Rolling line operation assisting method, operation assisting device, and operation assisting program

Abstract

A method of assisting an operation in a rolling line includes a step of acquiring various data related to a rolled product manufactured in the rolling line. The method includes a step of calculating, based on the various data, an occurrence rate of an error occurring in at least one of a quality of the rolled product, an operation state of the rolling line, and the operator operation of an actuator of the rolling line, for each product specification, and a step of setting, according to a level of the occurrence rate, a manufacturing difficulty of the rolled product for each product specification. The method includes a step of outputting, from an operator terminal, assistance data for assisting an operator participating in a manufacturing process of a manufacturing object manufactured in the rolling line, the assistance data including data of the manufacturing difficulty of the rolled product having the same product specification as a product specification of the manufacturing object as data of the manufacturing difficulty of the manufacturing object.

Description

Technical Field

[0001] This disclosure relates to methods, apparatus, and procedures for assisting operations in a rolling mill line. Background Technology

[0002] Patent Document 1 discloses an apparatus for generating a logistics schedule that includes the charging of steel billets into a heating furnace and the rolling of materials in a hot rolling mill. This conventional apparatus optimizes the logistics schedule over a period of approximately one day to one week by performing real-time simulations using information related to the properties and delivery dates of the steel subject to the logistics schedule to predict future logistics conditions. Furthermore, based on this future logistics condition, a batch production schedule is created by combining the logistics schedules for batches of cold-rolled steel sheets and batches of hot-rolled steel sheets.

[0003] In the past, when creating batch schedules, conventional equipment prioritized the acquisition of cold-rolled steel sheets. Patent Document 1 explains that this was due to a trend where the requirements for cold-rolled steel sheet production were higher than those for hot-rolled steel sheet production, and that rolling cold-rolled steel sheet was more difficult and prone to missing rolls than rolling hot-rolled steel sheet.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2005-59020 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] The ease or difficulty of rolling steel varies depending on the properties of the steel, or in other words, on the specifications of the products manufactured on the rolling line—a fact known empirically or statistically. Therefore, considering the objects to be manufactured on the rolling line, it can be anticipated that when the difficulty of manufacturing objects is high, the frequency of operator involvement in the manufacturing process will increase. Conversely, even if the frequency of involvement is lower, the timing and extent of involvement will become important.

[0009] However, issues such as what kind of information should be provided to operators related to their participation in the manufacturing process, and in what manner should this information be provided to operators, have not been studied in the prior art, exemplified by Patent Document 1. Therefore, technological development based on this perspective is desired.

[0010] One object of this disclosure is to provide technology that can provide information useful to operators involved in the manufacturing process of manufactured objects in a rolling line.

[0011] Methods for solving problems

[0012] The first point of this disclosure is a method for assisting operations in a rolling line, and has the following characteristics.

[0013] The method includes the following steps:

[0014] The step of obtaining various data related to the rolled products manufactured in the rolling line, including data related to the product specifications of the rolled products, data related to the quality of the rolled products, data related to the operating status of the rolling line during the manufacture of the rolled products, and data related to operator operations on the actuators of the rolling line during the manufacture of the rolled products.

[0015] Based on the various data, the steps include calculating the error rate of at least one of the following for each product specification: the quality of the rolled product, the operating status of the rolling line, and the operation of the operator.

[0016] The step of determining the manufacturing difficulty of the rolled product according to the occurrence rate and for each product specification; and

[0017] The step of outputting auxiliary data from the operator terminal, which is used to assist the operator in the manufacturing process of the manufactured object in the rolling line, the auxiliary data including data on the manufacturing difficulty of the rolled product having the same product specifications as the manufactured object, as data on the manufacturing difficulty of the manufactured object.

[0018] The second aspect of this disclosure is an apparatus for assisting operations in a rolling line, and has the following features.

[0019] The apparatus includes a storage device for storing various data related to the rolled products manufactured in the rolling line; and one or more processors.

[0020] The various data include data related to the product specifications of the rolled product, data related to the quality of the rolled product, data related to the operating status of the rolling line during the manufacturing of the rolled product, and data related to the operator operations performed on the actuators of the rolling line during the manufacturing of the rolled product.

[0021] The one or more processors are configured to perform the following processing:

[0022] Based on the various data, the processing involves calculating the error rate of at least one of the following factors for each product specification: the quality of the rolled product, the operating status of the rolling line, and the operation of the operator.

[0023] Based on the occurrence rate, the processing of setting the manufacturing difficulty of the rolled product according to each product specification; and

[0024] Processing of auxiliary data output from operator terminals, which is used to assist operators in the manufacturing process of manufactured objects in the rolling line, the auxiliary data including data on the ease of manufacturing of the rolled product having the same product specifications as the manufactured object.

[0025] The third aspect of this disclosure is a procedure for assisting operations in a rolling line, and has the following characteristics.

[0026] The program causes the computer to perform the following processes:

[0027] Processing of acquiring various data related to the rolled products manufactured in the rolling line, including data related to the product specifications of the rolled products, data related to the quality of the rolled products, data related to the operating status of the rolling line during the manufacturing of the rolled products, and data related to operator operations on the actuators of the rolling line during the manufacturing of the rolled products.

[0028] Based on the various data, the processing involves calculating the error rate of at least one of the following factors for each product specification: the quality of the rolled product, the operating status of the rolling line, and the operation of the operator.

[0029] Based on the occurrence rate, a processing method is set for the manufacturing difficulty of the rolled product corresponding to the occurrence rate for each product specification; and

[0030] Processing of auxiliary data output from operator terminals, which is used to monitor operators involved in the manufacturing process of manufactured objects in the rolling line, the auxiliary data including data on the ease of manufacturing of rolled products having the same product specifications as the manufactured objects.

[0031] Invention Effects

[0032] According to this disclosure, the ease of manufacturing a rolled product produced in a rolling line is set according to each product specification, based on the rate of error occurrence in at least one of the following: the quality of the rolled product, the operating status of the rolling line during the production of the rolled product, and the operator's operation of the actuator during the production of the rolled product. Furthermore, according to this disclosure, as auxiliary data for assisting operators participating in the manufacturing process of a manufactured object produced in the rolling line, data on the ease of manufacturing of a rolled product having the same product specification as the manufactured object is output from an operator terminal.

[0033] Here, "rolled products manufactured in a rolling line" refers to products that have been completed in the rolling line. Therefore, the manufacturing difficulty of "rolled products manufactured in a rolling line" is clear. On the other hand, "manufacturing objects manufactured in a rolling line" refers to products that are not yet completed in the rolling line, products that are about to be manufactured in the rolling line, and products that are being manufactured in progress in the rolling line. Therefore, the manufacturing difficulty of "manufacturing objects manufactured in a rolling line" is not yet clear.

[0034] However, if the product specifications of the "manufactured object manufactured in the rolling line" are the same as the product specifications of the "rolled product manufactured in the rolling line," it is possible to predict that the manufacturing difficulty of the former is the same as that of the latter. This disclosure addresses this point. Therefore, the manufacturing difficulty of the "rolled product manufactured in the rolling line," which has the same product specifications as the "manufactured object manufactured in the rolling line," is output from the operator's terminal as the aforementioned auxiliary data. According to this disclosure as described above, useful information regarding the manufacturing difficulty of the "manufactured object manufactured in the rolling line" can be provided to the operator. Attached Figure Description

[0035] Figure 1 This is a diagram illustrating an example of the configuration of a rolling system using the operation assistance device of the rolling line according to the first embodiment.

[0036] Figure 2 This is a diagram illustrating an example of the structure of data related to product specifications.

[0037] Figure 3 This is a diagram illustrating an example of the composition of data related to the operational status.

[0038] Figure 4 It is a graph that illustrates data related to operator actions.

[0039] Figure 5 This is a diagram illustrating an example of the composition of data related to quality.

[0040] Figure 6This is a diagram illustrating an example of data processing performed by a management server (processor) in the first embodiment.

[0041] Figure 7 This is a diagram illustrating an example of data processing performed by a management server (processor) in the first embodiment.

[0042] Figure 8 This is a diagram illustrating an example of the configuration of a rolling system using the operation assistance device of the rolling line according to the second embodiment.

[0043] Figure 9 This is a diagram illustrating an example of data processing performed through a management server (processor) in the second implementation method.

[0044] Figure 10 This is a diagram illustrating an example of data processing performed through a management server (processor) in the second implementation method.

[0045] Figure 11 This is a diagram illustrating an example of data processing performed through a management server (processor) in the second implementation method. Detailed Implementation

[0046] Hereinafter, the operation assistance method, operation assistance device, and operation assistance procedure of the rolling line according to the embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Furthermore, common elements in all figures will be labeled with the same reference numerals, and repeated descriptions will be omitted.

[0047] 1. First Implementation Method

[0048] 1-1. Example of a rolling system configuration

[0049] Figure 1 This is a diagram illustrating an example of the configuration of a rolling system using the auxiliary equipment of the rolling line according to the first embodiment. Figure 1 In the example shown, the rolling system includes a management server 1, a PLC (Programmable Logic Controller) 2, and an operator terminal 3.

[0050] The management server 1 functions as an operation assistance device in the first embodiment. The management server 1 communicates with the PLC 2 and the operator terminal 3 via a communication network. The communication network is not particularly limited and can use wired or wireless networks. Examples of communication networks include the Internet, WWW, telephone lines, LAN (Local Area Network), SAN (Storage Area Network), and DTN (Delay Tolerant Network). Examples of wireless communication include Wi-Fi (Wireless Fidelity) and Bluetooth (registered trademark).

[0051] The management server 1 includes a data processing unit 11 and a database 12. The data processing unit 11 includes at least one processor 13 and at least one memory 14. The processor 13 includes a CPU (Central Processing Unit). The memory 14 is a volatile memory such as DDR memory, used for executing various programs used by the processor 13 and temporarily storing various data. Among the various programs used by the processor 13 is a job assistance program according to the first embodiment. Among the various data used by the processor 13 is various data stored in the database 12.

[0052] The various data stored in database 12 include data related to the Product Specifications (SPCs) of multiple rolled products (RPs) manufactured in the rolling line RL. These data also include data related to the Operation Status (OPS) of the rolling line RL during the manufacture of these rolled products (RPs), data related to the Operator Manipulation (OPM) of the actuators on the rolling line RL during the manufacture of these rolled products (RPs), and data related to the Quality Level (QLT) of these rolled products (RPs).

[0053] Data related to product specifications (SPC) is contained in the manufacturing instruction information (rolling command information) of PLC 2. Data related to operation status (OPS) is obtained by PLC 2 from the rolling line RL. This data is updated periodically via communication with PLC 2. Data related to operator operation (OPM) is updated periodically via communication with operator terminal 3. Furthermore, since data related to operator operation (OPM) is input to the actuators of the rolling line RL, this data can also be provided to management server 1 via the rolling line RL (PLC 2).

[0054] The data related to Quality Level Tolerance (QLT) consists of data on various qualities of multiple rolled product resistives (RPs), such as surface quality and dimensional quality evaluated based on plate thickness and width. The QLT-related data is updated periodically by the management server 1, which retrieves inspection data from these rolled product resistives after manufacturing.

[0055] Figure 2 This is a diagram illustrating an example of the structure of data related to Product Specifications (SPC). In Figure 2 In the example shown, the product specification data (SP1 to SPx) of multiple rolled products RP are combined with the batch number data (LT1 to LT4). The batch number is set for every 100 to 200 rolled products RP. Figure 2 The example shown illustrates the data structure for focusing on a single day's rolling history, during which four batches of rolling were performed. Furthermore, the period for focusing on the rolling history is not specifically limited; for example, it can focus on a week's rolling history or a month's rolling history.

[0056] Product specifications are divided into x groups (x > i > 2). Product specifications SP1 to SPx are determined, for example, by a combination of material differentiation (AA to YY) and size differentiation (SZ1 to SZz). The material differentiation of AA to YY is determined, for example, by referring to JIS or DIN standards. The size differentiation of SZ1 to SZz (z > j > 2) is determined, for example, by a combination of the thickness and width of the rolled product RP.

[0057] In addition, Figure 2 In the example shown, although product specifications are determined by a combination of material and size distinctions, this combination can also be further combined with other elements such as temperature distinctions and rolling method distinctions.

[0058] Figure 3 This is a diagram illustrating an example of the structure of data related to the Operation Status (OPS). Figure 3 In the example shown, data on operational error items (OPS_ER1~OPS_ERp) and their occurrence counts for multiple rolled product RPs are combined with batch number and product specification data. The history period of the data related to the operational status OPS is... Figure 2 The data related to the product specifications SPC described herein are recorded over the same period (e.g., one day). The operational error items OPS_ER1 to OPS_ERp (p>2) are set corresponding to the phenomena that cause the normal operation to stop the rolling line RL. Examples of such phenomena include material breakage during rolling, bite failure, and tail-end shrinkage.

[0059] Figure 4This is a diagram illustrating data related to operator actions (OPM). The operator (OP) responsible for the manufacturing process in the rolling mill (RL) monitors the HMI (Host Interface) at operator terminal 3 (e.g., ...). Figure 1 The display groups DPG1 and DPG2 shown output various information. Furthermore, when it is determined that participation in the manufacturing process is necessary, the operator (OP) operates the input device of the operator terminal 3, sending various operating commands to the actuators. Examples of actuators mentioned here include hydraulic cylinders for controlling roller gaps, drive motors for controlling roller speeds, and water pumps for controlling cooling water.

[0060] Figure 4 An example of the progression of time-series data (ACT) of operation instructions sent from operator terminal 3 to actuator is shown. Data related to operator operation (OPM) is generated based on the results of analysis and processing of this time-series data. Furthermore, this analysis and processing, as well as the generation of data related to operator operation (OPM), are performed by management server 1 (e.g., processor 13).

[0061] When operators (OPs) are involved in the manufacturing process, the data ACT changes. If the ACT data changes frequently during the manufacturing of a certain rolled product RP, the OP can identify certain risks affecting the manufacturing process of that rolled product RP, or infer that certain failures have occurred during manufacturing. Furthermore, the identification of such risks and the occurrence of failures can also be inferred when the rate of change of the ACT data, dACT / dt, is large.

[0062] In the analysis and processing of ACT data, for example, if the number of changes in ACT data exceeds the upper limit, data indicating an error (hereinafter also referred to as "error data") is assigned. In another example, error data is assigned when the rate of change dACT / dt exceeds the upper limit. The assignment of error data is based on a combination of the batch number of the rolled product RP and the product specification data. Error data can also be data that identifies the type of actuator. If multiple actuators of the same type exist, error data can also be data that identifies the actuator.

[0063] Figure 5 This is a diagram illustrating an example of the composition of data related to Quality Level (QLT). In Figure 5 In the example shown, data on quality error items (QLT_ER1 to QLT_ERq) and their occurrence (yes: 0, no: 1) for multiple rolled products RP are combined with data on batch numbers and product specifications. The history period of the data related to quality QLT is... Figure 2The data related to the product specifications SPC described herein are recorded over the same period (e.g., one day). Quality error items from QLT_ER1 to QLT_ERq (q>2) are set corresponding to pre-defined quality benchmarks. Examples of quality benchmarks include those related to surface quality and those related to dimensional quality evaluated based on plate thickness and width.

[0064] Various data stored in database 12 are processed by processor 13. This processing includes (i) calculation of the error rate α in the rolled product RP, (ii) setting of the manufacturing difficulty DLv of the rolled product RP, and (iii) sending of the manufacturing difficulty DLv.

[0065] (i) The calculation of the occurrence rate α is performed for at least one of the following: errors occurring in the operating conditions of the rolled product RP, errors occurring in the operator's operation of the actuator, and errors occurring in the quality of the rolled product RP.

[0066] The error rate α_OPS in the work status can be represented by the ratio of the number of work error items N_Eops in the data related to work status OPS to the number of rolled products RP manufactured N_RP. The error rate α_OPM in operator operation can be represented by the ratio of the number of error data assigned to the data related to operator operation OPM N_Eopm to the number of rolled products RP manufactured N_RP. The error rate α_QLT in quality can be represented by the ratio of the number of quality error items N_Eqlt in the data related to quality QLT to the number of rolled products RP manufactured N_RP. Furthermore, the number of manufactured products N_RP can be obtained from manufacturing instruction information.

[0067] The occurrence rates α_OPS, α_OPM, and α_QLT are calculated for each product specification SPC of the rolled product RP. Hereinafter, the occurrence rate α_OPS calculated for each product specification SPC of the rolled product RP will be referred to as "occurrence rate α_OPS(SP)". Furthermore, the occurrence rate α_OPM calculated for each product specification SPC of the rolled product RP will be referred to as "occurrence rate α_OPM(SP)", and the occurrence rate α_QLT calculated for each product specification SPC of the rolled product RP will be referred to as "occurrence rate α_QLT(SP)". In the absence of certain occurrence rates α_OPS(SP), α_OPM(SP), and α_QLT(SP), they will be collectively referred to as "occurrence rate α(SP)".

[0068] (ii) The setting process for manufacturing difficulty DLv is based on at least one of the occurrence rates α_OPS(SP), α_OPM(SP), and α_QLT(SP) calculated in the occurrence rate α calculation process (i). Similar to the occurrence rate α(SP), manufacturing difficulty DLV is calculated per product specification SPC of the rolled product RP. Hereinafter, the manufacturing difficulty DLV calculated per product specification SPC of the rolled product RP will be referred to as "manufacturing difficulty DLV(SP)".

[0069] The manufacturing difficulty level DLV(SP) can be set by dividing the occurrence rate α(SP) into two or more stages based on one or more pre-defined boundary values, such as "DLV: LV1", "DLv: LV2", "DLv: LV3", etc., according to the numerical range to which the occurrence rate α(SP) belongs. Alternatively, the numerical value of the occurrence rate α(SP) can be set as the manufacturing difficulty level DLv(SP).

[0070] In the case where more than two occurrence rates α(SP) are calculated in the calculation of occurrence rate α, the following formula (1) containing the weight coefficient β corresponding to each occurrence rate α(SP) can also be used to calculate the "comprehensive" manufacturing difficulty (hereinafter also referred to as "comprehensive difficulty") CDLv(SP).

[0071] CDLv(SP)=Σα_OPS(SP)×βops+α_OPM(SP)×βopm+α_QLT(SP)×βqlt···(1)

[0072] Furthermore, the weighting coefficient β represents the relative weight of two or more occurrence rates α(SP) used in the calculation of the overall difficulty CDLv, and is preset based on the calculation elements (i.e., error elements) of these occurrence rates α(SP). Specifically, the weighting coefficient βops for errors in the work state can also be measured when the occurrence of the error causes a delay in the manufacturing process. Moreover, the relative values ​​of the weighting coefficient βops relative to the weighting coefficient βopm for errors in operator operations (OP) and the weighting coefficient βqlt for quality errors can be adjusted according to the length of this delay time. Alternatively, if the production volume decreases due to errors in the work state, the amount of decrease can be measured and the relative value of the weighting coefficient βops adjusted accordingly.

[0073] (iii) The process of sending manufacturing difficulty DLv (or comprehensive difficulty CDLv) is the process of sending the data of manufacturing difficulty DLv set in (ii) manufacturing difficulty DLv setting process to the operator terminal 3.

[0074] return Figure 1The following is a description of the configuration of the rolling system. PLC 2 is a computer that performs various setting calculations for the rolling line RL. PLC 2 communicates with the control devices for various actuators installed on the rolling line RL, and with various sensors installed on the rolling line RL. PLC 2 sends, for example, control commands calculated based on the setting calculations, and operation commands received from the operator terminal 3 to the control device. The control device sends data related to the status of the actuators to PLC 2. Examples of various sensors include a thermometer for measuring the temperature of the intermediate product (hereinafter also referred to as "the rolled material MR"), a thickness gauge for measuring the thickness of the rolled material MR, a load cell for measuring the load on the rolling mill, and a speedometer for measuring the roll speed. Measurements taken by these sensors are sent to PLC 2.

[0075] Operator terminal 3 is a terminal used by the operator (OP) to monitor or participate in the manufacturing process on the rolling line RL. Operator terminal 3 is connected to display groups DPG1 and DPG2. It outputs images, for example, from monitoring cameras installed on the rolling line RL to display group DPG1. It outputs data measured by various sensors on the rolling line RL, as well as data related to actuator operation handled by the operator (OP), to display group DPG2. Although not shown in the diagram, operator terminal 3 is equipped with an input device. The operator (OP) operates this input device when it is determined that participation in the manufacturing process is necessary.

[0076] In the first embodiment, data used to assist the operator (hereinafter also referred to as "Assist Data (ASS)") is output from one of the display groups DPG2. The Assist Data ASS contains data on the manufacturing difficulty DLv (or comprehensive difficulty CDLv) received from the management server 1. The manufacturing difficulty DLv data is the data of the manufactured object TP manufactured in the rolling line RL. The output control of the Assist Data ASS is performed by the processor (not shown) of the operator terminal 3.

[0077] Here, the manufacturing object TP refers to the product in the rolling line RL before its manufacturing is completed, that is, the product to be manufactured in the rolling line RL, and the product in the rolling line RL where manufacturing is in progress. On the other hand, the manufacturing difficulty DLv is set for the rolled product RP, that is, the finished product in the rolling line RL. Therefore, the manufacturing difficulty DLv of the manufacturing object TP is not actually set.

[0078] However, as long as the product specification SPC of the manufacturing target TP is the same as that of the rolled product RP, it can be predicted that the manufacturing difficulty DLv of the former is the same as that of the latter. Therefore, in the embodiment, the manufacturing difficulty DLv data of the rolled product RP, which has the same product specification SPC as the manufacturing target TP, is used as the manufacturing difficulty DLv data of the manufacturing target TP and output from the display group DPG2.

[0079] 1-2. Data Processing Examples

[0080] Next, a data processing example, particularly related to the first embodiment, will be explained. Figure 6 as well as Figure 7 This is a diagram illustrating an example of data processing performed through management server 1 (processor 13). Figure 6 The processing flow shown in 7 is repeated at certain intervals.

[0081] exist Figure 6 In the illustrated routine, firstly, various data are acquired (step S11). These various data are stored in database 12. Examples of these various data include data related to the product specifications (SPC) of multiple rolled products (RPs), data related to the operating status (OPS) of the rolling line RL during the manufacture of these rolled products (RPs), data related to the operator operations (OPM) of the actuators on the rolling line RL during the manufacture of these rolled products (RPs), and data related to the quality (QLT) of these rolled products (RPs).

[0082] Following step S11, the error occurrence rate α(SP) is calculated (step S12). The calculation of the occurrence rate α(SP) is performed on the data obtained in step S11. Specifically, when data related to the work status OPS is obtained, the occurrence rate α_OPS(SP) is calculated. When data related to the operator's operation OPM is obtained, the occurrence rate α_OPM(SP) is calculated. When data related to the quality level (QLT) is obtained, the occurrence rate α_QLT(SP) is calculated.

[0083] Following step S12, the manufacturing difficulty DLv(SP) is calculated (step S13). The calculation of manufacturing difficulty DLv(SP) is performed, for example, based on the occurrence rate α(SP) calculated in step S12, according to each calculation element (i.e., error element) of the occurrence rate α(SP). When calculating the comprehensive difficulty CDLv(SP), the occurrence rate α(SP) calculated in step S12 is applied to the above equation (1). In addition, the manufacturing difficulty DLv(SP) or comprehensive difficulty CDLv(SP) data obtained through step S13 is stored in database 12.

[0084] exist Figure 7 In the example shown, firstly, it is determined whether the manufacturing of the object TP is scheduled (step S21). The processing of step S21 is performed, for example, by referring to the schedule data contained in the manufacturing instruction information held by PLC 2. The reference range for the schedule data is, for example, the operating day of the rolling line RL. In another example, the schedule data for the next manufacturing batch of the currently manufactured batch is referenced.

[0085] Following step S21, the product specification SPC data for the manufacturing object TP is determined (step S22). Step S22 is performed, for example, with reference to the manufacturing instruction information held by PLC 2.

[0086] Following step S22, the manufacturing difficulty DLv(SP) of the manufacturing object TP is determined (step S23). In step S23, for example, the product specification SPC of the manufacturing object TP determined in step S22 is used as a reference to database 12. Thus, the manufacturing difficulty DLv(SP) and / or the combined difficulty CDLv(SP) of the rolled product RP having the same product specification SPC as the manufacturing object TP are determined.

[0087] Following step S23, the manufacturing difficulty DLv(SP) data is sent to operator terminal 3 (step S24). Specifically, in step S24, the manufacturing difficulty DLv(SP) and / or the comprehensive difficulty CDLv(SP) data determined in step S23 are sent to operator terminal 3. When sending the data, the calculation element (i.e., error element) data of the occurrence rate α(SP) used in the calculation of manufacturing difficulty DLv(SP) and / or comprehensive difficulty CDLv(SP) may also be sent along with the data.

[0088] The manufacturing difficulty DLv(SP) and / or comprehensive difficulty CDLv(SP) data sent to the operator terminal 3 through the above processing are output from the display group DPG2 as auxiliary data ASS.

[0089] 1-3. Effects

[0090] According to the first embodiment described above, useful information such as manufacturing difficulty DLv(SP) and / or comprehensive difficulty CDLv(SP) can be provided to the operator (OP). Furthermore, by providing data on the calculation elements (i.e., error elements) of the occurrence rate α(SP) used in the calculation of manufacturing difficulty DLv(SP) and / or comprehensive difficulty CDLv(SP), useful information such as error elements that should be noted during the manufacturing process of the manufactured object TP can be provided to the operator (OP).

[0091] 2. Second Implementation Method

[0092] 2-1. Example of a rolling system configuration

[0093] Figure 8 This is a diagram illustrating an example of the configuration of a rolling system using the operation assistance device of the rolling line according to the second embodiment. Figure 8 The example shown is similar to Figure 1 The configuration examples shown are basically the same. The difference between the former and the latter is that the management server 1 has a database 15 in addition to database 12.

[0094] Database 15 stores data related to the State of Material to be Rolled (SMR) of the rolled product RP (more precisely, the material to be rolled) obtained in the rolling line RL during the manufacturing of the rolled product RP. Examples of data related to the State of Material to be Rolled (SMR) include data directly obtained from various sensors installed in the rolling line RL, such as the temperature and thickness of the material to be rolled (SMR), the load applied to the material to be rolled (SMR), and the rolling speed, or data calculated based on the obtained data.

[0095] Database 15 also stores data EX_SMR extracted from data related to the SMR status. EX_SMR data refers to data related to the SMR status when the quality of the rolled product RP, the operating status OPS of the rolling line RL, and the occurrence of errors not identified in the operator's OPM. Examples of errors referred to here are shown in... Figure 3 The error items described in the document (OPS_ER1~OPS_ERp) are in... Figure 4 The description refers to the operator's handling of OPM, which was given incorrect data, and in... Figure 5 The quality error items (QLT_ER1~QLT_ERq) are described in the document.

[0096] Database 15 further stores data EX_OPM extracted from data related to operator operation OPM. Data EX_OPM is data related to operator operation OPM when no errors are confirmed in the quality of the rolled product RP and the operating status OPS of the rolling line RL. Furthermore, the extraction and processing of data EX_OPM and data EX_SMR are performed by management server 1 (e.g., processor 13).

[0097] Various data stored in database 15 are processed by processor 13. This processing includes (i) the generation of data on the typical state of the rolled material MR, (ii) the generation of data on the typical operation of the operator, (iii) the generation of suggestion data for the operator, and (iv) the transmission of suggestion data (ADV).

[0098] (i) The generation process of typical state data is based on data EX_SMR and performed according to each product specification SPC of the rolled product RP. (ii) The generation process of typical operation data is based on data EX_OPM and performed according to each combination of product specification SPC of the rolled product RP and actuator. The generation processes of (i) and (ii) are performed, for example, each time data EX_SMR and EX_OPM are generated.

[0099] (iii) The generation process of the suggested data ADV is performed during the manufacturing of the manufacturing object TP. The generation process of (iii) is, for example, based on data related to the typical state of a rolled product RP having the same product specification SPC as the manufacturing object TP, and data related to the actual state of the manufacturing object TP. The data related to the actual state of the manufacturing object TP refers to the data related to the state of the manufacturing object TP obtained in the rolling line RL during the manufacturing of the manufacturing object TP. In this example, the suggested data ADV is the data of the typical state and the difference between the data of the typical state and the data of the actual state.

[0100] In another example, recommended data (ADV) is generated using data from atypical states of the rolled product RP corresponding to the same product specification SPC as the manufactured object TP. Atypical state data refers to data related to the state SMR when an error is identified in the rolling line RL's operating status (OPS), the operator operation (OPM) during the manufacturing of the rolled product RP, and the quality level (QLT) of the rolled product RP. Atypical state data is generated for each product specification SPC of the rolled product RP. In this example, the recommended data (ADV) includes both typical and atypical state data.

[0101] In another example of the generation process in (iii), recommended data ADV is generated based on typical operation data of operators in the manufacture of a rolled product RP having the same product specification SPC as the manufacturing object TP, and actual operation data of operators in the manufacture of the manufacturing object TP. The actual operation data of operators refers to the data related to operator operation OPM in the manufacture of the manufacturing object TP for the same actuator as the actuator from which the typical operation data was generated. In this example, the recommended data ADV is the typical operation data and the difference between the typical operation data and the actual operation data.

[0102] In another example, recommended data (ADV) is generated using data from atypical operations by operators in the manufacture of a rolled product RP with the same product specification SPC as the manufactured object TP. Atypical operation data refers to operator operation OPM data at the time an error is confirmed to have occurred in the operating status OPS of the rolling line RL during the manufacture of the rolled product RP, and in the quality QLT of that rolled product RP. The generation of atypical operation data is performed for each product specification SPC of the rolled product RP. In this example, the recommended data ADV includes both typical and atypical operation data.

[0103] Furthermore, in another example, data on typical and atypical operations are applied to well-known data analysis methods such as AI and machine learning to generate data on the optimal operation of actuators to avoid errors. In this example, the suggested data (ADV) represents the data for the optimal operation.

[0104] (iv) The ADV sending process is the process of sending the ADV suggested in (iii) the ADV generation process to the operator terminal 3.

[0105] In the first embodiment, manufacturing difficulty DLv data, which is auxiliary data ASS, is output from one of the display groups DPG2. In the second embodiment, suggestion data ADV is output from one of the display groups DPG2 in the form of appending to the manufacturing difficulty DLv data. That is, in the second embodiment, manufacturing difficulty DLv and suggestion data ADV are output as auxiliary data ASS.

[0106] 2-2. Data Processing Example

[0107] Next, a data processing example, particularly related to the second embodiment, will be described. Figures 9-11 This is a diagram illustrating an example of data processing performed in management server 1 (processor 13). Figures 9-11 The processing flow shown is repeated at certain intervals.

[0108] exist Figure 9In the example shown, firstly, various data are obtained (step S31). These various data are data stored in databases 12 and 15. Examples of these various data are shown in... Figure 6 The data described in the instructions for step S11. Among the various data is data related to the state SMR of the rolled product RP obtained in the rolling line RL during the manufacture of the rolled product RP.

[0109] Following step S31, data extraction is performed (step S32). The extracted data referred to here is at least one of data EX_SMR and EX_OPM. Data EX_SMR is generated by extracting data from the SMR-related data obtained in step S31, specifically data concerning the quality of the rolled product RP, the operating status OPS of the rolling line RL, and the operator's operation OPM, from which no errors were detected. Data EX_OPM is generated by extracting data from the OPM-related data obtained in step S31, specifically data concerning the quality of the rolled product RP and the operating status OPS of the rolling line RL, from which no errors were detected.

[0110] Following step S32, typical data is generated (step S33). Step S33 is performed based on the extracted data generated in step S32. If data EX_SMR was generated in step S32, typical state data is generated based on this data. Conversely, if data EX_OPM was generated in step S32, typical operation data is generated based on this data. Furthermore, the typical data obtained through step S33 is stored in database 15.

[0111] exist Figure 10 In the example shown, firstly, it is determined whether to plan the manufacture of object TP (step S41). The content of step S41 is related to... Figure 7 The process in step S21 is the same. Furthermore, following step S41, the product specification SPC data for the manufacturing object TP is determined (step S42). The process in step S42 is the same as... Figure 7 The process in step S22 is the same.

[0112] Following step S42, typical data for the manufacturing object TP is determined (step S43). In step S43, for example, the product specification SPC of the manufacturing object TP determined in step S42 is used as a reference to database 15. Thus, typical data (i.e., typical state and typical operation data) corresponding to the rolled product RP having the same product specification SPC as the manufacturing object TP is determined.

[0113] Following step S43, typical data is sent to operator terminal 3 (step S44). In step S44, the typical data determined in step S43 is sent to operator terminal 3. Atypical data (i.e., data on atypical states and atypical operations) may also be sent along with the typical data.

[0114] The typical data sent to operator terminal 3 after the above processing is output from display group DPG2 as auxiliary data (ASS). When non-typical data is sent to operator terminal 3, the non-typical data is output from display group DPG2 together with the typical data.

[0115] exist Figure 11 In the example shown, firstly, typical data (i.e., data on typical states and typical operations) is obtained (step S51). The typical data obtained in step S51 is data on rolled product RP having the same product specification SPC as the manufacturing object TP manufactured in the rolling line RL, and is read from database 15.

[0116] Following the processing in step S51, actual data (i.e., data on the actual state and actual operation) is obtained (step S52). The actual data obtained in step S52 corresponds to the typical data obtained in the processing in step S51.

[0117] Following step S52, recommended data ADV is generated (step S53). Recommended data ADV is, for example, the difference between typical data obtained in step S52 and the corresponding actual data obtained in step S53. Another example of recommended data ADV is the typical data obtained in step S52 and the corresponding atypical data. Yet another example of recommended data ADV is data on the optimal operation of the actuator to avoid errors.

[0118] Following step S53, suggestion data ADV is sent (step S54). In step S54, the suggestion data ADV generated in step S53 is sent to operator terminal 3.

[0119] The suggestion data ADV sent to operator terminal 3 through the above processing is output from display group DPG2 as auxiliary data ASS.

[0120] 2-3. Effects

[0121] According to the second implementation method described above, useful information such as typical data can be provided to the operator (OP). Furthermore, useful information such as recommended data (ADV) containing the typical data can also be provided to the operator (OP). Therefore, it is expected that errors in the manufacturing of the production object (TP) in the rolling mill (RL) can be avoided in advance.

[0122] Explanation of reference numerals in the attached figures

[0123] 1 Management Server

[0124] 2PLC

[0125] 3. Operator Terminal

[0126] Databases 12 and 15

[0127] 13 processors

[0128] 14. Memory

[0129] OP operators

[0130] RL rolling line

[0131] RP rolled products

[0132] DLv manufacturing difficulty

[0133] OPM operator operation

[0134] OPS Job Status

[0135] QLT Quality

[0136] SMR rolled stock condition

[0137] SPC Product Specifications.

Claims

1. A method for assisting operations in a rolling mill line, characterized in that, Includes the following steps: The step of obtaining various data related to the rolled products manufactured in the rolling line, including data related to the product specifications of the rolled products, data related to the quality of the rolled products, data related to the operating status of the rolling line during the manufacture of the rolled products, and data related to operator operations on the actuators of the rolling line during the manufacture of the rolled products. Based on the various data, the step of calculating at least one of the following error rates for each product specification: the error rate occurring in the quality of the rolled product, the error rate occurring under the operating conditions of the rolling line, and the error rate inferred from the operator's operation; the error rate occurring in the quality of the rolled product is represented by the ratio of the number of quality error items included in the quality-related data to the number of rolled products manufactured; the error rate occurring under the operating conditions of the rolling line is represented by the ratio of the number of operation error items included in the operating conditions to the number of rolled products manufactured; and the error rate inferred from the operator's operation is represented by the ratio of the number of error data indicating that an error occurred included in the operator's operation data to the number of rolled products manufactured. The step of setting the manufacturing difficulty of the rolled product according to each product specification is based on the occurrence rate of errors in the quality of the rolled product, the occurrence rate of errors in the operating conditions of the rolling line, and the occurrence rate of errors inferred from the operator's operation. Based on the incidence rates of at least two of the following: the incidence rate of errors occurring in the quality, the incidence rate of errors occurring in the operating conditions, and the incidence rate of errors inferred from the operator's operation, a step is taken to set a comprehensive difficulty level that represents the overall manufacturing difficulty of the rolled product for each product specification. as well as The step of outputting auxiliary data from the operator terminal, which is used to assist the operator in the manufacturing process of the manufactured object in the rolling line, includes data on the manufacturing difficulty and overall difficulty of the rolled product having the same product specifications as the manufactured object, as data on the manufacturing difficulty of the manufactured object.

2. The operation assistance method for a rolling line as described in claim 1, characterized in that, Also includes: Based on the various data, the steps include extracting data related to the quality of the rolled product, the operating status of the rolling line, and the operator's operation when no error is confirmed from the inference made by the operator's operation, according to each product specification; as well as The steps involve extracting data related to the operator's actions, and generating typical operator operation data for the rolling line according to product specifications and each combination of actuators. In the step of outputting the auxiliary data from the operator terminal, the auxiliary data also includes data on the typical operation, which is generated based on the various data regarding product specifications that are the same as those of the manufactured object.

3. The operation assistance method for a rolling line as described in claim 2, characterized in that, It also includes the step of obtaining data on the actual operations performed by the operators during the manufacturing of the manufactured object. In the step of outputting the auxiliary data from the operator's terminal, the auxiliary data further includes suggestion data for the operator. The recommended data is generated based on the data from the actual operation and the data from the typical operation, which is generated based on the various data regarding product specifications that are the same as those of the manufactured object.

4. The operation assistance method for a rolling line as described in claim 1 or 2, characterized in that, The various data also include data related to the state of intermediate products of the rolled product obtained in the rolling line during the manufacture of the rolled product. The operation assistance method for the rolling line also includes: Based on the various data, the steps include extracting data related to the quality of the rolled product, the operating status of the rolling line, and the status of the intermediate product when no error is confirmed from the inferences made by the operator; as well as The steps involve extracting data related to the state of the intermediate products and generating data on the typical state of the intermediate products during the operation of the rolling line, according to each product specification. In the step of outputting the auxiliary data from the operator terminal, the auxiliary data further includes data on the typical state, which is generated based on the various data regarding product specifications that are the same as those of the manufactured object.

5. The operation assistance method for a rolling line as described in claim 4, characterized in that, It also includes a step of obtaining actual state data, which is data on the actual state of the manufactured object obtained during the manufacturing process on the rolling line. In the step of outputting the auxiliary data from the operator's terminal, the auxiliary data further includes suggestion data for the operator. The recommended data is generated based on the data of the actual state and the data of the typical state, which is generated based on the various data regarding product specifications that are the same as those of the manufactured object.

6. A rolling mill line auxiliary device for assisting operations in the rolling mill line, characterized in that, have: Storage device, storing various data related to the rolled products manufactured in the rolling line; and One or more processors The various data include data related to the product specifications of the rolled product, data related to the quality of the rolled product, data related to the operating status of the rolling line during the manufacturing of the rolled product, and data related to the operator operations performed on the actuators of the rolling line during the manufacturing of the rolled product. The one or more processors are configured to perform the following processing: Based on the aforementioned data, the processing involves calculating at least one of the following error rates for each product specification: the error rate occurring in the quality of the rolled product, the error rate occurring under the operating conditions of the rolling line, and the error rate inferred from the operator's operation. The error rate occurring in the quality of the rolled product is represented by the ratio of the number of quality error items included in the quality-related data to the number of rolled products manufactured. The error rate occurring under the operating conditions of the rolling line is represented by the ratio of the number of operation error items included in the operating conditions to the number of rolled products manufactured. The error rate inferred from the operator's operation is represented by the ratio of the number of error data points indicating that errors occurred included in the operator's operation data to the number of rolled products manufactured. The manufacturing difficulty of the rolled product is determined according to the rate of error occurrence in the quality of the rolled product, the rate of error occurrence in the operating conditions of the rolling line, and the rate of error inferred from the operator's operation, for each product specification. Based on the incidence rates of at least two of the following: the incidence rate of errors occurring in the quality, the incidence rate of errors occurring in the operating conditions, and the incidence rate of errors inferred from the operator's operation, a comprehensive difficulty rating is set for each product specification, representing the overall manufacturing difficulty of the rolled product. as well as Processing of auxiliary data output from operator terminals, which is used to assist operators in the manufacturing process of manufactured objects in the rolling line, the auxiliary data includes data on the manufacturing difficulty and overall difficulty of the rolled product having the same product specifications as the manufactured object.

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