CN116402388A - Welding workstation worker operation fatigue comprehensive evaluation analysis method based on combined weighting-cloud model - Google Patents
Welding workstation worker operation fatigue comprehensive evaluation analysis method based on combined weighting-cloud model Download PDFInfo
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Abstract
The invention relates to a welding workstation worker operation fatigue comprehensive evaluation analysis method based on a combined weighting-cloud model, which comprises the following steps: and analyzing the fatigue factors of workers in the automobile seat framework welding workstation, and constructing a fatigue evaluation system of the workers in the automobile seat framework welding workstation from the aspect of physiological and psychological fatigue. And calculating the weight of each factor by combining with fuzzy AHP-DEMATEL combined weighting, and adopting a cloud model comprehensive evaluation method to reduce subjective influence of fatigue evaluation and determine the fatigue grade of workers in a welding workstation. The fatigue index calculation method is simple and practical, accurately quantifies the fatigue of the worker in work, improves the fatigue index calculation precision, and provides scientific basis for evaluating the work fatigue degree of the worker.
Description
Technical Field
The invention relates to a welding workstation worker operation fatigue comprehensive evaluation analysis method based on a combined weighting-cloud model, and belongs to the technical field of human engineering and ergonomics.
Background
In the welding operation process of the automobile seat framework, the work fatigue of workers influences the work efficiency of the workers and the quality of man-machine cooperation, and the method has important significance for further improving the efficiency of man-machine cooperation and accurately evaluating and analyzing the work fatigue of the workers of the welding workstation in the man-machine cooperation operation. At present, scholars at home and abroad develop a great deal of extensive and intensive research on the aspects of measuring the fatigue factors and the physiological fatigue of workers, and achieve a certain effect. However, objective and accurate quantification is not performed on the aspect of psychological fatigue measurement, so that comprehensive fatigue indexes of workers are calculated inaccurately, and the calculation of the working time of the workers is influenced. Therefore, the fatigue degree of the operation workers is accurately estimated, the fatigue degree is reasonably and quantitatively analyzed, and the method has important significance for improving the man-machine cooperative work efficiency.
Disclosure of Invention
Aiming at the technical requirements and problems, the invention aims to provide a comprehensive evaluation analysis method for the fatigue degree of workers in a combined weighting-cloud model welding workstation, which can objectively and reasonably quantify the fatigue condition of the workers by combining physiological fatigue and psychological fatigue, and further obtain a comprehensive fatigue index, so that the fatigue condition of the workers can be objectively and accurately evaluated. Firstly, analyzing fatigue factors of workers of an automobile seat framework welding workstation, constructing an automobile seat framework welding workstation worker fatigue evaluation system from physiological and psychological fatigue aspects, calculating weights of the factors by combining fuzzy AHP-DEMATEL (analytic hierarchy process-decision experiment and evaluation laboratory method) combined weighting on the basis, adopting a cloud model comprehensive evaluation method, reducing subjective influence of fatigue evaluation, and determining fatigue grade of welding workstation workers. The invention discloses a welding workstation worker operation fatigue comprehensive evaluation analysis method based on a combined weighting-cloud model, which comprises the following steps:
s1, constructing a fatigue evaluation index system of workers in an automobile seat framework welding workstation;
s2, analyzing the working fatigue of workers at an automobile seat framework welding workstation;
s3, a comprehensive evaluation method for the fatigue degree of the workers in the welding workstation based on the combined weighting-cloud model;
s4, analyzing examples.
Preferably, the step S1 includes the following sub-steps:
specifically, in the step S1: s11, analyzing fatigue factors of workers in the automobile seat framework welding workstation, and dividing a fatigue index layer into two indexes of physiological fatigue and psychological fatigue by taking the fatigue of man-machine cooperation welding operation as a target. In an automobile seat framework welding production line, factors influencing physiological fatigue of workers mainly comprise upper limb muscle fatigue and back muscle fatigue; factors influencing the mental fatigue of workers' work are mainly mental load, time stress, man-machine tension and man-machine satisfaction.
Specifically, in the step S1: s12, a fatigue degree evaluation index system of workers of the automobile seat framework welding workstation mainly comprises physiological fatigue and psychological fatigue, and human factor elements related to human-computer cooperative production are divided into two layers. The first layer is the target layer and comprises two parts of physiological fatigue and psychological fatigue. The second layer is an index layer, and physiological fatigue is divided into upper limb muscle fatigue and back muscle fatigue through analysis of the welding production operation characteristics of the automobile seat framework, and psychological fatigue is divided into rhinoceros coincidence degree, time pressure, man-machine tension degree and man-machine satisfaction degree.
Preferably, the step S2 includes the following sub-steps:
specifically, in the step S2: s21, evaluating upper limb muscle fatigue degree analysis based on RULA (RapidUpper LimbAssessment: rapid upper limb evaluation), performing simulation analysis on RULA comfort degree of workers in an automobile seat framework welding workstation based on Jack (an ergonomic simulation software) ergonomic simulation software, wherein a RULA analysis scoring formula is as follows
Wherein R is i RULA scoring of i jobs, R ij The RULA score for the first step of the ith job j, m is the number of steps of job decomposition.
Specifically, in the step S2: s22, back muscle fatigue analysis based on NIOSH (national institute of occupational safety and health) manual lifting equation
Wherein m is ref For a constant load, the common working population is 99% male, 90% female or 95% male and female (composed of the same number of men and women) acceptable m ref (kg) 23kg; h is a m (h m =0.25/h) is a horizontal distance coefficient, h (m) is a horizontal distance between the center of the palm and the middle of the two ankle joints when lifting starts or stops; v M (v M =1-0.3x0.75-v) is a vertical height coefficient, v (m) is a vertical distance of the palm from the ground at the start or stop of lifting; d, d M (d M =0.82+0.045/d) is the vertical displacement coefficient, d (m) is the vertical spacing of the lift start and stop; a, a M (a M =1-0.0032×α) is an asymmetry coefficient, α (degrees, °) is an angle from the sagittal plane; f (f) M (times/minutes, PM) is a frequency coefficient, and different coefficients are determined according to the lifting frequency table; c M To grasp the quality coefficient, according to the difficulty1.00, 0.95 and 0.90.
Specifically, in the step S2: s23, analyzing the psychological load degree, establishing psychological load analysis and evaluation indexes, and calculating a formula of the psychological load of the operator by adopting an information entropy method
H＝f(H g ,H r )＝H g +H r
Wherein H is g Representing perceived complexity, H r Representing the cognitive complexity, H representing the total psychological load, the cognitive complexity and the cognitive complexity calculation formula
Wherein H(s) represents an entropy value, p i Representing the probability that the system is in the ith state, log 2 p i Is the weight of the probability that the system is in the i-th state.
Specifically, in the step S2: s24, time pressure analysis, namely, in terms of welding workstation workers, the workers need to make corresponding preparation work before the welding robot reaches the station, and the production cycle of the welding seat framework is selected as rated time. Under the condition that the welding robot is not stopped, the work that welding workstation workers need to process in the man-machine cooperation production work is as follows: installation work, unloading work and inspection work. In addition, the time required to pick up the goods is also a necessary time consumption. The calculation formula for the welding workstation worker time pressure can be obtained by the method as follows:
wherein TS represents time pressure, ΣT i CT is the production cycle time of the car seat frame, representing the time required for a welding workstation worker to operate.
∑T i ＝nT f +nT b +T k +T w +T x
Wherein n represents the number of the welding robots served by workers, T f Represents the loading and unloading time of a single automobile seat framework, T b Represents the time for starting the welding robot, T k Representative of the inspection time of the automobile seat framework, T w Representing the time of the worker to take the goods and walk in the production period, T k Representing downtime repair time.
Specifically, in the step S2: s25, analyzing the human-machine tension and the human-machine satisfaction, and collecting subjective feelings of welding workstation workers on the fatigue degree of the human-machine tension and the human-machine satisfaction through a questionnaire issuing mode, wherein the subjective feelings comprise two parts: the man-machine tension has an influence on the operation speed and an influence on the fatigue of workers, and the man-machine satisfaction has an influence on the operation speed and an influence on the fatigue of workers. The man-machine tension and man-machine satisfaction calculation formula is as follows:
wherein R represents the score of man-machine tension and man-machine satisfaction, where n represents the total number of questionnaires and x represents the score of a single questionnaire question.
Preferably, the step S3 includes the following sub-steps:
specifically, in the step S3: s31, based on fuzzy AHP initial weight calculation, combining a traditional nine-level scale method and a triangle fuzzy number to establish a nine-level fuzzy scale table, collecting judging opinions of expert on the relative importance of each factor by two by utilizing the nine-level fuzzy scale table, assuming that a certain index layer has n factors,
the relative importance of the ith factor to the jth factor determined by the kth expert, the fuzzy determination matrix of the index layer>
Calculating the triangle fuzzy number of the fatigue factor weight of each level by using a correction formula:
wherein:
and the triangle fuzzy number of the single-ranking weight of the ith fatigue factor judged by the kth expert.
Assuming that index layers below the target layer are the 1 st layer and each index layer is the 2 nd to (n-1) th layers in sequence, the single ranking weight iterative calculation of each factor can be expressed as the total ranking weight relative to the target layer:
to facilitate the sorting and the calculation of the comprehensive weights, the results are deblurred. Setting a certain triangle fuzzy number
The defuzzification value of the triangular blur number can be formulated as follows
Line computation
Specifically, in the step S3: s32, based on DEATE centrality calculation, a mapping relation between the linguistic variable and the fuzzy number is established, and the angular fuzzy number is converted into an accurate value. Collecting judgment comments of expert on the degree of mutual influence among factors, and setting
For the kth expertAs a result of the determination, the initial direct impact matrix may be expressed as:
calculating left and right standard values
And->
Calculating a comprehensive normalized value
Calculating the influence value of the ith factor on the jth factor
According to
Constructing a fuzzy direct influence matrix +.>
And then calculating the influence degree, the affected degree, the cause degree and the center degree of each fatigue factor by adopting a traditional DEMATEL method.
Specifically, in the step S3: s33, fuzzy AHP-DEMATEL based comprehensive weight calculation
Wherein w is i Is the comprehensive weight of the first factor, h i The defuzzification value, m, of the initial weight for the ith factor i Is the centrality of the ith factor.
Specifically, in the step S3: s34, dividing the fatigue degree into 5 grades, and giving out corresponding evaluation value ranges and characteristic parameters.
Specifically, in the step S3: s35, counting comprehensive weights which are obtained based on fuzzy AHP-DEMATEL calculation and are considered below by each expert, and obtaining cloud model parameters of the comprehensive weights by using a reverse cloud generator, wherein the expression of a comprehensive weight matrix is as follows
Specifically, in the step S3: s36, assigning values to the grades of the fatigue degree evaluation results of the n fatigue factors according to the above groups, and obtaining cloud model characteristic parameters of the comprehensive evaluation value by adopting a reverse cloud algorithm to a statistical sample, wherein the comprehensive evaluation matrix expression is as follows:
specifically, in the step S3: s37, synthesizing the comprehensive weight matrix W and the comprehensive evaluation matrix V to obtain fatigue cloud model parameters, wherein the expression is as follows:
preferably, the step S4 includes the following sub-steps:
specifically, the step S4: s41, calculating the fatigue degree of the upper limb muscles.
Specifically, in the step S4: s42, back muscle fatigue degree calculation
Specifically, in the step S4: s43, calculating psychological load degree
Specifically, in the step S4: s44, calculating time pressure
Specifically, in the step S4: s45, calculating man-machine satisfaction and man-machine tension
Specifically, in the step S4: s46, determining initial weight based on fuzzy AHP
Specifically, in the step S4: s47, determination of concentricity based on DEMATEL
Specifically, in the step S4: s48, comprehensive weight calculation
Specifically, in the step S4: s49, comprehensive fatigue degree evaluation based on cloud model
The beneficial effects of the invention are as follows: the fatigue level is comprehensively evaluated, a fatigue level model is established, seven factors which mainly influence the fatigue of workers in the welding workstation at two levels are listed, and the factors are limb muscle fatigue, back muscle fatigue, psychological load degree, time pressure, man-machine tension and man-machine satisfaction. Adopting an RULA evaluation method, and analyzing the muscle fatigue of the upper limbs of the workers based on Jack ergonomic simulation; analyzing back muscle fatigue by adopting an improved NIOSH manual lifting equation; processing the psychological load degree by adopting an information entropy method; calculating the time pressure by using a mathematical method; and counting the man-machine tension and man-machine satisfaction of workers in man-machine cooperation operation by using a point questionnaire method. On the basis, the fatigue degree of the welding workstation workers is comprehensively evaluated based on the cloud model of the layer fuzzy AHP-DEMATEI combined weighting method, and finally the comprehensive fatigue degree grade of the welding workstation workers in a certain state in the production operation process is determined.
Drawings
FIG. 1 is a hierarchical model of fatigue according to the present invention;
FIGS. 2a-2c illustrate three worker models of the welding workstation worker model of the present invention, respectively;
FIG. 3 is a welding workstation worker parameter of the present invention;
FIGS. 4a and 4b show, from two sides, respectively, a human-machine collaboration model of a welding station of the present invention;
FIG. 5 is a psychological load analysis and assessment according to the present invention;
FIG. 6 is a standard cloud model of the present invention;
FIG. 7 is an arc welding workstation worker operation of the present invention;
FIG. 8 is a cognitive information graph of a shutdown repair operation of the present invention;
fig. 9a and 9b are questionnaire results of the present invention, wherein fig. 9a is a human stress result and fig. 9b is a human satisfaction result;
fig. 10 is a cloud model for fatigue comprehensive evaluation according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, but it should be understood that the examples are intended to illustrate the invention and not to limit the invention.
Comprehensive evaluation analysis method for fatigue degree of workers in combined weighting-cloud model welding workstation
Further, a fatigue factor analysis is performed on workers in the automobile seat framework welding workstation, a fatigue level model of man-machine cooperation welding operation is constructed from three aspects of 'man-machine-environment', fatigue of man-machine cooperation welding operation is used as a target, and a fatigue index layer is divided into two indexes of physiological fatigue and psychological fatigue. In an automobile seat framework welding production line, factors influencing physiological fatigue of workers mainly comprise upper limb muscle fatigue and back muscle fatigue; factors influencing the mental fatigue of workers' work are mainly mental load, time stress, man-machine tension and man-machine satisfaction.
Further, an index system diagram for evaluating the fatigue degree of workers in the automobile seat framework welding workstation is constructed, and human factor elements related to the man-machine cooperation production are divided into two layers. The first layer is the target layer and comprises two parts of physiological fatigue and psychological fatigue. The second layer is an index layer, and physiological fatigue is divided into upper limb muscle fatigue and back muscle fatigue through analysis of the welding production operation characteristics of the automobile seat framework, and psychological fatigue is divided into rhinoceros coincidence degree, time pressure, man-machine tension degree and man-machine satisfaction degree.
Further, modeling an automobile seat framework welding production line based on RULA evaluation upper limb muscle fatigue analysis, and modeling workers in a welding workstation at the same time; leading digital persons into a bilateral welding production line model; defining digital human working posture parameters; the RULA scores for the different work poses were analyzed. In the man-machine cooperation production of welding of the automobile seat framework, three parts of loading, unloading and checking of the workpiece seat framework are mainly involved, RULA analysis is carried out on each gesture of each working step respectively to obtain RULA score of each gesture, and the average score of all decomposition steps is used as the RULA score of the operation
Wherein R is i RULA scoring of i jobs, R ij The RULA score for the first step of the ith job j, m is the number of steps of job decomposition.
Further, back muscle fatigue analysis based on NIOSH manual lifting equation
Wherein m is ref For a constant load, the common working population is 99% male, 90% female or 95% male and female (composed of the same number of men and women) acceptable m ref (kg) 23kg; h is a m (h m =0.25/h) is a horizontal distance coefficient, h (m) is a horizontal distance between the center of the palm and the middle of the two ankle joints when lifting starts or stops; v M (v M =1-0.3x0.75-v) is a vertical height coefficient, v (m) is a vertical distance of the palm from the ground at the start or stop of lifting; d, d M (d M =0.82+0.045/d) is the vertical displacement coefficient, d (m) is the vertical spacing of the lift start and stop; a, a M (a M =1-0.0032×α) is an asymmetry coefficient, α (degrees, °) is an angle from the sagittal plane; f (f) M (sub/minute, PM) is a frequency coefficient, and frequency search is mentionedThe table determines different coefficients; c M The grip quality coefficients were easily defined as 1.00, 0.95 and 0.90.
Further, psychological load degree analysis is carried out, and the psychological load in the sensing stage is measured by sensing complexity; the cognitive complexity is used to measure the magnitude of mental load in the cognitive phase. By evaluating and analyzing psychological load, the psychological load of the operator is calculated by adopting an information entropy method
H＝f(H g ,H r )＝H g +H r
Wherein H is g Representing perceived complexity, H r Representing cognitive complexity, H represents overall psychological burden.
Method for selecting information entropy to calculate perception complexity and cognition complexity
Wherein H(s) represents an entropy value, p i Representing the probability that the system is in the ith state, log 2 p i Is the weight of the probability that the system is in the i-th state.
Further, in the case of a welding station worker, the worker must make a corresponding preparation before the welding robot reaches the station, and the welding seat frame production cycle is selected as the nominal time. Under the condition that the welding robot is not stopped, the work that welding workstation workers need to process in the man-machine cooperation production work is as follows: installation work, unloading work and inspection work. In addition, the time required to pick up the goods is also a necessary time consumption. The calculation formula for the welding workstation worker time pressure can be obtained by the method as follows:
wherein TS represents time pressure, ΣT i CT is the production cycle time of the car seat frame, representing the time required for a welding workstation worker to operate.
∑T i ＝nT f +nT b +T k +T w +T x
Wherein n represents the number of the welding robots served by workers, T f Represents the loading and unloading time of a single automobile seat framework, T b Represents the time for starting the welding robot, T k Representative of the inspection time of the automobile seat framework, T w Representing the time of the worker to take the goods and walk in the production period, T k Representing downtime repair time.
Further, the man-machine tension and man-machine satisfaction analysis, by means of issuing a questionnaire, collects subjective feelings of welding workstation workers about the man-machine tension and man-machine satisfaction on fatigue, including two parts: the man-machine tension has an influence on the operation speed and an influence on the fatigue of workers, and the man-machine satisfaction has an influence on the operation speed and an influence on the fatigue of workers. Human-computer tension and human-computer satisfaction calculation formula
Wherein R represents the score of man-machine tension and man-machine satisfaction, where n represents the total number of questionnaires and x represents the score of a single questionnaire question.
Preferably, the step S3 includes the following sub-steps:
further, based on fuzzy AHP initial weight calculation, combining the traditional nine-level scaling method and the triangular fuzzy number to establish a nine-level fuzzy scaling table, collecting judgment opinion of relative importance of each factor by an expert by using the nine-level fuzzy scaling table, assuming that a certain index layer has n factors in total,
the relative importance of the ith factor to the jth factor determined by the kth expert, the fuzzy determination matrix of the index layer>
Calculating triangle fuzzy number of fatigue factor weight of each level by using correction formula
In the method, in the process of the invention,
and the triangle fuzzy number of the single-ranking weight of the ith fatigue factor judged by the kth expert. Assuming that index layers below the target layer are the 1 st layer and each index layer is the 2 nd to (n-1) th layers in sequence, the single ranking weight iterative calculation of each factor can be expressed as the total ranking weight relative to the target layer:
to facilitate the sorting and the calculation of the comprehensive weight, the result is defuzzified, and a certain triangle fuzzy number is set
The defuzzification value formula for the triangular fuzzy number
Further, based on DEMATEL centrality calculation, a mapping relation between the language variable and the fuzzy number is established
TABLE 1 mapping relationship
The triangle fuzzy number is converted into an accurate value, and the expert is collected for eachJudgment opinion of degree of mutual influence between factors is provided
For the kth expert's decision, then the initial direct impact matrix can be expressed as:
calculating left and right standard values
And->
Calculating a comprehensive normalized value
Calculating the influence value of the ith factor on the jth factor
According to
Constructing a fuzzy direct influence matrix +.>
And then calculating the influence degree, the affected degree, the cause degree and the center degree of each fatigue factor by adopting a traditional DEMATEL method.
Further, fuzzy AHP-DEMATEL based comprehensive weight calculation
Wherein w is i Is the comprehensive weight of the first factor, h i The defuzzification value, m, of the initial weight for the ith factor i Is the centrality of the ith factor.
Further, the fatigue degree is divided into 5 grades, and corresponding evaluation value ranges and characteristic parameters are given.
Table 2 fatigue evaluation value ranges and characteristic parameters
Further, statistics is based on comprehensive weights of the meanings of each expert obtained through fuzzy AHP-DEMATEL calculation, cloud model parameters of the comprehensive weights are obtained through a reverse cloud generator, and the expression of the comprehensive weight matrix is as follows
Further, according to the fatigue degree evaluation results of the n fatigue factors from the above group, assigning the grades, and then obtaining the cloud model characteristic parameters of the comprehensive evaluation value by adopting a reverse cloud algorithm on the statistical sample, wherein the comprehensive evaluation matrix expression is as follows:
further, synthesizing the comprehensive weight matrix W and the comprehensive evaluation matrix V to obtain fatigue cloud model parameters, wherein the expression is as follows:
specifically, in the example analysis, an automobile seat framework welding workstation worker is taken as an example, an arc welding workstation worker is selected as a research object, and the operation process of the worker is analyzed.
Further, the upper limb muscle fatigue degree is calculated, and variable parameters affecting comfort degree such as action load, duration time and working frequency are set by using JACK software for working tasks and working characteristics of workers in the automobile seat framework welding workstation. Sequentially simulating actions of a human in the welding production of the automobile seat framework, and outputting a grade scoring table of each operation action of a welder workstation worker through RULL evaluation analysis commands in simulation
TABLE 3 action class scoring
Comprehensive scoring was performed on the weld workstation worker RULA evaluation:
and grading and rounding to obtain the final RULA grade of 2 of the action in the production of the workers in the welding workstation. In the RULA scoring level, 2-level operation indicates that the posture needs to be studied and changed after a long time, and muscle fatigue is easy to occur in a state of being in such a working posture for a long time, so that the working speed is reduced, and a certain influence on human-computer cooperation is exerted.
Further, the back muscle fatigue degree is calculated, the average value is measured by adopting a method of measuring a plurality of times, the distance h from the hand to the side surface central line of the body, the distance v from the ground to the hand at the conveying height, the initial position to the final placing position d of the workpiece, the included angle alpha between the initial vertical surface of the workpiece and the vertical surface of the workpiece, and the conveying frequency f within 1 minute are measured, and the weight of the workpiece is measured as shown in the table.
TABLE 4 results of parameter calculations
LI＝L/RWL＝1/18.44＝0.054
As shown in the calculation result, li=0.054 <1, which indicates that the present transportation method is acceptable, long-time operation will not cause harm to human body, and the fatigue of workers will not be greatly affected.
Furthermore, the psychological load degree is calculated, the main tasks of the workers in the welding workstation are ten tasks of material taking and feeding, scanning, labeling, warehousing, shutdown repairing, checking, deburring and the like, so that the total amount of information to be perceived is I=10, p i =1/10=0.1. The perceived complexity caused by superposition of all tasks is as follows:
the shutdown repairing operation of the welding workstation workers is regular operation, the alarm perception of the welding robot belongs to the perception complexity in the operation, the process is that the workers perceive that the indicator lights flash and alarm sound after the welding robot is shut down, and then the workers accumulate the perception complexity formed by all the shutdown conditions of the welding robot. According to the actual measurement data of the shutdown repair of the welding workstation workers, the statistical analysis can obtain a statistical table of the shutdown probability of the welding workstation workers as follows.
Table 5 shutdown probability statistics
The statistical data is substituted into the formula (2.4) to calculate and analyze, so that the perceived complexity of the shutdown repair problem can be obtained as follows:
to sum up, the worker perceives the complexity as:
H g ＝H g2 +H g2 ＝2.3903+0.3632＝2.7535
the cognitive complexity of the welding workstation workers is calculated, the cognitive complexity is generated when the welding workstation workers only stop for repairing, and the formation of the cognitive complexity is two, so that on one hand, the workers need to judge the cause of the stop after sensing the alarm information of the welding robot. On the other hand, after judging the shutdown cause, the corresponding repair rule needs to be searched for under the error, and the repair operation is performed. Five reasons for stopping the welding robot are respectively error recognition of workpieces of the welding robot, alarm stopping of the welding robot, insufficient welding protection gas of the welding robot, replacement of welding products and fault-free false alarm, five reasons with the highest occurrence probability are selected for analysis of cognitive complexity, the occurrence probability of the five reasons accounts for 95% of the total stopping times, and the five reasons are main reasons for stopping. According to the reasons of shutdown repair of the welding robot, a welding workstation worker judges the reasons of shutdown, and shutdown repair operation is carried out according to repair rules. And drawing a cognitive information graph of the shutdown repair operation of the worker. The complexity of each repair operation is different, so that the known complexity of the welding workstation workers for repairing different types of machine halt is different. And according to statistics and analysis of the actual data of the welding production, obtaining a cognitive complexity calculation table of the shutdown cause of the welding robot as shown in the following table.
TABLE 6 cognitive complexity of the causes of downtime
Substituting the statistical data into the formula can obtain that the cognitive complexity generated when the worker stops repairing operation to judge the stop reason is
According to the different shutdown reasons of the welding robot, the selected repair steps are different when repairing the welding robot. The number of repair steps included in each repair rule is regarded as the information amount corresponding to the rule, and according to the difference of the information amounts included in each repair step, a cognitive complexity calculation table of the repair rule of the welding robot can be obtained as shown in the following table.
TABLE 7 cognitive complexity of repair rules
Substituting the data into a formula can obtain the cognitive complexity of the shutdown repair problem of the worker as follows:
to sum up, the cognitive complexity of the problem of shutdown repair of the welding workstation workers is as follows:
H r ＝H r2 +H r2 ＝1.5558+2.2170＝3.7728
the mental load of the workers in the welding working station is calculated, and the mental load born by the welding working station is the result of the combined action of the perception complexity of the workers and the cognition complexity of the workers in the working process of the workers. Substituting the analysis result into an operator psychological load formula to obtain the psychological load of the welding workstation workers as follows
H＝f(H g ,H r )＝H g +H r ＝2.7535+3.7728＝6.5281
Further, calculating time pressure, carrying out flow analysis on the current production process of the workstation according to the observation record data, and analyzing by using a flow program chart to obtain the welding workstation worker operation analysis to obtain the loading and unloading time T of the automobile seat framework f 33 (s/table), start welding robot time T b =9 (s/table), car seat skeleton check time T k =44 (s/table), travel time T of the picked-up goods in each production cycle of the car seat frame w ＝18(s)。
In the double-sided production line, a welding station worker performs man-machine cooperation work on only one welding robot, so n=1, and a production cycle ct=108(s) of welding an automobile seat frame is known. From the above, the repair time is 15.09% of the total work time of the welding workstation workers, so the weight w of the shutdown repair operation is taken x =0.15, so shutdown repair time T x ＝w x X CT. Wherein, because man-machine cooperation degree is not high, in man-machine cooperation welding production operation, the workman can accomplish the inspection operation task when welding robot welding operation, consequently can cause the phenomenon that weldment work station workman waited for, wait time is 10s. Calculating the time pressure Sigma T of the work of the welding workstation workers i ＝nT f +nT b +T k +T w +T x ＝33+9+44+18+10+0.15×108＝130.2(s)
Therefore, the welding station worker working time pressure is 1.21.
Further, according to the tabulated questionnaire data, a human-computer tension and human-computer satisfaction score distribution map is drawn. Man-machine satisfaction and man-machine tension calculation
Thus, the welding station had a man-machine tension of 2.9406 and a man-machine satisfaction of 3.0844.
Further, based on the initial weight determination of fuzzy AHP, the triangle fuzzy number of the fatigue evaluation model is
Further, based on the determination of the DEMATEL centrality, the influence, affected, cause and centrality of each factor are calculated using SPSS, and the calculation results are shown in the following table. The weight of muscle fatigue is highest as seen from the table.
Table 8 centrality calculations
Further, comprehensive weight calculation is carried out, and initial weights and centrality of fatigue factors are synthesized to obtain comprehensive weight values and are sequenced.
Table 9 comprehensive weight values
Further, based on the comprehensive fatigue evaluation of the cloud model, calculating to obtain characteristic parameters of the comprehensive fatigue evaluation cloud model R. And (5) carrying out model drawing by using MATLAB software. In the standard cloud model, the fatigue levels are five levels of "low", "lower", "medium", "higher" and "high" in order. And according to the characteristic parameters of the fatigue evaluation cloud R obtained through calculation, 5 fatigue grades and the fatigue grade evaluation result of the welding workstation workers are simulated and displayed by using a forward cloud generator. From the figure, it can be seen that the fatigue level of the welding workstation workers in this state lies between "lower" and "medium", but the cloud represented by the distance "medium" is closer. Therefore, it can be approximately considered that the fatigue level of the welding station worker in this state is medium.
The fatigue degree of the welding workstation workers is lower in general from the evaluation result of the workers by carrying out field investigation on the welding workstation workers and analyzing subjective feelings of the fatigue degree of the welding workstation workers in a questionnaire mode. The fatigue degree of workers in the welding workstation is objectively analyzed from three factors of environment, man and machine, the evaluation result is moderate, and the fatigue degree evaluation result of the workers is consistent with the actual investigation result.
The fatigue level of the car seat frame welding workstation workers is 'medium', which indicates that in the state, the fatigue level of the welding workstation workers is acceptable, and the working state of the workers is good. Therefore, the relevant data of the working time of the worker in different fatigue states can be obtained by sampling from the actual test, and the completion time of the production task in the fatigue state can be estimated according to the fatigue state of the worker in the middle.
The foregoing description of the preferred embodiments of the present invention is not intended to limit the invention, and those skilled in the art may still make modifications to the above technical solutions or make equivalent substitutions of some of the technical features thereof. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A welding workstation worker operation fatigue comprehensive evaluation analysis method based on a combined weighting-cloud model is characterized by comprising the following steps:
s1, constructing a fatigue evaluation index system of workers in an automobile seat framework welding workstation;
s2, analyzing the working fatigue of workers at an automobile seat framework welding workstation;
s3, a comprehensive evaluation method for the fatigue degree of the workers in the welding workstation based on the combined weighting-cloud model;
s4, analyzing examples.
2. The welding workstation worker work fatigue comprehensive evaluation analysis method based on the combined weighting-cloud model as claimed in claim 1, wherein the step S1 includes the sub-steps of:
s11, analyzing fatigue factors of workers in an automobile seat framework welding workstation, and dividing a fatigue index layer into two indexes of physiological fatigue and psychological fatigue, wherein the physiological fatigue comprises upper limb muscle fatigue and back muscle fatigue by taking the fatigue of man-machine cooperation welding operation as a target; psychological fatigue includes psychological load, time stress, man-machine tension, and man-machine satisfaction;
s12, dividing human factor elements related to human-computer cooperative production into two layers, wherein the first layer is a target layer and comprises two parts of physiological fatigue and psychological fatigue, the second layer is an index layer, and dividing the physiological fatigue into upper limb muscle fatigue and back muscle fatigue and dividing the psychological fatigue into rhinoceros coincidence degree, time pressure, human-computer tension degree and human-computer satisfaction degree through analysis of characteristics of the welding production operation of the automobile seat framework.
3. The welding workstation worker work fatigue comprehensive evaluation analysis method based on the combined weighting-cloud model as claimed in claim 1, wherein the step S2 includes the sub-steps of:
s21, evaluating upper limb muscle fatigue degree analysis based on RULA
S22, back muscle fatigue analysis based on NIOSH manual lifting equation
Wherein m is ref For a constant load, the common working population is 99% male, 90% female or 95% male and female (composed of the same number of men and women) acceptable m ref (kg) 23kg; h is a m (h m =0.25/h) is a horizontal distance coefficient, h (m) is a horizontal distance between the center of the palm and the middle of the two ankle joints when lifting starts or stops; v M (v M =1-0.3x0.75-v) is a vertical height coefficient, v (m) is a vertical distance of the palm from the ground at the start or stop of lifting; d, d M (d M =0.82+0.045/d) is the vertical displacement coefficient, d (m) is the lift start and stopIs a vertical pitch of (2); a, a M (a M =1-0.0032×α) is an asymmetry coefficient, α (degrees, °) is an angle from the sagittal plane; f (f) M (times/minutes, PM) is a frequency coefficient, and different coefficients are determined according to the lifting frequency table; c M The grip quality coefficients were easily defined as 1.00, 0.95 and 0.90.
S23, analyzing the psychological load degree, wherein a formula for calculating the psychological load of the operator by adopting an information entropy method is shown as the following H=f (H) g ,H r )＝H g +H r
Wherein H is g Representing perceived complexity, H r Representing the cognitive complexity, H representing the total psychological load, the cognitive complexity and the cognitive complexity calculation formula
Wherein H(s) represents an entropy value, p i Representing the probability that the system is in the ith state, log 2 p i Is the weight of the probability that the system is in the i-th state.
S24, time pressure analysis and calculation formula of time pressure of workers in welding workstation
Wherein TS represents time pressure, ΣT i Representing the time required for the operation of the welding workstation workers, CT is the production cycle time of the car seat frame,
∑T i ＝nT f +nT b +T k +T w +T x
wherein n represents the number of the welding robots served by workers, T f Represents the loading and unloading time of a single automobile seat framework, T b Represents the time for starting the welding robot, T k Representative of the inspection time of the automobile seat framework, T w Representing the time of the worker to take the goods and walk in the production period, T k Representing the time for the shutdown repair,
s25, man-machine tighteningTenseness and man-machine satisfaction analysis, man-machine tenseness and man-machine satisfaction calculation formula
Wherein R represents the score of man-machine tension and man-machine satisfaction, where n represents the total number of questionnaires and x represents the score of a single questionnaire question.
4. The welding workstation worker work fatigue comprehensive evaluation analysis method based on the combined weighting-cloud model as claimed in claim 1, wherein the step S3 includes the sub-steps of:
s31, based on fuzzy AHP initial weight calculation, combining a traditional nine-level scale method and a triangular fuzzy number, establishing a nine-level fuzzy scale table, collecting judging opinion of expert on the relative importance of each factor by two by utilizing the nine-level fuzzy scale table, assuming that a certain index layer has n factors,
the relative importance of the ith factor to the jth factor determined by the kth expert, the fuzzy determination matrix of the index layer>
As shown below
Calculating the triangle fuzzy number of fatigue factor weight of each level
In the method, in the process of the invention,
the triangle fuzzy number representing the ith fatigue factor single ranking weight determined by the kth expert, the single ranking weight iterative calculation of each factor single ranking weight can be expressed as the total ranking weight relative to the target layer
Assuming a certain triangular blur number
Triangle ambiguity number defuzzification formula +.>
S32, based on DEMATEL centrality calculation, collecting judgment comments of expert on mutual influence degree among factors, and setting
For the k expert's decision, then the initial direct impact matrix may be expressed as
Calculating left and right standard values
And->
Calculating a comprehensive normalized value
Calculating the influence value of the ith factor on the jth factor,
according to
Constructing a fuzzy direct influence matrix +.>
Then the influence, the affected degree, the cause degree and the center degree of each fatigue factor are calculated by adopting the traditional DEMATEL method,
s33, fuzzy AHP-DEMATEL based comprehensive weight calculation
Wherein w is i Is the comprehensive weight of the first factor, h i The defuzzification value, m, of the initial weight for the ith factor i Is the centrality of the ith factor.
S34, dividing the fatigue into 5 grades, giving out corresponding evaluation value ranges and characteristic parameters,
s35, counting comprehensive weights which are obtained based on fuzzy AHP-DEMATEL calculation and are considered below by each expert, and obtaining cloud model parameters of the comprehensive weights by using a reverse cloud generator, wherein the expression of a comprehensive weight matrix is as follows
S36, the statistic sample adopts a reverse cloud algorithm to obtain cloud model characteristic parameters of the comprehensive evaluation value, and the comprehensive evaluation matrix expression is as follows
S37, synthesizing the comprehensive weight matrix W and the comprehensive evaluation matrix V to obtain fatigue degree cloud model parameters, wherein the expression is as follows
5. The welding workstation worker work fatigue comprehensive evaluation analysis method based on the combined weighting-cloud model as claimed in claim 1, wherein the step S4 includes the sub-steps of:
s41, calculating the fatigue degree of the upper limb muscles,
s42, calculating the fatigue degree of the back muscle,
s43, calculating the psychological load degree,
s44, calculating the time pressure of the product,
s45, calculating man-machine satisfaction and man-machine tension,
s46, determining based on the fuzzy AHP initial weight,
s47, determining based on the DEMATEL centrality,
s48, comprehensive weight calculation is performed,
s49, comprehensively evaluating the fatigue degree based on the cloud model.
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