JP5673567B2 - Manufacturing process efficiency prediction method, apparatus and program - Google Patents

Description

  INDUSTRIAL APPLICABILITY The present invention is suitable for use in predicting the consistent efficiency of the entire manufacturing process in a manufacturing process having a plurality of processes of series, direct connection, and multiple stages having different processing capacities, and manufacturing by combining a plurality of types of product groups. The present invention relates to a manufacturing process efficiency prediction method, apparatus, and program.

  The steel plate rolling process, which is a representative product of the steel industry, consists of a series of slab yard process, heating process, rough rolling process, finish rolling process, accelerated cooling process, shearing process in series, direct connection, and multiple stages. Yes.

  On the other hand, thick plates have a variety of thickness, width, length size and standard specifications, and the process conditions of each process are inevitably varied. For this reason, the rolling process is a large-scale, but also multi-product, small-mix production, and the rate-limiting step in the consistent process varies sequentially for each piece of slab, increasing the efficiency and productivity of the entire rolling process. Predicting accurately and simply is a technically extremely difficult task.

JP 2002-366219 A JP 2006-48540 A (Patent No. 4660137) JP 2007-179519 A JP-A-3-49853 (Patent No. 2802393) JP 2002-333911 A (Patent No. 4473467) JP 2003-162313 A JP-A-10-138102 JP 2008-27150 A (Patent No. 47575729) JP 2010-128679 A JP 2011-134283 A Special Table 2000-511118 JP 2004-78273 A (Patent No. 3976640) JP 7-191735 A (Patent No. 3203918)

"Introduction to Production Management I" Takahiro Fujimoto, Nikkei Inc., 2001 “Let's change the view of the field with a flow diagram of“ things ”and“ information ”based on the Toyota production system” Mike Rosar & John Sook, Nikkan Kogyo Shimbun, 2001

  As this type of technology, for example, the technology disclosed in Patent Document 1 uses a virtual factory model on a computer to evaluate a production plan by simulation that performs virtual production imitating real equipment in a factory. It is about. However, it is a method that assumes various conditions, calculates and accumulates individual events according to each condition sequentially, evaluates and compares the results with an evaluation function, and selects the condition that takes the optimum value. In large-scale, low-mix mixed-flow production, if the number of combinations of production permutations, that is, simulation calculation conditions, is enormous, even with the latest computer processing capacity, it takes a long time to calculate and is suitable for practical use. There is no problem.

  Further, the technology disclosed in Patent Document 2 relates to a method for producing and evaluating a production plan by representing a processing process as a discrete event model and performing a computer simulation for large-scale, multi-mix, small-mix production. It is. However, it mainly relates to a process for processing a plurality of products through a plurality of process paths, and is not suitable for calculating the consistency efficiency of a serial multi-stage process. Furthermore, the number of combinations of production permutations becomes enormous, and it takes a long time for calculation and is not suitable for practical use.

  The technique disclosed in Patent Document 3 targets a steelmaking process (refining-continuous casting) in the iron and steel industry, and uses Gantt charts to produce a production plan for producing a relatively wide variety of products in a plurality of processes. Concerning planning. However, if it is intended to be applied to a rolling process of thick plates with a large variety of small quantities and a large number of combinations of production permutations, the calculation takes a long time and is not suitable for practical use.

  The technique disclosed in Patent Document 4 relates to a manufacturing process in which the manufacturing order and the time required for each process are determined. However, this method is not applicable to large-scale, multi-product, small-volume mixed flow production such as a thick plate rolling process in which the processing time varies for each product.

  Further, the technique disclosed in Patent Document 5 relates to a large-scale production plan when a variety of products are manufactured in a plurality of processes. However, it is intended for a wide range of processes ranging from steelmaking to rolling in the steel industry, and is intended for rough calculation and prediction of daily production volume. It cannot be applied to applications that calculate integrated productivity (efficiency) with high accuracy.

  In addition, the technique disclosed in Patent Document 6 relates to a method for supporting production planning of a process for producing a small amount of various products in a plurality of processes, and the process having the maximum residence time is defined as a bottleneck process. An evaluation standard value is calculated and a simulation using a production line model is performed. However, when trying to apply to a manufacturing process such as thick plate rolling, where the combination of production permutations is enormous and the number of combinations of production permutations is enormous, even with the latest computer processing capacity, it takes a long time to calculate and is practical. Not suitable for.

  For example, in Non-Patent Documents 1 and 2, in the processing and assembly line for high-mix low-volume production, various types of parts and products are grouped into groups (based on the identity or similarity of the attributes and processes of parts and products). Group technologies are also introduced to optimize production by classifying them into parts families and product families.

  As an example of the application of group technology, in Patent Document 7, in an assembly processing line for high-mix low-volume production, product groups having similar product components or load on the production line are handled as a group of the same product, and the load on the production line is leveled. There are proposals to make it easier. However, since this method categorizes into groups of broad varieties based on knowledge about the manufacturing process, the accuracy of predicting the manufacturing load is not always sufficient.

  Patent Documents 8, 9, and 10 propose a method of creating a product group based on a product type classification logic created using a decision tree creation logic and predicting a manufacturing load for each product type. However, these techniques do not apply to a series of directly connected lines of a plurality of steps.

  Patent Documents 11, 12, and 13 propose a method for specifying a bottleneck process when a plurality of products are manufactured on a line having a plurality of processes. However, these techniques do not apply to a series of directly connected lines of a plurality of steps.

  As described above, existing production, manufacturing, and processing plans are assumed to have various conditions, such as by using a simulator, and individual events according to each condition are calculated and integrated sequentially, and the results are evaluated functions. In most cases, the conditions for selecting the conditions to obtain the optimum value by evaluating and comparing are calculated, and in large-scale, multi-mix, small-mix production, the number of combinations of production permutations, i.e., simulation conditions, becomes enormous. Increases / excesses, which is not suitable for practical use.

  In a manufacturing process that has multiple processes of series, direct connection, and multiple stages with different processing capacities and manufactures by combining multiple types of product groups, the load of each process fluctuates for each material, resulting in an integrated process for each material. Because the rate-limiting process in the inside varies sequentially, it is extremely difficult and important to technically predict the consistent efficiency / productivity of the entire process, and these can be calculated easily with a light computer load. No plan has been proposed so far.

  The present invention has been made in view of the above points, and is a manufacturing process in which a plurality of series of steps having different processing capabilities, series, direct connection, and multiple stages are combined and manufactured by combining a plurality of types of product groups. The purpose is to be able to calculate the overall consistency efficiency with high accuracy and simplicity.

The method for predicting the efficiency of a manufacturing process according to the present invention is a method for predicting the efficiency of a manufacturing process having a plurality of series, direct connection, and multi-stage processes having different processing capacities, and manufacturing by combining a plurality of types of product groups. Extract parameters that affect processing efficiency, classify them into product groups, identify the rate-limiting process in the series, direct connection, and multi-stage multiple processes for each product group, and obtain the inherent consistency efficiency for each product group. And a step of calculating a consistent efficiency of the entire manufacturing process in accordance with a composition ratio of the product group using a unique consistent efficiency for each product group.
Further, the manufacturing process efficiency prediction method according to the present invention is characterized in that the manufacturing process includes a slab yard process, a heating process for reheating the slab, and a reheated slab being rolled out to a predetermined width. The rough rolling process to perform, the finished steel slab to a predetermined thickness and length, and the finish rolling process to create the material by controlled rolling to roll at a predetermined temperature, the steel slab after finish rolling has a large water volume Multiple processes, such as an accelerated cooling process that cools with the cooling water and rolling process, and a shearing process that shears the cooled steel slab into a plurality of steel sheets of a predetermined width and length, are arranged in series, directly connected, and in multiple stages. It is the point which is the rolling process of the made thick board. In this case, the product group is classified in the order of steel grade, HCR (Hot Charged Rolling), CCR (Cold Charged Rolling), earring or cutting, and further subdivided by size. You may be made to do. Moreover, you may make it classify | categorize a size in order of slab thickness, aim thickness, and rolling width.
The production process efficiency prediction apparatus of the present invention is a production process efficiency prediction apparatus that has a plurality of series, direct connection, and multistage processes with different processing capacities, and is manufactured by combining a plurality of types of product groups. In addition to extracting parameters that affect processing efficiency and classifying into product groups, each product group is identified by determining the rate-limiting process in the series, direct connection, and multiple stages, and is unique to each product group. A storage means for storing a consistent efficiency, and an arithmetic means for calculating a consistent efficiency of the entire manufacturing process according to a composition ratio of the product group using a unique consistent efficiency for each product group. .
The program of the present invention is a program for predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and manufacturing by combining a plurality of types of product groups. Specific consistency efficiency for each product group, which is obtained by extracting parameters that affect the product, classifying the product group, and specifying the rate-limiting process in the series, direct connection, and multiple stages of each product group The computer is caused to function as a computing means for calculating the consistent efficiency of the entire manufacturing process in accordance with the composition ratio of the product group, using the storage means for storing the data and the consistent efficiency specific to each product group.

  According to the present invention, by specifying the rate-limiting process among a plurality of processes in series, direct connection, and multiple stages for each product group, and obtaining the inherent consistency efficiency for each product group, the product group that varies from period to period can be obtained. Depending on the composition ratio, the consistent efficiency of the entire manufacturing process can be easily calculated with high accuracy.

It is a figure which shows the example of the rolling line of the rolling process of a thick plate. It is a figure which shows the result of having subdivided by size about normal steel (CCR, with ears) and statistically calculating parameters. It is a figure which shows the ratio of the rate-limiting process of steel classification. It is a figure which shows the structure of the efficiency prediction apparatus of the manufacturing process which concerns on embodiment.

Preferred embodiments of the present invention will be described below with reference to the accompanying drawings.
In this embodiment, a rolling process of a thick plate, which is a representative product of the steel industry, is a manufacturing process that has a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and combines a plurality of types of product groups. set to target.

  FIG. 1 shows an example of a rolling line in a thick plate rolling process. In the rolling line, from the upstream side, a slab yard 101, a heating furnace 102, a rough rolling mill 103 as a reverse rolling mill, a finishing mill 104 as a reverse rolling mill, a straightening machine 105, a water cooling device 106, a cooling bed 107, a shearing machine. Equipment such as 108 is arranged, and the product flows in the direction of the arrow in the figure.

  That is, in the thick plate rolling process, the slab was supplied to the heating furnace 102, the “slab yard (SY) process” by the slab yard 101, the slab was reheated, the “heating process” by the heating furnace 102, and reheated. “Rough rolling process” by a roughing mill 103 that performs rolling out to a predetermined width of the slab, and by controlled rolling in which a steel piece after the widening is built to a predetermined thickness and length and rolled at a predetermined temperature “Finish rolling process” by finishing mill 104 that builds the material, steel strip after finishing rolling is cooled with a large amount of cooling water, and quenching structure such as bainite and martensite is built, and “accelerated cooling” by water cooling device 106 (CLC) process ”, multiple processes such as“ shear (DSS) process ”by the shearing machine 108, which shears the rolled and cooled steel pieces into a plurality of steel plates having a predetermined width and length. Column, direct connection, are arranged in multiple stages.

  In such a thick plate rolling process, there are factors that inhibit the processing efficiency in each step. For example, in the “slab yard process”, slab cutting speed, slab unit weight, cutting frequency, material circumstances, and the like can be mentioned. In addition, in the “heating step”, a charging temperature, a temperature raising capability, a heating condition, and the like can be given. In the “rough rolling process”, rolling speed, scheduling time, turn time, dummy pass, CR temperature, warp correction, pitch balance with heating, slab unit weight, and the like can be given. In the “finish rolling process”, rolling speed, scheduling time, turn time, CR temperature, temperature waiting, work roll replacement, warpage correction, pitch balance with finishing, slab unit weight, and the like can be mentioned. In the “shearing process”, cutting speed, cutting ability, conveying ability, cutting accuracy and the like can be mentioned.

  In the present embodiment, the processing efficiency of each process, that is, parameters that affect how much processing can be performed in a unit time (number of passes, rolling temperature, temperature waiting time, etc.) are extracted, and these Are classified into product groups having significant differences, and the processing efficiency is statistically calculated for each product group. For example, classification is performed for each parameter such that the average of the parameters for each product group differs by 10%, and the processing efficiency is calculated. Then, by comparing the processing efficiency of each process for each product group, it is possible to identify the rate-limiting (neck) process in a plurality of processes in series, direct connection, and multiple stages for each product group, and to obtain the inherent consistency efficiency for each product group. Ask.

  More specifically, parameters that affect the processing efficiency of each process are first extracted. In the present embodiment, as shown in the vertical item of FIG. 2, parameters affecting the processing efficiency of the “slab yard process” include HCR ratio, cutting temperature, width length replacement rate, pitch, T / H (Ton / hour), off T / H, pitch (+ off), and T / H (+ off). Further, heating conditions, management time, in-furnace time, pitch, and T / H are extracted as parameters that affect the processing efficiency of the “heating step”. Further, as parameters affecting the processing efficiency of the “rough rolling process”, the tenacity ratio, the number of thickness adjusting passes, the number of tentering passes, the number of transfer passes, the number of passes, the interval, the rolling time, the total TIM (Time in Metal: Biting time), total TOM (Time off Metal: time between passes), turn (turns 1 and 2), each pass TIM, each pass TOM, pitch (interval + rolling time), and T / H are extracted. Parameters affecting the processing efficiency of the “finish rolling process” include transfer thickness, biting temperature, finishing temperature, number of passes, interval, rolling time, total TIM, total TOM, each pass TIM, each pass TOM, pitch (Interval + rolling time), T / H is extracted. In addition, start waiting time, cooling time, pitch, and T / H are extracted as parameters that affect the processing efficiency of the “accelerated cooling process”. In addition, as parameters affecting the processing efficiency of the “shearing process”, earring / cutting, shear rate, aligning, interval, pitch, and T / H are extracted.

  Moreover, it classify | categorizes into the product group in which a parameter has a significant difference. In this embodiment, it classifies in the order of steel type, HCR (Hot Charged Rolling) or CCR (Cold Charged Rolling), ear-attached or cut, and further subdivides by size. . The steel types are classified into ordinary steel, CR material, and CLC material. In addition, in CR material and CLC material, you may make it further classify | categorize according to intensity | strength and use (shipbuilding, construction, a bridge, a motor vehicle ... etc.). Each steel type is classified as HCR (Hot Charged Rolling) or CCR (Cold Charged Rolling), and further classified as ear-attached or cut. Then, subdivide and classify by size. FIG. 2 is a diagram showing the results of statistical calculation of parameters after subdividing the size of ordinary steel (CCR, with ears). The horizontal item in FIG. 2 represents the size. In this embodiment, it classify | categorizes into slab thickness, classifies into each plate thickness which is several target thickness in each slab thickness, and classify | categorizes into several rolling width further. The average value and standard deviation of parameters and processing efficiency are calculated for each product group classified. If it is determined that the standard deviation of the parameters of the classified product group is not significantly smaller than the overall standard deviation, the classification is corrected by means such as adjusting the classification threshold.

  In FIG. 2, “rolling thickness, width, length” and “slab thickness, width, length, single weight” above the vertical items are average values for products handled in the target rolling line. is there. For example, in a class of slab thickness <1500, plate thickness <8, and rolling width <2000, the average thickness of rolling 6.4 (mm), average width 1724 (mm), average length 33237 ( mm), an average slab thickness of 144 (mm), an average width of 1169 (mm), an average length of 2345 (mm), and a single weight of 3.08 (T).

  As described above, parameters that affect the processing efficiency of each process are extracted, and if the parameters are classified into product groups, the parameters are statistically calculated for each product group. That is, as shown in FIG. 2, the numerical value of each column is statistically calculated based on the facility capacity in each process. In FIG. 2, only some sizes of ordinary steel (CCR, with ears) are illustrated, but all product groups, that is, each size of ordinary steel (CCR, with cutting), ordinary steel (HCR, with ears) , Each size of ordinary steel (HCR, cutting), each size of CR material (CCR, with ears), each size of CR material (CCR, cutting), each size of CR material (HCR, with ears), Each size of CR material (HCR, cutting), each size of CLC material (CCR, with ears), each size of CLC material (CCR, cutting), each size of CLC material (HCR, with ears), CLC material (HCR) Similarly, the parameters are statistically calculated for each size.

  Then, by comparing the processing efficiency of each process for each product group, a rate-limiting process among a plurality of processes in series, direct connection, and multiple stages is specified for each product group, and a unique consistent efficiency for each product group is obtained. In the example of FIG. 2, that is, a product group of slab thickness <1500, sheet thickness <8, and rolling width <2000 of ordinary steel (CCR, with ears), when the slab yard off is included, processing of “rough rolling process” Since the efficiency T / H is 112 and the minimum, the “rough rolling process” is the rate-limiting process, and the inherent consistency efficiency is 112 (T / H). This is because when only a product group of the class of plain steel (CCR, with ears) slab thickness <1500, sheet thickness <8, rolling width <2000 is included to include slab yard off, It means that it can be produced at an efficiency of 112 (T / H) by an integrated process from “to the shearing step”. When only the slab yard is on, the T / H, which is the processing efficiency of the “slab yard process”, is the smallest 106, so the “slab yard process” is the rate-limiting process, and the inherent consistency efficiency is 106 (T / H).

  As shown in FIG. 3, in the rolling line targeted in the present embodiment, the “rough rolling process” is often the rate-limiting process in ordinary steel, while the “finishing process” is the rate-limiting process in the CR material. There were many cases.

Once the inherent efficiency of each product group is determined as described above, the overall efficiency of the entire manufacturing process can be determined using the inherent efficiency of each product group according to the composition ratio of the product group in the period to be predicted. Calculate and predict consistent efficiency. For example, assuming that the product group is classified into 1 to N, X _n (T) (n is a number from 1 to N representing the product group) is manufactured for the product group n in a period to be predicted. If the inherent consistency efficiency of product group n is Y _n (T / H), the consistency efficiency of the entire manufacturing process is
Manufacturing process consistent efficiency = ΣX _n / Σ (X _n / Y _n )
Is calculated as Note that Σ means the sum of n = 1 to N.

  To explain with a simple example, for example, in one week from the present time, a product group of slab thickness <1500, sheet thickness <8, rolling width <2000 of ordinary steel (CCR, with ears) (hereinafter referred to as product group A) 50 (T), and slab thickness <1500 for normal steel (CCR, with ears), plate thickness <15, and a product group of the class of rolling width <3000 (hereinafter referred to as product group B) is 100 (T). Suppose you plan to manufacture each slab yard off. Since the inherent consistency efficiency of the product group A is 112 (T / H) and the inherent consistency efficiency of the product group B is 151 (T / H), the consistency efficiency of the entire manufacturing process is (50 (T) +100 ( T)) / {(50 (T) / 112 (T / H)) + (100 (T) / 151 (T / H))} = approximately 135 (T / H).

  Here, the actual value of the consistent efficiency observed in actual production is generally different from the inherent efficiency of each product group obtained as described above. This is because, in actual production, since a plurality of types of product groups are combined and manufactured, the rate-limiting process changes sequentially, and a rate-limiting situation different from the inherent rate-limiting process of the product group frequently occurs. Therefore, the actual value of consistent efficiency observed in actual production is different from the inherent consistent efficiency when the same product group is manufactured continuously, that is, it includes a combination of production permutations that include errors and fluctuate over time. In order to be affected, it will inevitably fluctuate from period to period when results are tabulated. For this reason, when calculating the consistent efficiency for each product group for a certain period and calculating the consistent efficiency of the entire manufacturing process according to the composition ratio of the product group, which varies from period to period, an error from the inherent consistent efficiency and It is understood that high-precision prediction is difficult due to fluctuations in production permutation.

  Therefore, the present inventor quantitatively scrutinized the effects of the rate-determining process for each product group on the inherent consistency efficiency for each product group and the actual efficiency value observed in actual production, and repeated investigations. In addition to properly extracting parameters that affect the processing efficiency of each process and properly defining product groups that have significant differences in these parameters, the inherent consistency efficiency of each product group can be calculated with high accuracy. When comparing the inherent efficiency of each product group calculated in this way with the actual value of the consistency efficiency of each product group observed in actual production, the latter Variation in a certain range, that is, in the actual production, when one product group is manufactured in combination with another product group, an error / variation occurs based on the inherent consistency efficiency. Furthermore, in the entire manufacturing process, regardless of the production permutation, the error in the actual value of the consistent efficiency and the inherent consistency error of the product group are offset / cancelled, and the consistent efficiency of the entire manufacturing process is Using consistent efficiency, we found that it was possible to calculate with high accuracy and simplicity according to the composition ratio of the product group.

  As described above, parameters that affect the processing efficiency of each process are appropriately extracted in a manufacturing process that combines multiple types of product groups with series, direct connection, and multiple stages with different processing capabilities. At the same time, by properly defining and classifying product groups with significant differences in these parameters and obtaining unique consistency efficiency for each product group, the consistency efficiency of the entire manufacturing process according to the composition ratio of the product group Can be calculated and predicted with high accuracy and simplicity.

(Production process efficiency prediction device)
Hereinafter, an apparatus for calculating and predicting the consistent efficiency of the entire manufacturing process using the consistent efficiency specific to each product group as described above will be described. FIG. 4 shows a configuration of the efficiency prediction apparatus 100 for the manufacturing process according to the embodiment.

  Reference numeral 101 denotes a storage unit that extracts parameters that affect the processing efficiency of each process as described above, classifies the product into product groups, and controls the rate in the series, direct connection, and multistage processes for each product group. The unique consistency efficiency for each product group obtained by specifying the process is stored. When there is a change such as an increase in equipment capacity in each process, the inherent consistency efficiency for each product group is obtained again, and the inherent consistency efficiency for each product group stored in the storage unit 101 is updated.

  Reference numeral 102 denotes an input unit for inputting information on a product group in a period to be predicted. In the example described above, for example, in one week from the present time, product group A is manufactured as 50 (T) and product group B as 100 (T) so as to include the slab yard off. input.

  Reference numeral 103 denotes an arithmetic unit, which uses the consistent efficiency specific to each product group stored in the storage unit 101 and uses the consistency of the entire manufacturing process according to the composition ratio obtained from the product group information input from the input unit 102. Calculate and predict efficiency. In the above example, the inherent consistency efficiency of product group A is 112 (T / H), the inherent consistency efficiency of product group B is 151 (T / H), and the consistency efficiency of the entire manufacturing process is ( 50 (T) +100 (T)) / {(50 (T) / 112 (T / H)) + (100 (T) / 151 (T / H))} = approximately 135 (T / H) The

  Reference numeral 104 denotes an output unit that displays a consistent efficiency of the entire manufacturing process, which is a calculation result of the calculation unit 103, on a display device (not shown). The operator refers to the consistent efficiency of the entire manufacturing process and adjusts the throughput and operating rate of the front and rear processes (adjusts the amount of steel slabs received from the steelmaking process, which is the previous process, Adjustment of the operation rate / processing capacity of each process, etc.) and, if necessary, measures are taken to increase the facility capacity of the process that becomes a bottleneck.

  The manufacturing process efficiency prediction apparatus of the present invention can be specifically configured by a computer system including a CPU, a ROM, a RAM, and the like, and is realized by the CPU executing a program. The manufacturing process efficiency prediction apparatus of the present invention may be composed of a single device or a plurality of devices.

  The object of the present invention can also be achieved by supplying a storage medium storing a program code of software that realizes the efficiency prediction function of the manufacturing process described above to a system or apparatus. In this case, the program code itself read from the storage medium realizes the functions of the above-described embodiments, and the program code itself and the storage medium storing the program code constitute the present invention. As a storage medium for supplying the program code, for example, a flexible disk, a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, a CD-R, a magnetic tape, a nonvolatile memory card, a ROM, or the like can be used.

  101: storage unit, 102: input unit, 103: calculation unit, 104: output unit

Claims (6)

A method for predicting the efficiency of a manufacturing process that has a plurality of processes in series, direct connection, and multiple stages with different processing capacities, and is manufactured by combining a plurality of types of product groups,
In addition to extracting parameters that affect the processing efficiency of each process, classify them into product groups, identify the rate-limiting process among the series, direct connection, and multiple stages in each product group, The steps to find consistency efficiency,
A method for predicting the efficiency of a manufacturing process, comprising: calculating a consistent efficiency of the entire manufacturing process according to a composition ratio of the product group using a consistent efficiency specific to each product group.   The manufacturing process includes a slab yard process, a heating process for reheating the slab, a rough rolling process in which the reheated slab is widened to a predetermined width, and a steel slab after the widening has a predetermined thickness and length. Finished rolling process that builds up the material by controlled rolling that rolls at a predetermined temperature, cooled steel slab after finishing rolling with a large amount of cooling water, accelerated cooling process that builds a quenched structure, after rolling and cooling A plurality of steps such as a shearing step of shearing a steel piece into a plurality of steel plates having a predetermined width and length are rolling processes for thick plates arranged in series, directly connected, and in multiple stages. A method for predicting the efficiency of manufacturing processes.   The product group is classified in the order of steel grade, HCR (Hot Charged Rolling) or CCR (Cold Charged Rolling), with ears or with cutting, and further divided into sizes. The method for predicting the efficiency of a manufacturing process according to claim 2, wherein:   The method according to claim 3, wherein the sizes are classified in the order of slab thickness, target thickness, and rolling width. A process for predicting the efficiency of a manufacturing process having a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and combining a plurality of types of product groups,
For each product group, parameters that affect the processing efficiency of each process are extracted and classified into product groups, and the rate-limiting process in the series, direct connection, and multiple stages is specified for each product group. Storage means for storing the inherent consistency efficiency of
A manufacturing process efficiency predicting apparatus, comprising: a calculation means for calculating a consistent efficiency of the entire manufacturing process according to a composition ratio of the product group using a consistent efficiency specific to each product group. A program for predicting the efficiency of a manufacturing process that has a plurality of processes of series, direct connection, and multiple stages with different processing capacities, and that combines a plurality of types of product groups.
For each product group, parameters that affect the processing efficiency of each process are extracted and classified into product groups, and the rate-limiting process in the series, direct connection, and multiple stages is specified for each product group. Storage means for storing the inherent consistency efficiency of
A program for causing a computer to function as a calculation means for calculating a consistent efficiency of the entire manufacturing process according to a composition ratio of a product group using a consistent efficiency specific to each product group.

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