ab) / (AB) in FIG. [Defect occurrence coping power] is the power of the workplace to prevent defective products by shortening the defect coping time as much as possible when a defective product occurs. It is possible to express it by the degree of defect handling time until the time. [Defect detection power] is the power of the workplace that can detect (extract) defective products so that defective products are not sent from the manufacturing workplace to the workplace in the subsequent process. In FIG. 1, the degree of defect extraction = (ab ′) / (ab ).

  Therefore, calculate [defect generation suppression force] × [defect generation response force] × [defect detection capability] for the standard manufacturing operation (in the case of assembly operation, the standard assembly operation is, for example, the simplest downward movement operation). By doing so, it becomes possible to evaluate the ability of the manufacturing workplace.

  By the way, the manufacturing workplace includes a worker who performs manufacturing work, a supervisor who supervises (manages) the worker, manufacturing equipment such as tools and jigs used in the manufacturing work, manufacturing line equipment, and workers. Formed in the workplace environment such as temperature, humidity, brightness, noise. Therefore, the causes of defects in manufacturing workplaces (defect occurrence categories) are related to manufacturing workers, related to manufacturing equipment, related to manufacturing conditions such as manufacturing line speed and the number of production lots / unit time, and related to the manufacturing physical environment. It is clear from our research that it is classified into things related to manufacturing workplace management.

  However, the cause of defects in these manufacturing workplaces and [defect creation degree as defect generation suppression ability], [defect handling time degree as defect coping ability], and defect extraction degree as defect detection ability I cannot take action. Therefore, poor workplace conditions that can correspond to [defect creation degree as defect generation suppression ability], [defect handling time degree as defect handling ability], and defect extraction degree as defect detection ability It is necessary to subdivide into impact items.

  The subordinately categorized work condition impact items (evaluation factors) for manufacturing workers include the attendance rate of manufacturing workers, the nature and capabilities of manufacturing workers, the level of work skills of manufacturing workers, and work instructions to manufacturing workers. There is a system.

  The sub-category workplace condition impact items (evaluation factors) related to manufacturing equipment include equipment performance and reliability, a management system including maintenance of equipment, and the degree of determination of persons in charge of equipment.

  As the work condition defect influence items (evaluation factors) subdivided into the production conditions, there are production forms such as production line speed, number of production lots / unit time, and the like.

  The sub-category workplace condition influence items related to the manufacturing physical environment include physical environments such as temperature, humidity, brightness, and noise.

  The sub-category workplace condition defect impact items (evaluation factors) related to manufacturing workplace management include education / training for workers, work instructions / distribution to workers, countermeasures when defects occur, work check methods, etc. .

  As described above, by setting the work condition defect influence items (evaluation factors) subdivided, as shown by arrows in FIG. 2, [Defect creation degree], [Defect handling time degree], [Defect] It is possible to obtain a correlation with the degree of extraction], and for each of these items, a reference level (for example, in the case of assembly work, the reference assembly work is the simplest downward movement work) for each item in advance. The workplace evaluation database 4a1 (shown in FIG. 3) is composed of a defect creation coefficient, a defect extraction coefficient, a defect handling time coefficient, and an inter-item relative weight coefficient, which are defect occurrence coefficients at the highest level at which no defect occurs. ) Is prepared, it is possible to estimate the ease of occurrence of defects in the manufacturing workplace (defect occurrence degree), that is, the ability of the manufacturing workplace. That is, by setting the subordinately classified work condition defect influence items, for each of these items, the correlation with [Defect creation degree], [Defect handling time degree], and [Defect extraction degree] with respect to the standard manufacturing work is obtained. It is possible to determine a defect creation coefficient, a defect extraction coefficient, a defect handling time coefficient, and a relative weight coefficient between items, which are defect occurrence coefficients at the reference level shown. Note that the relative weight coefficient between items can be included in the defect occurrence coefficient.

  In addition, as a method for creating a database for workplace evaluation, the degree of defect creation, the degree of defect extraction, and the defect, which are the degree of occurrence of defects relative to the standard manufacturing work according to the level of each item affected by the bad condition of workplace conditions measured in the representative manufacturing workplace Based on the degree of coping time, it is only necessary to calculate the defect creation coefficient, defect extraction degree coefficient, defect coping time coefficient, and inter-item relative weight coefficient at the standard level for each item affected by defect in workplace conditions. . Naturally, the ease of occurrence of defects in the representative manufacturing workplace (defect occurrence rate), that is, the total defect occurrence rate indicating ability and the workplace defect rate are also measured.

  In this way, by creating and preparing a database for workplace evaluation based on actual measured values at representative manufacturing workplaces, it is possible to simply enter the level for each item affected by poor work condition conditions at various manufacturing workplaces. It is possible to estimate and evaluate the ability (total defect occurrence rate and workplace defect rate). This is also because the inventors have confirmed that the measured values and the estimated values almost coincide with each other in a plurality of manufacturing workplaces other than the representative manufacturing workplace.

  As explained above, it is possible to evaluate and estimate the ability (total defect occurrence rate and workplace defect rate) in a manufacturing workplace simply by entering the level for each item affected by poor workplace conditions in various manufacturing workplaces. By registering the estimated capability of the manufacturing workplace as a workplace constant in the product structure evaluation database (work target evaluation database) 4b1 shown in FIG. 3 (an index indicating the reliability of the manufacturing operation reliability in each manufacturing workplace) As described in Japanese Patent Application Laid-Open No. 10-334151, it is possible to estimate a defective rate of a product manufactured (for example, assembled) in the manufacturing workplace.

  Next, an embodiment of a method for evaluating and estimating the ability (total defect occurrence rate and workplace defect rate) in the manufacturing workplace simply by inputting the level for each item affected by the poor working condition in various manufacturing workplaces according to the present invention. explain.

  FIG. 3 shows the workplace evaluation unit 10a that evaluates and estimates the ability (total defect occurrence rate and workplace failure rate) in the manufacturing workplace according to the present invention, and the failure rate when manufacturing a product or a part of the product in the manufacturing workplace. It is a block diagram which shows one Example of the product evaluation part (manufacturing object evaluation part) 10b to estimate. In the following, a case where a product is manufactured by assembling as a manufacture will be described. The product evaluation unit 10b that estimates the defective rate when manufacturing a product or a part of the product in a manufacturing workplace is described in Japanese Patent Application Laid-Open No. 10-334151, and will be briefly described.

  The evaluation apparatus 10 according to the present invention includes a calculation unit 3 including a CPU 32 connected to a bus 35, a ROM 31 that stores predetermined programs, a RAM 33 that temporarily stores various data, and the like, and an interface 34 to the calculation unit 3. It is comprised from the input means 1, the display means 2, the memory | storage device 4, and the output means 5 which were connected through this. The input means 1 includes assembling operation information 1b1 at the time of assembling the parts, property information 1b2 of the assembling parts and the assembled parts, information 1b3 of presence / absence of the checking process, information 1b4 of the manufacturing workplace name, and manufacturing workplace It is composed of a keyboard, a mouse, a recording medium, a network, etc. so that manufacturing workplace condition information 1a and the like can be input. The display means 2 displays an input screen for inputting various types of information with the input means 1, displays the evaluation results (diagnosis results and improvement advice, etc.) of the manufacturing workplace, and the product structure (manufacturing work target) Evaluation results (product defect rate, defect phenomenon, manufacturing cost, etc.) can be displayed. The storage device 4 includes a manufacturing workplace evaluation storage section 4a storing a manufacturing workplace evaluation database 4a1, a manufacturing workplace evaluation calculation program 4a2, and a manufacturing workplace evaluation input / output control program 4a3, a product structure evaluation database 4b1, and a product. The structure evaluation calculation program 4b2 and the product structure evaluation storage section 4b that stores the product structure evaluation input / output control program 4b3. The output unit 5 includes a recording medium, a network, or the like provided separately from the display unit 2 so that the evaluation result of the manufacturing workplace and the evaluation result of the product structure can be output. Furthermore, the evaluation apparatus 10 is configured to connect the design system 20 via a network or a recording medium, and to input design data of a product to be manufactured.

  As described above, the evaluation apparatus 10 according to the present invention is functionally large and manufactured at the workplace evaluation unit 10a (shown in FIG. 5) that evaluates the degree of occurrence of defects in the manufacturing workplace. The product evaluation unit 10b (shown in FIG. 13) that evaluates the degree of occurrence of defects of the product to be manufactured. By the way, although workplace evaluation can be performed only by the workplace evaluation unit 10a, functionally connected to the product evaluation unit 10b, the evaluation information on the degree of occurrence of defects in the manufacturing workplace by the workplace evaluation unit 10a, and the product evaluation unit 10b It is possible to estimate a specific defect rate when a certain product is manufactured in a certain manufacturing workplace by using the defect occurrence evaluation of the product.

  The evaluation apparatus 10 according to the present invention selects a large number of condition items (work condition adverse effect items) that particularly affect the degree of occurrence of defects from the various manufacturing workplace conditions described above, and the selected adverse effects on the workplace conditions. For each item, a value indicating the degree of the degree of defect occurrence ("defect creation coefficient", "defect “Defect occurrence coefficient” and “Relative weight coefficient between items”) are defined and stored as the workplace evaluation database 4a1, and the calculation means 3 Based on the information on the level of the workplace to be evaluated for each of a number of work condition bad influence items among the various work conditions input by the input means 1, each work condition bad influence item The “defect occurrence coefficient” and “inter-item relative weight coefficient” set with respect to the reference level of the workplace are read out, and the read “defect occurrence coefficient” and “inter-item relative weight coefficient” Thus, the degree of failure occurrence and the failure rate of the evaluation target manufacturing workplace over a large number of workplace condition failure influence items are calculated and registered in the workplace constant area of the database 4b1 for product structure evaluation in the storage device 4.

  As shown in FIG. 4, the workplace evaluation database 4a1 of the workplace evaluation storage portion 4a of the storage device 4 is preliminarily described with information 60 on the cause of failure (defect occurrence category) in the manufacturing workplace and the cause of failure. Information describing the contents of the classified work condition bad influence items (including information 63 describing work conditions for each of a plurality of work levels) 62, 63, and each work condition bad influence item are set “Defect occurrence coefficient” 65 and “Relative between items” which are composed of “Defect creation coefficient” 65 a, “Defect extraction degree coefficient” 65 b, “Defect handling time coefficient” 65 c at the reference level (for example, level 1) for the reference manufacturing operation When the degree of failure occurrence is extremely bad for each of the information on the "automatic weighting factor" 64 and at least for each cause of failure in the manufacturing workplace, or for each subordinate item of the workplace condition failure Improved one configured (measures) advice or the like from the information 66 is prepared.

  In the example shown in FIG. 4, the work condition defect effect item (No. 1) subdivided regarding the evaluation category 1 (manufacturing worker) indicates the attendance rate of the manufacturing worker and relates to the evaluation category 2 (manufacturing equipment). The sub-category workplace condition defect influence item (No. 8) indicates the determination of the person in charge of the equipment, and the sub-category workplace condition defect influence item (No. 13) related to evaluation category 4 (manufacturing physical environment) Indicates brightness (illuminance).

  Then, a plurality of levels (workplace levels) 63 are set for each workplace condition defect influence item 62 with reference to a reference level (for example, level 1 (high level)). For example, with respect to the item “attendance rate” affected by poor workplace conditions, level 1 (high level) is “attendance rate 97% or more”, level 2 (medium) is “attendance rate 90% to less than 97%”, level Three workplace levels are set such that 3 (low) is “less than 90% attendance rate”. For example, for the item “worker in charge of equipment” affected by poor workplace conditions, level 1 is “complete”, level 2 is “fixed (90% or more of all facilities)”, and level 3 is “fixed” Three workplace standards are set, such as “Yes (all facilities less than 90%)”. For example, level 1 is “L ≧ 1000 lx”, level 2 is “1000 lx> L ≧ 600 lx”, and level 3 is “600 lx> L” for the work condition bad influence item “illuminance (L)”. There are two workplace standards. In this way, in the example of FIG. 4, the workplace level 63 for each work condition defect effect item is set in three stages of levels 1 to 3, and level 1 corresponds to the workplace state in which defects are most unlikely to occur. Corresponds to a workplace situation in which defects are most likely to occur, and level 2 corresponds to a workplace state having an intermediate level of failure between level 1 and level 3. The workplace level to be set is at least two levels for indicating whether or not the workplace state is applicable, but there is no particular upper limit. If the number of levels increases, there is an advantage that the evaluation accuracy can be improved, but conversely, the choices at the time of input increase and the labor of input increases.

  Furthermore, a reference workplace level level (for example, level 1) for a reference manufacturing operation set for each work condition defect effect item 62 (in the case of assembly operation, the reference assembly operation is the simplest downward movement operation, for example). The “defect occurrence coefficient” 65 and the “relative weight coefficient between items” 64, which are composed of “defect creation coefficient” 65 a, “defect extraction degree coefficient” 65 b and “defect handling time coefficient” 65 c in FIG. For example, for the work condition defect effect item “attendance rate”, “defect creation coefficient” is “3”, “defect extraction degree coefficient” is “1”, and “defect handling time coefficient” is “2”. “Defect occurrence degree coefficient” 65 and “Relative weight coefficient between items” are set as “2”. For the work condition defect effect item “person in charge of equipment”, “defect creation coefficient” is “2”, “defect extraction coefficient” is “2”, and “defect handling time coefficient” is “1”. “Defect occurrence degree coefficient” 65 and “Relative weight coefficient between items” are set as “1”. For the work condition defect effect item “illuminance (L)”, “defect creation coefficient” is “2”, “defect extraction degree coefficient” is “2”, and “defect handling time coefficient” is “0”. “Defect occurrence degree coefficient” 65 and “Relative weight coefficient between items” are set as “1”. Here, the relative weight coefficient between items being “2” means that the “defect occurrence degree” is twice that of the other items. If the defect creation coefficient, defect extraction degree coefficient, and defect handling time coefficient are “2” or “3”, it means that they are twice or three times as compared with “1”. In addition, the fact that the defect creation coefficient, defect extraction degree coefficient, and defect handling time coefficient are “0” means that they are irrelevant. These coefficients are all shown as integers in FIG. 4 for easy understanding, but need not be shown as integers.

  Further, improvement (measures) advice 66 and comment 67 when the defect occurrence level is extremely bad for each factor causing the defect in the manufacturing workplace (evaluation category), or for each of the subordinately classified work condition defect influence items, is a bad defect occurrence level. Short-term measures and long-term measures are set according to Levels 2 and 3.

  Next, a processing flow for evaluating and estimating the ability (total defect occurrence rate and workplace defect rate) in the manufacturing workplace using the workplace evaluation unit 10a will be described with reference to FIG. First, manufacturing workplace condition information 1a in the manufacturing workplace to be evaluated is input using the input means 1 (steps 100a to 100h). Specifically, the level of the corresponding workplace is selected and entered for each work condition adverse effect item.

  That is, when this evaluation apparatus is activated by an evaluator (for example, a supervisor at a manufacturing workplace) or the like, first, the calculation means 3 performs “new input” 51a or “open registered file” 51b as shown in FIG. A screen 51 for selecting either of them is displayed on the display means 2 (step S100a). When the registered file is opened, it is used when a new manufacturing workplace is evaluated with reference to the once evaluated manufacturing workplace or when the evaluated manufacturing workplace is re-evaluated.

  When the evaluator selects “new input” 51a (step S100b), the calculation means 3 recognizes the information and displays the input screen 70 shown in FIG. 8 on the display means 2 (step S100c). When the evaluator selects “open existing file” 51b (step S100b), the file designation screen 52 shown in FIG. 7B is displayed (step S100f), and file designation is performed by designating the file name. The information is accepted (step S100g), and the corresponding file (reflecting the input information on the input screen 70) is opened (step S100h).

  The question item 75 and the answer option 76 which are input information are information 62 on each workplace condition defect influence item for each evaluation category and each workplace condition defect influence item stored in the workplace evaluation storage portion 4a of the storage device 4. On the other hand, the information 63 of the workplace level items stored by dividing into levels is read out and displayed on the input screen 70. By doing so, the items to be evaluated and the setting of the workplace level need only be changed by changing the information stored in the storage device 4, so that the evaluation device can be easily maintained and improved.

  Next, when the evaluator uses the input screen 70 and the input unit 1 to input information on the evaluation target workplace, the calculation unit 3 receives the information and temporarily stores it in the RAM 33, for example (step S100e). First, an evaluation target workplace name “manufacturing workplace X” to be evaluated is input. Then, referring to the document defining the question item obtained as a separate window by pressing the supplementary explanation button 75a for each question item (workplace condition defect influence item 62) 75 for each evaluation category, It is input by designating a level or 76a-76c. In other words, for each question item, that is, for each work condition adverse effect item, three answer options (that is, three levels of work conditions) are displayed in advance, and the radio button corresponding to the work condition of the work place to be evaluated is clicked with the mouse It becomes possible to input just by doing. In this way, for the input, in addition to the input from the input means 1 using a keyboard, a mouse, or the like, workplace information stored in another storage device may be taken in via a computer network. In addition, it is also possible to input into the calculation means 3 via storage media, such as a flexible disk. If necessary, it may be configured so that information necessary for evaluation can be retrieved and read.

  After the evaluator's input is completed, the CPU 32 which is the calculation means 3 recognizes that the evaluation calculation execution instruction has been given, and based on the work condition information input and stored in the RAM 33 or the like, From the “defect creation coefficient” 65 a, “defect extraction degree coefficient” 65 b, and “defect handling time coefficient” 65 c at the standard workplace level level for the standard manufacturing work set for each work condition defect influence item from the storage portion 4 a The information of “defect occurrence coefficient” 65 and “inter-item relative weight coefficient” 64 is read out and temporarily stored in the RAM 33 (step S110).

  The determination unit 51 in the workplace evaluation unit (calculation unit 3 having the CPU 32) 10a shown in FIG. 5 determines the workplace level level information inputted for each workplace condition defect affecting item by the input unit 1, and the workplace condition defect affecting item The information on the number of defects and the information indicating the workplace level level are used as search keys, and the information on the “defect occurrence coefficient” 65 and the “inter-item relative weight coefficient” 64 corresponding to the storage portion 4a for workplace evaluation of the storage device 4 Is retrieved and temporarily stored in the RAM 33. This is repeated for all the adverse workplace condition items to be evaluated.

  This will be described using the input information shown in FIG. The impact of poor working conditions is “working level” of “2” for “attendance rate”, “3” of working level for “person in charge of equipment”, and “1” of working level of “illuminance”. Have been entered. From this input, the CPU 32 in the workplace evaluation unit 10a determines that the number indicating the workplace condition defect effect item is “level 2”, “level 3”, “1”, “8”, “13”. The number information “1”, “8”, “13” indicating these workplace condition defect effect items and the information “level 2”, “level 3” indicating the workplace level level are judged to be “level 1” levels. , Using the two pieces of information of “level 1” as search keys, a coefficient (defect occurrence coefficient) of the workplace level with respect to the corresponding reference workplace level, “defect creation coefficient” 65a, “defect extraction degree coefficient” 65b, A defect occurrence coefficient database in which information of “defect occurrence coefficient” 65 and “inter-item relative weight coefficient” 64 composed of “defect handling time coefficient” 65 c is stored in the workplace evaluation storage portion 4 a of the storage device 4. From And search, and stored in the RAM33. In the case of the database shown in FIG. 4, the workplace level level coefficient (defect occurrence coefficient) relative to the reference workplace level level corresponds to the workplace level level.

  Next, as shown in FIG. 9, the CPU 32 of the calculation means 3 (workplace condition item-specific influence calculation unit 52), as shown in FIG. Using the workplace evaluation calculation program stored in the workplace evaluation storage portion 4a, the coefficient of the workplace level with respect to the reference workplace level level for each of the adverse effects of the workplace condition in the manufacturing workplace X stored in the RAM 33 (defect occurrence coefficient) ) (Shown at the workplace level in FIG. 4), “Defect occurrence coefficient” 65 a, “Defect creation coefficient” 65 a, “Defect extraction degree coefficient” 65 b, “Defect handling time coefficient” 65 c Based on the information of the “relative weight coefficient between items” 64, the defect occurrence degree 92 (“defect creation degree” 92a (step S121b) for each work condition defect influence item, “Defect extraction degree” 92b (step S121c), “Defect handling time degree” 92c (step S121d), “Defect occurrence degree” 92d (total of these “defect preparation degree”, “defect extraction degree”, “defect handling time degree”) Step S121e), “room for improvement” 92e (step S121g)), “ideal workplace failure occurrence” 93a, and “worst workplace failure occurrence” 93b (step S121f) are calculated and stored in the temporary RAM 33. “Ideal workplace failure occurrence degree” 93a indicates, for example, the failure occurrence degree in the case of workplace level level 1 (the highest level). The “worst workplace defect occurrence degree” 93b indicates, for example, the defect occurrence degree when the workplace level is 3 (the lowest level). The “room for improvement” 92e is indicated by the difference between the “defect occurrence degree” 92d and the “ideal workplace defect occurrence degree” 93a. For example, when the evaluation category shown in FIG. 10 is “1”, the workplace level level is “2”, and the relative weight coefficient between items is “2”. Therefore, “Defect creation degree”, “Defect extraction degree” ”And“ Defect handling time degree ”are four times the defect creation coefficient“ 3 ”, defect extraction degree coefficient“ 1 ”, and defect handling time coefficient“ 2 ”. 24 ". Since “Ideal workplace defect occurrence degree” is “12”, “room for improvement” is “12”.

  Then, the CPU 32 of the calculation means 3 (the workplace constant calculation unit 53 shown in FIG. 5) calculates the total for all the workplace condition defect affecting items over all defect occurrence factors (evaluation categories) in step S121h, As shown in FIG. 10, the total 98a of “defect creation degree”, the total 98b of “defect extraction degree”, the total 98c of “defect handling time degree” and their total 98 are calculated and stored in the temporary RAM 33 (step S121i). Next, the CPU 32 of the calculation means 3 calculates a workplace defect rate 99 based on the total 98 of “defect creation degree”, “defect extraction degree”, and “defect handling time degree” stored in the RAM 33, The data is stored in the workplace constant portion of the work target evaluation database 4b1 in the work target evaluation storage portion 4b of the storage device 4 (step S121j). In this way, as shown in FIG. 10, the ability in the manufacturing workplace X to be evaluated (total defect occurrence rate 98 and workplace failure rate 99) is estimated and stored in the workplace constant portion of the work target evaluation database 4b1. Will be.

  By the way, each manufacturing workplace has a good manufacturing operation (for example, an assembling operation) and a poor manufacturing operation (for an assembling operation). In order to reflect this in the workplace evaluation, it can be solved by storing and setting a plurality of workplace constants in the workplace constant portion of the work target evaluation database 4b1 instead of a single workplace constant. For example, workplace constants may be set for each of the manufacturing operations classified into several manufacturing operations (in the case of assembly operations, such as “press-fit”, “soldering”, and “screw tightening”). In this case, it is necessary for the workplace evaluation unit 10a to evaluate the easiness of occurrence of a workplace failure based on the corresponding work condition failure effect item for each manufacturing operation. Then, as manufacturing workplace condition information 1a which is input information for manufacturing workplace evaluation, it is necessary to input information on a manufacturing operation type for subdividing the workplace. Needless to say, when evaluating a product, it is necessary to add the subdivided manufacturing workplace name information or the manufacturing operation type as the manufacturing workplace name information 1b4 which is input information.

  Next, in step S130, as an evaluation calculation result evaluated for the manufacturing workplace X, in addition to the workplace defect rate 99, workplace diagnosis result 1 (score 86 by evaluation category and comment 87 by evaluation category), workplace diagnosis result 2 (improvement) 11 is displayed on the display means 2 as shown on the screen 80 in FIG. 11, for example, the CPU 32 of the calculation means 3 (the category-specific influence calculation section 54a in the message control section 54 shown in FIG. 5). ), In step S122, for each evaluation category, the sum of the “defect creation degree”, the “defect extraction degree”, the “defect handling time degree”, and the total (defect occurrence degree) are calculated. A score (failure rate) corresponding to the calculated failure occurrence degree is obtained and stored in, for example, the storage device 4. Next, in step S123, the CPU 32 determines, for each evaluation category, the workplace condition bad influence item having the largest value from the “room for improvement” 92e of the work condition bad influence item belonging to the category. A comment corresponding to the defect-affected item is retrieved from the workplace evaluation table 4a1 and stored in the storage device 4, for example. Further, in step S124, the CPU 32 selects a plurality of workplace condition failure effect items in descending order of the “room for improvement” 92e value of the workplace condition failure impact item, and selects the selected workplace condition failure impact item and the selected item. On the other hand, the improvement point advice is retrieved from the workplace evaluation table 4a1 based on the workplace level level input, and stored in the storage device 4, for example. Accordingly, in step S130, the evaluation calculation result evaluated for the manufacturing workplace X is output, which can be improved. In particular, if the score for each evaluation category is displayed as a pie chart or a line graph, it is possible to grasp at a glance what is affected.

  As described above, necessary data among the evaluation estimation results for the manufacturing workplace X is stored and stored in the storage device 4.

  FIG. 11 shows an example of an output screen of the workplace evaluation unit 10a according to the present invention. In the output example of FIG. 11, three types of evaluation results are output: (1) “workplace standard failure rate estimated value” as the degree of failure occurrence, (2) workplace diagnosis result 1, and (3) workplace diagnosis result 2.

  The “workplace standard defect rate estimated value” shown in (1) above is an average estimated defect rate value when the standard production work is performed in the production workplace X to be evaluated. Can be compared.

  In addition, two types of workplace diagnosis results are output. First, the workplace diagnosis result 1 is a workplace-level evaluation point for each evaluation category. This evaluation score is, for example, where the ideal manufacturing workplace is 100 points and the lowest manufacturing workplace is 0 points, and the total value of each coefficient value for each evaluation category in the evaluation target manufacturing workplace X is located between them. It shows what to do. The workplace diagnosis result 2 is the content of advice on workplace improvement points. This presents the work condition bad impact items that have a lot of room for improvement, that is, if they improve, the effect of reducing the incidence of defects, as “workplace improvement points”. It is presented separately as a countermeasure plan that should be tackled. This means that the manufacturing workplace evaluation database 4a1 of the storage device 4 stores a countermeasure plan that can be achieved in the short term and a countermeasure plan that should be addressed in the long term separately for each item affected by poor workplace conditions, and reads them out. Can be realized. In addition, if necessary, for a combination of workplace levels of multiple items that have adverse effects on work conditions, it is possible to store a countermeasure plan that can be achieved in the short term and a countermeasure plan that should be addressed in the long term separately. .

  In addition, it is preferable to display in the order of the large extent of room for improvement, that is, the effect of reducing the occurrence rate of defects when it is improved, in order to achieve efficient and targeted countermeasures. This can be done by calculating room for improvement and rearranging in order of output. Specifically, for each workplace condition defect impact item, calculate the difference between the defect occurrence coefficient of the manufacturing workplace subject to evaluation and the defect occurrence coefficient of the ideal manufacturing workplace (that is, the workplace whose level is level 1) By comparing the sizes, output can be performed in descending order.

  Through the above processing, it is possible to provide a highly reliable manufacturing workplace evaluation result with a simple input. Moreover, since the manufacturing workplace condition items (namely, workplace improvement points) are output in the descending order of the room for improvement as an evaluation result, the manufacturing workplace improvement can be started immediately.

  In this way, if the workplace evaluation unit 10a is used, it is possible to quantitatively grasp the probability (ability) of causing a defect in the manufacturing workplace without actually making a product in the manufacturing workplace to be evaluated.

  In addition, because the manufacturing department can quantitatively grasp the workplace condition items that have a large impact on the occurrence of defects in the manufacturing workplace and how much the occurrence of defects can be reduced by improving or taking countermeasures, It helps to formulate a manufacturing workplace improvement plan to efficiently improve workplace standards, and has the effect of preventing defects. In addition, if evaluation is performed before production, the priority management points in the manufacturing workplace will be clarified in advance, so that it is possible to accurately arrange inspection processes and select inspection methods, which has a great effect on defect extraction. In addition, the design / development department can estimate in advance the degree of defect occurrence at the manufacturing workplace where the product is to be manufactured, so that product development / design according to the manufacturing workplace can be performed efficiently. .

  Next, in the product evaluation section 10b, as described above, the product is manufactured in the manufacturing workplace based on the defective rate 99 of the manufacturing workplace where the product stored in the workplace constant portion of the work target evaluation database 4b1 is to be manufactured. An embodiment for estimating the defect rate of a product to be assembled (for example, assembly) will be briefly described.

  Hereinafter, an assembly operation among the operations for manufacturing the product will be described.

  The design system 20 provides information such as a part name, a part number, a material, a weight, and a unit price of parts for constituting a product.

  A product defect occurrence model according to the present invention is shown in FIG. One assembly work sequence is a “positioning operation” and then a “joining operation”. That is, one assembling operation is composed of a “positioning operation” and a subsequent “joining operation”. The occurrence of a defect is considered to be a case where the [variation of operation] exceeds the [variable tolerance] and the defect cannot be found. [Operational variation] includes “positioning variation” that occurs during “positioning operation” determined by the assembling operation and the properties of parts, “force variation” that occurs during “joining operation”, and the like. [Allowable variation range] is determined by “dimensional accuracy, ease of damage, etc.” determined during “positioning operation” and “required operating force” determined during “joining operation”, etc. Thus, the occurrence of a defect due to “positioning” includes damage deformation and the like, and the occurrence of a defect due to “force” includes incomplete insertion.

  As described above, one assembly work operation is basically considered to be a repetition of “positioning operation” and “subsequent coupling operation”. Among the standard assembly operations, there are only operations for "positioning operations" such as operations for holding parts and operations for shaping electric wires, but many assembly operations are performed after "positioning". Operation ".

  As described above, the standard assembling operation is composed of “positioning operation” and “combining operation”, and there is a large work defect, and it is divided into two types, one occurring during the positioning operation and one occurring during the combining operation.

  First, a defect that occurs during the positioning operation is a defect that occurs due to variations (inaccuracy) in component positions and component orientations during the positioning operation. If you move to this joining operation with insufficient positioning, a failure that prevents this joining operation (incomplete work) will occur. However, the strength of the assembly part and the joint part of the assembled product and the operation of this operation will occur. Depending on the force, the joint may be damaged or deformed. Normally, an operator confirms that positioning is sufficient before proceeding to the coupling operation.If positioning is insufficient, the positioning operation is corrected before proceeding to this coupling operation, and then the coupling operation is performed. Transition. The above-described defects are particularly likely to occur when it is difficult to confirm the positioning, for example, it is difficult to see the work site, or when you forget to confirm the positioning.

  In addition, there are assembly failures caused by the coupling operation, which are caused by poor control of the locus of the coupling operation, that is, due to variations in the operation locus, and those caused by insufficient coupling operation force. The assembly failure caused by the poor control of the locus of the coupling operation is frequently generated particularly during the long section operation. On the other hand, defective assembly caused by insufficient combined operating force is a case where the operating force required for assembly cannot be exerted, especially when the required operating force such as press-fitting operation is large, or depending on the operation and the property conditions of parts, etc. The frequency of occurrence is high when the operating force cannot be demonstrated.

  Therefore, the assembly work of the product and assembly to be evaluated is composed of a plurality of assembly operations for sequentially assembling a plurality of parts, so that a plurality of preset standard assembly operations (downward movement, lateral movement, inversion) , Press fitting, soldering, screw tightening, shaping, etc.). Then, the ease of assembly failure (failure rate) in the assembly work of the products and parts to be evaluated can be calculated by integrating the failure rate coefficients of the respective standard assembly operations. Then, the defect rate coefficient of each standard assembly operation is calculated based on the number of assembly operations up to completion of an arbitrary assembly operation, the property conditions of the assembly parts / parts to be assembled (for example, functions (design products, etc. Part type), size, weight, shape (microparts for assembly part condition, multiple assembly postures (multi-point simultaneous alignment), microhole / small hole, assembly completion judgment, position for assembly part condition The number of alignment points, the space around the assembly, no positioning guide, assembly to moving parts, etc.), dimensional accuracy, surface accuracy, material (with non-contactable surface, special material), function), etc.), assembly workplace Estimating the ease of assembly failure (defective rate) in the assembly work of products and parts to be evaluated by correcting the conditions (workplace constants) and the presence or absence of a check process to confirm assembly completion as correction factors Accuracy can be improved.

  In other words, the evaluation target is expressed as a combination of standard assembly operations, and the defect rate coefficient of each standard assembly operation is calculated based on the number of assembly operations, the property conditions of the assembled / attached components, and the assembly workplace conditions. Then, the defect rate is calculated by combining the values corrected according to the presence or absence of the process for confirming the completion of assembly.

In this way, a check process for confirming the assembly failure rate of the part assembly work, the contents of the operation of the assembly work, the properties of the parts to be assembled and the parts to be assembled, and whether the work has been properly completed. The reasons for determining the presence and absence and the conditions of the workplace where the assembly work is carried out are as follows.

  Of course, if there is an assembly operation, there is a potential (assembly failure rate coefficient) at which an assembly failure may occur.

  Factors that increase or decrease the assembly failure rate coefficient of the assembling operation include the nature of the assembling parts and the assembling parts and the conditions of the workplace where the assembling work is performed.

  Regarding the nature of the assembled part and the assembled part, for example, if the shape of the part to be assembled or the part to be assembled is difficult to assemble, the assembly failure rate coefficient of the assembling operation is amplified. The reason why the component type (function) can be input as the properties of the assembled part and the assembled part is as follows.

  That is, there are two types of assembly failures: incomplete assembly and component damage / dirt. “Incomplete assembly” is mainly caused by fluctuations in human work movements (variations in operation accuracy) or mistakes. This type of failure is often caused by “incomplete insertion (completely into the back) in the case of connector insertion work. Not inserted) ”and“ connector is inserted in the opposite direction ”. On the other hand, “part damage / dirt” mainly occurs as a result of the above-mentioned blurring of human work movement (variation in motion accuracy) and mistakes. It depends on the type of parts even with the same damage and contamination. For example, a design part exposed to the appearance is a part type that can be defective even with a slight scratch or dirt, unlike other parts inside a product. In other words, depending on the type of component, that is, the function of the component, even if the same external force (stress) is applied to the component, it is not uniform whether it becomes defective. Therefore, for each part type, a coefficient value indicating the strength (drag) of the part type against the external force is stored in the database, and it is possible to input the part type of the assembled part and the assembled part, and against the external force of the evaluation target part. The estimated failure rate was calculated by comparing the strength (drag) and the magnitude of external force (stress) acting on the component during the assembly operation of the component, and taking into account the probability of “component damage / dirt” failure. As described above, the defect rate is estimated in consideration of not only “incomplete assembly” defects but also “part damage / dirt” defects.

  Similarly, the assembly failure rate coefficient of the assembly operation is also affected by the conditions of the workplace where the assembly work is performed. For example, if the equipment used for work is prone to failure, the failure rate coefficient of the assembly operation will be high even in the same assembly operation, and if the overall technical level of workers in the workplace is high, the same assembly Even in the attaching operation, the defective rate coefficient of the assembling operation is low.

  In addition, as a defect detection potential, if there is a check process to check whether or not the assembly work is properly completed after the assembly work process of the assembly defect rate estimation target, a defect has occurred. Even so, the probability of a final failure is reduced by the fact that it is discovered in the process and the rework measures are taken.

  For this reason, in the product evaluation unit 10b, the contents of the operation of the assembling work, the nature of the assembling parts and the assembling parts, and whether the work is properly completed, which greatly affects the assembly failure. The defect rate was calculated based on the presence or absence of a check process to confirm whether or not and the conditions of the workplace where the assembly work was performed.

  For this reason, in the product structure evaluation storage portion 4b of the storage device 4, the defect rate coefficient for each standard assembly operation corresponding to the type of standard assembly operation (part assembly operation), and any assembly work is completed. Motion order correction coefficient corresponding to the number of assembly operations (referred to as assembly number), assembly part conditions and assembly condition correction factors corresponding to the properties of the assembly parts and assembly parts, assembly Product structure evaluation database 4b1 which stores a check process correction coefficient in the case where a process for confirming the completion of assembly is provided in the post-process of work, and a workplace constant corresponding to the conditions of the assembly workplace where the assembly work is performed, and this product evaluation There are a part 4b2 storing a product structure evaluation calculation program including an arithmetic expression for calculating the defective rate of the part, a part 4b3 storing a product structure evaluation input / output control program, and the like. The coefficients stored in the product structure evaluation database 4b1 are set on the basis of actual assembly failure occurrence data so that the items that are likely to cause defects are larger or smaller.

  Therefore, first, an input screen is displayed on the display means 2, and using the input means 1, assembly operation information (type of operation, order of operations) 1b1 during assembly of the parts shown in FIG. Assembling information on properties of parts and assembled parts (information on factors affecting the uncertainty of assembly operation) 1b2, information 1b3 on presence / absence of check process, and information 1b4 on the name of the manufacturing workplace where the assembly work is performed An input corresponding to the operation is performed and temporarily stored in the RAM 33.

  Next, the CPU 32 (extracting units 131a to 131c in the discriminating unit 131 shown in FIG. 13) stores the defect rate coefficient corresponding to each input standard assembly operation element by the standard assembly operation failure rate stored in the database 4b1. The correction coefficients corresponding to the number of assembling (order of standard assembling operations) extracted from the coefficients and inputted in the standard assembling operations are extracted from the operation order correction coefficients stored in the database 4b1, and each inputted The correction coefficient corresponding to the property information of the assembled part and the assembled product in the standard assembling operation is extracted from the assembled part condition and the assembled product condition correction coefficient stored in the database 4b1, and each inputted A correction coefficient corresponding to the presence or absence of the check process in the standard assembly operation is extracted from the check process correction coefficient stored in the database 4b1 and input. Extracted from a series of work constants a correction coefficient stored in the database 4b1 corresponding to the manufacturing work name standard assembly operation is performed, is stored in a temporary RAM 33.

  Next, the CPU 32 (product structure defect rate calculation unit 132 shown in FIG. 13) calculates the product assembly failure rate estimated value according to the following equation (Equation 1) according to the product structure evaluation calculation program stored in the part 4b2. Based on the assembly failure estimation formula (Equation 2) based on the relationship, the failure rate estimation value for each part assembly operation is integrated.

Product assembly failure rate estimate = Σf1 (Assembly operation details, number of assemblies, parts properties, workplace conditions, presence of check process) (Equation 1)
= Σf2 (Defect rate coefficient by assembly operation, operation order correction coefficient, parts correction coefficient, workplace correction coefficient, check process correction coefficient) (Equation 2)
The above f1 () and f2 () represent functions. These functions include, for example, a method for multiplying the defect rate coefficient for each assembly operation by an operation order correction factor, a component correction factor, a workplace correction factor, a check process correction factor, a method for adding and subtracting, or an exponential correction. There are various methods such as adding.

  In addition, regarding a correction method when there are a plurality of operation order correction coefficients, component correction coefficients, workplace correction coefficients, and check process correction coefficients for one assembling operation, the defect rate for each assembling operation of the assembling operation The coefficient is multiplied by all motion order correction coefficients, parts correction coefficients, workplace correction coefficients, and check process correction coefficients. There is a method of adding (including subtracting) a correction coefficient, a workplace correction coefficient, and a check process correction coefficient.

  In the present invention, any method may be selected as long as the defective rate coefficient for each assembling operation is corrected by an operation order correction coefficient, a component correction coefficient, a workplace correction coefficient, and a check process correction coefficient.

  The operation order correction coefficient in the database 4b1 increases the complexity of the operation as the number of operations increases in the case of an assembly operation expressed by a plurality of standard assembly operation elements. This is a correction coefficient for increasing the “defect rate basic coefficient by assembling operation” of each operation in accordance with the order of the assembling operations.

  Furthermore, since the easiness of work failure of each assembling operation is affected by the conditions of the assembling parts and the assembling parts and their peripheral parts, a part condition correction coefficient is provided. That is, the ease of occurrence of work defects in each assembling operation varies depending on the conditions of the properties of the assembling parts, such as the size, weight, material, and number of parts to be assembled. Similarly, it varies depending on the property condition of the assembled product. From the above, the assembly part condition correction coefficient database and the assembly part condition correction coefficient database in the database 4b1 have an important effect on the ease of occurrence of work defects in the assembly operation and the assembly part property factor and the coverage. It is a component condition correction coefficient for setting an assembly product property factor and correcting the defect rate coefficient for each standard assembly operation for each factor. The assembled parts condition correction coefficient database and the assembled parts condition correction coefficient database may have different database structures.

  Furthermore, since the ease of occurrence of work defects in each assembling operation varies greatly depending on the conditions of the manufacturing workplace where the assembling work is performed, a standard assembly work such as “downward movement” estimated by the workplace evaluation unit 10a ( Stored in the database 4b1 is a workplace constant indicating an average probability of occurrence of defects in the manufacturing workplace relative to the (standard manufacturing work) (an index of the capability of the manufacturing workplace). The workplace constant indicates the average likelihood of occurrence of defects in the manufacturing workplace (an index of the ability of the manufacturing workplace), but it is not necessarily a single workplace constant, and a series of assembly operations May be classified into a plurality of assembly operations, and the workplace constant may be evaluated by the workplace evaluation unit 10a for each of the classified assembly operations and stored in the database 4b1. That is, a plurality of workplace constants may be evaluated by the workplace evaluation unit 10a and stored in the database 4b1. By doing so, it is possible to change the workplace correction coefficient, which is a workplace constant, for each of a plurality of assembling operations in the equation (2).

  Furthermore, if there is a process for checking whether or not the part assembly work is properly performed after performing the part assembly work for which the assembly failure rate is to be estimated, the check rate correction coefficient decreases for that reason. Therefore, it is a correction coefficient for reflecting the effect. If the defect extraction rate differs depending on the type of check work, a check process correction coefficient may be set for each different check process. Note that the information 1b3 regarding the presence / absence of the check process is not always necessary, and a desired defect rate can be calculated without the information.

  When evaluating and estimating the defect rate of the product described above, it is assumed that there is no defect in the assembled part itself, but actually there is also a defect rate in the assembled part itself. Considering this, it is possible to calculate the defect rate of the true product. Since the defect rate of the assembled part itself is based on the management of the workplace where the assembled part is manufactured or the manufacturer, the correction coefficient is calculated based on the defective rate of the assembled part itself in the same way as the evaluation of the manufacturing workplace. Then, it can be stored in the product structure evaluation database 4b1 in correspondence with the assembly part name.

  Next, the defect phenomenon estimation unit (CPU 32) 133 provided in the product evaluation unit 10b will be described. Since the defect rate calculation unit 132 of the product structure calculates a corrected total defect rate coefficient or defect rate for each assembly operation, the defect phenomenon estimation unit 133 calculates the calculated total for each assembly operation. By selecting a plurality of failure rate coefficients or failure rate groups from the largest, for example, it is possible to select an assembling operation that is estimated to cause the failure phenomenon most. Then, the defect phenomenon estimation unit 133 further searches for a defect rate coefficient, an operation order correction coefficient, a component correction coefficient, and the like that determine the total defect rate coefficient for each assembly operation, thereby performing the “positioning operation”. It is possible to find out a defective phenomenon such as whether it is due to “coupling operation”.

  Next, a manufacturing cost calculation unit (CPU 32) 134 including an assembly cost calculation unit 134a, a loss cost calculation unit 134b due to assembly failure, and a total cost calculation unit 134c provided in the product evaluation unit 10b will be described. The unit price of the assembled part is input from the design system 20 and stored in the product structure evaluation database 4b1. Further, the product structure evaluation database 4b1 includes a work time required for each standard assembly operation, a work time correction coefficient corresponding to the assembly part condition, the assembled product condition, the operation order, and the correction coefficient for the presence or absence of the check process. The cost per unit work time according to the manufacturing workplace constant is stored. Therefore, the assembly cost calculation unit 134a calculates and estimates the assembly cost based on the work time for each standard assembly operation, the work time correction coefficient, and the cost per unit work time according to the manufacturing workplace constant. Can do. Further, since the defect rate of the entire product and the total defect rate for each standard assembly operation can be calculated, the loss cost calculation unit 134b due to assembly failure disassembles the defective assembly component into a non-defective assembly component. The work time for replacement and reassembly can be calculated and estimated based on the data used when calculating the assembly cost. At this time, it is necessary to consider the unit cost of non-defective assembly parts and the cost required to discard defective assembly parts. In addition, when a defective assembly part can be made a non-defective product, the cost required for the repair may be taken into account.

  As described above, the total cost calculation unit 134c totals the total assembly cost calculated by the assembly cost calculation unit 134a, the total loss cost calculated by the loss cost calculation unit 134b due to defective assembly, and the total unit price of the assembled parts. Thus, the manufacturing cost of the product can be calculated.

  As described above, the defect rate of the product or the assembly estimated by the defect rate calculation unit 132 of the product structure, the defect phenomenon estimated by the defect phenomenon estimation unit 133, the product estimated by the manufacturing cost calculation unit 134, or The manufacturing cost of the assembly can be output to the display means 2 or the output means 5 together with the name of the product or assembly.

  Note that the estimation of the defect rate of the product or the assembly has been described in the case of the assembly work, but can be similarly applied to the case of the machining work. In the case of machining operations, the standard assembling operation is subdivided according to standard machining operations, and the assembling parts and assembling conditions are replaced with the processing means and conditions (properties) of the assembling work. The order may be replaced with the machining operation order.

  By using the evaluation apparatus 10 described above for the factory inspection in the quality assurance department, it becomes possible to evaluate the manufacturing process of the factory, and to determine whether or not the factory satisfies the required quality, and to improve the quality. It can be used for the guidance of quality improvement.

  In addition, the evaluation apparatus 10 can accurately prevent and detect defects in the design, manufacturing, and quality assurance departments.

  From the above, by using the evaluation apparatus 10 according to the present invention in each process of product development / manufacturing, defects occurring in the manufacturing process and defects occurring in the market can be greatly reduced. As a result, the reliability of the shipped product can be greatly increased.

It is a figure for demonstrating "defect preparation degree", "defect handling time degree", and "defect extraction degree" which determine the easiness to raise the defect of the manufacturing workplace which concerns on this invention. “Defect occurrence suppression ability”, “Defect occurrence response ability”, and “Defect detection ability” that determine the ease of occurrence of defects in the manufacturing workplace according to the present invention are related to various evaluation factors (subjects of subordinately classified work condition defect influence items). It is a figure for demonstrating that it is. It is a hardware block diagram which shows one Example of the evaluation apparatus which consists of the workplace evaluation part which concerns on this invention, and a product evaluation part. It is a figure which shows the specific content of the database for manufacturing workplace evaluation memorize | stored in the memory | storage device shown in FIG. It is a functional block diagram which shows one Example of the function structure in the workplace evaluation part of the evaluation apparatus which concerns on this invention. It is a figure which shows the whole manufacturing workplace evaluation flow performed in the workplace evaluation part which concerns on this invention. It is a figure which shows the selection screen of a new input / opening of a registered file, and a file designation screen. It is a figure which shows one Example of the input screen for inputting the workplace level for every evaluation subject workplace name and evaluation category for workplace evaluation, and every workplace condition defect influence item. It is a figure which shows the specific flow of the calculation which evaluates the defect incidence degree of the whole workplace, the average defect rate of the whole workplace according to the workplace condition defect influence item shown in FIG. FIG. 10 is a diagram showing evaluation results by the flow shown in FIG. 9, by evaluation category, by work condition defect effect items, by the degree of defect occurrence in the entire workplace, and by the average defect rate of the entire workplace. It is a figure which shows one Example of the screen which outputs a workplace evaluation result. It is a figure for demonstrating the model in which a defect generate | occur | produces by the components assembly | attachment operation | movement which concerns on this invention. It is a functional block diagram which shows one Example of the function structure in the product evaluation part of the evaluation apparatus which concerns on this invention.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Input means, 2 ... Display means, 3 ... Calculation means, 4 ... Memory | storage device, 4a ... Memory part for manufacturing workplace evaluation, 4a1 ... Database for manufacturing workplace evaluation, 4b ... storage part for product structure evaluation, 4b1 ... database for product structure evaluation, 5 ... output means, 10 ... evaluation device, 10a ... workplace evaluation part, 10b ... product evaluation part, 20 ... calculation system, 31 ... ROM, 32 ... CPU, 33 ... RAM, 34 ... interface, 35 ... bus line, 51 ... discriminating unit, 52 ... workplace Condition item-specific influence calculation unit, 53 ... workplace constant calculation unit, 54 ... message control unit, 54a ... category-specific influence calculation unit, 54b ... message determination unit, 131 ... discrimination unit 132 ... Product structure defect rate calculation unit, 33 ... bad phenomenon estimation unit, 134 ... production cost calculation unit.

Claims (3)

Evaluation of a manufacturing workplace that evaluates the easiness of occurrence of a defect in an evaluation target manufacturing workplace using an evaluation apparatus having a storage means, an input means, an evaluation means, and an output means, and presents a cause of failure having a large room for improvement. A method,
A plurality of workplace condition bad influence items related to evaluation categories consisting of preset workers, production equipment, production conditions, production physical environment, and management, and reference production at least at the standard workplace level for each of the work condition bad influence items A defect creation coefficient that is a defect generation suppression ability for work, a defect handling time coefficient that is a countermeasure capacity when a defect occurs, and a defect extraction degree coefficient that is a defect detection capability are set, and between the items in each workplace condition defect influence item A workplace database storage process in which a workplace evaluation database in which a relative weight coefficient is set is created in advance and stored in the storage means ;
Using the input means, a workplace level input process of inputting at least a workplace level level for each of the workplace condition defect effect items in the manufacturing workplace to be evaluated ;
Using the evaluation means, the workplace level input is performed based on the set values of the defect creation coefficient, defect handling time coefficient, and defect extraction degree coefficient for at least the reference manufacturing level in the workplace evaluation database. Corresponding to the workplace level level for each workplace condition defect effect item entered in the process, and by multiplying the relative weight coefficient between items in each workplace condition defect impact item, the defect creation degree index and defect handling time degree Each of the index of the defect and the index of the defect extraction degree is calculated, and for each of the workplace condition defect influence items, the defect occurrence that is the sum of the defect creation index, the defect handling time index, and the defect extraction index is calculated. Degree of occurrence of defects in each workplace condition defect item at the manufacturing workplace to be evaluated by calculating an index of room for improvement by the difference between the degree of failure and the degree of occurrence of defects at the ideal workplace And the evaluation process to assess the magnitude of the reduction effect,
A method of evaluating a manufacturing workplace , comprising: using the output means, and presenting a plurality of workplace condition bad influence items in order of increasing room for improvement to support countermeasure determination to be tackled . In the workplace database storage process, the workplace evaluation database further shows countermeasures for reducing the occurrence of defects at a level where the workplace level does not reach the ideal workplace, corresponding to each work condition defect effect item. Create and store advice or comment information in advance.
In the presentation process by the output means, an advice or comment indicating a countermeasure plan for reducing the degree of occurrence of defects read out from the workplace evaluation database is presented together with each workplace condition defect effect item. The manufacturing workplace evaluation method according to claim 1, wherein: A manufacturing workplace evaluation method for evaluating and estimating the likelihood of occurrence of defects in a manufacturing workplace to be evaluated using an evaluation apparatus comprising a storage means, an input means, an evaluation means, and an output means,
A plurality of workplace condition bad influence items related to evaluation categories consisting of preset workers, production equipment, production conditions, production physical environment, and management, and reference production at least at the standard workplace level for each of the work condition bad influence items A defect creation coefficient that is a defect generation suppression ability for work, a defect handling time coefficient that is a countermeasure capacity when a defect occurs, and a defect extraction degree coefficient that is a defect detection capability are set, and between the items in each workplace condition defect influence item A workplace database storage process in which a workplace evaluation database in which a relative weight coefficient is set is created and stored in the storage means;
Using the input means, a workplace level input process of inputting at least a workplace level level for each of the workplace condition defect effect items in the manufacturing workplace to be evaluated;
  Using the evaluation means, the workplace level input is performed based on the set values of the defect creation coefficient, defect handling time coefficient, and defect extraction degree coefficient for at least the reference manufacturing level in the workplace evaluation database. Corresponding to the workplace level level for each workplace condition defect effect item entered in the process, and by multiplying the relative weight coefficient between items in each workplace condition defect impact item, the defect creation degree index and defect handling time degree For each of the evaluation categories, the total of the defect creation degree index, the total of the defect handling time degree index, and the total of the defect extraction degree index, and those A workplace assessment process that evaluates the likelihood of defects in the production workplace subject to assessment for each assessment category,
A method for evaluating a manufacturing workplace, comprising: using the output means, and presenting a degree of occurrence of defects for each evaluation category to assist in determining measures to be taken.

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