Processing condition estimation device

Abstract

The processing condition estimation device has a processing information database that stores processing information data in which data related to a processing content and data related to a required specification are associated with data related to a processing condition that satisfies the processing content and the required specification. Based on the processing information data stored in the processing information database, a processing condition estimated to satisfy a desired processing content and a required specification is derived, and simulation of the wire electric discharge machine is performed based on the derived processing condition. Based on the result of the execution of the simulation, the processing condition is evaluated, and based on the result of the evaluation, the processing information database is updated.

Description

Technical Field

[0001] This invention relates to a processing condition estimation device, and more particularly to a processing condition estimation device for searching for processing conditions of a wire electrical discharge machining (EDM) machine. Background Technology

[0002] When using a wire electrical discharge machining (EDM) machine, machining conditions (voltage waveform, voltage polarity, current waveform, discharge time, rest time, machining fluid volume, machining hydraulic pressure, wire tension, wire feed control, etc.) are prepared according to the machining content (workpiece material, workpiece thickness, machining shape, steps, surrounding environment, etc.) and required specifications (machining speed, wire breakage frequency, straightness accuracy, surface roughness, shape error, etc.). The manufacturer performs multiple actual machining operations while changing the EDM machine's machining conditions, searching for suitable machining conditions for various combinations of machining content and required specifications, and generating a list of machining conditions. This search for machining conditions requires time from a skilled operator. The user selects machining conditions from the manufacturer's pre-prepared list that meet the desired machining content and required specifications.

[0003] As a prior art related to searching for processing conditions, for example, Patent Document 1 discloses a technique that pre-databases the relationship between set processing conditions and actual processing results, and uses machine learning to predict processing conditions that meet the required specifications based on the generated database. In the technique of Patent Document 1, processing is actually performed under the predicted processing conditions, the database is updated based on the results obtained from those processing conditions, and the next processing conditions to be tried are predicted and repeatedly suggested. Therefore, operators can discover more suitable processing conditions faster than manual execution.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: International Publication WO 2020 / 255370 A1 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] The aforementioned processing content and requirements involve a vast array of combinations. Therefore, it is difficult to find appropriate processing conditions for all combinations of processing content and requirements. Currently, manufacturers limit their search for processing conditions to commonly used processing content and requirements. For special processing content and requirements not included in the processing condition list, manufacturers must compromise on performance degradation by using similar processing conditions within the list, or commission a custom search from the manufacturer. Against this backdrop, manufacturers demand that as many combinations of processing content and requirements as possible be included in the processing condition list. The search for processing conditions requires varying the processing conditions while performing actual processing based on the processing content and requirements. Since manual intervention is required in actual processing and evaluation of results, full automation is also difficult.

[0009] Therefore, a technology is desired that can perform the search for processing conditions with minimal manual effort.

[0010] Methods for solving problems

[0011] The processing condition estimation apparatus of the present invention solves the aforementioned problem by using simulation techniques to evaluate the processing conditions predicted through the search when searching for processing conditions. By using simulation techniques, processing conditions can be evaluated without actual processing, and more data can be obtained in a shorter time compared to performing actual processing.

[0012] Furthermore, one aspect of the present invention is a processing condition estimation device for estimating processing conditions in a wire electrical discharge machining (EDM) machine. This processing condition estimation device comprises: a processing information database storing processing information data related to processing content and required specifications, and processing information data associated with processing conditions that satisfy the processing content and required specifications; a processing condition derivation unit that derives at least one processing condition estimated to satisfy the desired processing content and required specifications based on the processing information data stored in the processing information database; a simulation unit that performs a simulation of the EDM machine based on the processing conditions derived by the processing condition derivation unit; a processing condition evaluation unit that evaluates the processing conditions based on the simulation results performed by the simulation unit; and a database update unit that updates the processing information database based on the evaluation results performed by the processing condition evaluation unit.

[0013] Invention Effects

[0014] According to one aspect of the present invention, when searching for processing conditions that satisfy the processing content and required specifications input from the operator, the processing conditions can be evaluated without actual processing by using simulation techniques to evaluate the processing conditions predicted by the search. Attached Figure Description

[0015] Figure 1 This is a schematic hardware structure diagram of the control device according to the first embodiment.

[0016] Figure 2 This is a schematic block diagram illustrating the function of the control device in the first embodiment.

[0017] Figure 3 This is a diagram representing an example of processing information data stored in a processing information database.

[0018] Figure 4 This is a schematic hardware structure diagram of the control device according to the second embodiment.

[0019] Figure 5 This is a schematic block diagram illustrating the function of the control device in the second embodiment. Detailed Implementation

[0020] The following is related to the appendix. Figure 1 This section describes the embodiments of the present invention.

[0021] Figure 1 This is a schematic hardware structure diagram showing the main parts of the processing condition estimation device according to the first embodiment of the present invention.

[0022] The processing condition estimation device 1 of the present invention can be installed as a control device for the wire electrical discharge machine 8, or as a personal computer arranged in parallel with the control device, or as a computer such as a fog computer 6 or a cloud server 7. In this embodiment, the processing condition estimation device 1 is installed as a computer connected to the wire electrical discharge machine 8 (the control device) via a wired / wireless network 5.

[0023] The CPU 11 of the machining condition estimation device 1 in this embodiment is a processor that controls the machining condition estimation device 1 as a whole. The CPU 11 reads the system program stored in the ROM 12 via the bus 22 and controls the machining condition estimation device 1 as a whole according to the system program. The RAM 13 temporarily stores temporary calculation data, display data, and various data input from the outside.

[0024] The non-volatile memory 14 is composed of, for example, a memory backed up by a battery (not shown), an SSD (Solid State Drive), etc., and maintains its storage state even when the power supply to the processing condition estimation device 1 is disconnected. The non-volatile memory 14 stores programs and data read from external device 72 via interface 15, programs and data input from input device 71 via interface 18, and programs and data obtained from other devices such as fog computer 6 and cloud server 7 via network 5. The data stored in the non-volatile memory 14 may include, for example, data related to the processing content in the wire EDM machine 8, data related to required specifications, and data related to various physical quantities detected by sensors (not shown) installed on other wire EDM machines 8. The programs and data stored in the non-volatile memory 14 can also be expanded in RAM 13 during execution / use. Furthermore, various system programs, such as known analysis programs, are pre-written in ROM 12.

[0025] Interface 15 is an interface for connecting to external devices 72, such as the CPU 11 of the machining condition estimation device 1 and external storage media. Programs and data for simulation, for example, are read from the external device 72. Furthermore, programs and data edited within the machining condition estimation device 1 can be stored via the external device 72 on external storage media (not shown), such as a CF card or USB memory.

[0026] Interface 20 is used to connect the CPU of the processing condition estimation device 1 to a wired or wireless network 5. Network 5 can communicate using technologies such as RS-485 serial communication, Ethernet, optical communication, wireless LAN, Wi-Fi, and Bluetooth. Control devices for controlling other machinery, a fog computer 6, a cloud server 7, and other upper-level management devices are connected to network 5, exchanging data with the processing condition estimation device 1.

[0027] In the display device 70, data obtained as a result of executing various data and programs read into the memory is output and displayed via the interface 17. In addition, the input device 71, which consists of a keyboard, indicator devices, etc., transmits instructions and data based on the operator's operation to the CPU 11 via the interface 18.

[0028] Figure 2 The functions of the processing condition estimation device 1 of the first embodiment of the present invention are represented by a schematic block diagram.

[0029] The processing condition estimation device 1 of this embodiment possesses various functions through Figure 1The CPU 11 of the machining condition estimation device 1 shown executes the system program and controls the operation of each part of the machining condition estimation device 1 to achieve this.

[0030] The processing condition estimation device 1 of this embodiment includes a processing condition derivation unit 100, a simulation unit 110, a processing condition evaluation unit 120, and a database update unit 130. Furthermore, in the RAM 13 and non-volatile memory 14 of the processing condition estimation device 1, a processing information database 200 is provided, which stores processing information data associated with data related to processing conditions.

[0031] Figure 3 This represents an example of processing information data stored in the processing information database 200.

[0032] like Figure 3 As illustrated, the processing information database 200 stores data related to processing conditions that satisfy the processing content and specifications, in association with data related to the processing content and specifications. Furthermore, the data related to the processing content includes workpiece material, workpiece thickness, processing shape, steps, and surrounding environment; the data related to the specifications includes processing speed, breakage frequency, straightness accuracy, surface roughness, and shape error; and the data related to the processing conditions includes voltage waveform, voltage polarity, current waveform, discharge time, rest time, processing fluid volume, processing hydraulic pressure, line tension, and line feed control. Each piece of processing information data is associated with unique identification information that identifies that piece of processing information data.

[0033] The processing information data stored in the processing information database 200 in the initial stage can be, for example, data input by the operator via input device 71 or external device 72, or data observed during past processing in the in-line electrical discharge machining (EDM) machine 8. The data stored in the processing information database 200 in the initial stage does not need to comprehensively cover all processing content and required specifications.

[0034] pass Figure 1 The CPU 11 of the processing condition estimation device 1 shown executes the system program read from the ROM 12. The CPU 11 mainly performs arithmetic processing using the RAM 13 and the non-volatile memory 14, thereby realizing the processing condition derivation unit 100. The processing condition derivation unit 100 derives predetermined processing conditions that are estimated to meet the processing content and the required specifications based on the data related to the processing content and the required specifications input from the operator.

[0035] The processing condition derivation unit 100 may, for example, derive predetermined processing conditions that are presumed to satisfy the processing content and requirements input from the operator, based on processing information data stored in the processing information database 200. In this case, the processing condition derivation unit 100 performs a similarity search between the data related to the processing content and requirements input from the operator and the data related to the processing content and requirements stored in each processing information data in the processing information database 200. In the similarity search, for example, the distance is calculated when the data related to each processing content and the data related to the requirements are treated as vectors, and the data with the closest distance is extracted as the processing information data with high similarity. Then, for the processing conditions contained in the extracted processing information data, multiple processing conditions with predetermined changes are derived as predetermined processing conditions that are presumed to satisfy the processing content and requirements.

[0036] When the changing data is numerical, the processing condition derivation unit 100 can change the data, for example, by adding predetermined variables. These predetermined variables can be multiple variables. The processing conditions derived by the processing condition derivation unit 100 are used as parameters for the simulated operation of the wire electrical discharge machining (EDM) machine in the simulation unit 110. Essentially, in the simulation, processing conditions that cannot be actually processed due to the specifications of the actual machine can also be set. Therefore, the variations applied to the data by the processing condition derivation unit 100 can exceed the range of general set values ​​and combinations.

[0037] As an example, the processing condition derivation unit 100 may also apply predetermined changes to each condition item based on the processing information data extracted from the processing information database 200, thereby deriving predetermined processing conditions that are presumed to meet the processing content and requirements specifications. This may be a case where changes are applied to each condition item individually, or a combination of cases where changes are applied to each condition item individually.

[0038] For simplicity, the processing conditions include two items: voltage value and discharge time. Furthermore, the voltage value is predefined as a predetermined variation of ±0.1V, and the discharge time is predefined as ±1μsec. The processing condition derivation unit 100 then derives the processing conditions by applying the following eight variations to the processing information data extracted from the processing information database 200.

[0039] (Processing Condition 1) Voltage value +0.1V

[0040] (Processing condition 2) Voltage value -0.1V

[0041] (Processing condition 3) Discharge time +1μsec

[0042] (Processing condition 4) Discharge time -1μsec

[0043] (Processing Condition 5) Voltage value +0.1V, discharge time +1μsec

[0044] (Processing Condition 6) Voltage +0.1V, Discharge Time -1μsec

[0045] (Processing Condition 7) Voltage -0.1V, Discharge Time +1μsec

[0046] (Processing Condition 8) Voltage -0.1V, Discharge Time -1μsec

[0047] In addition to predetermined variations, random numbers can be used to derive variations in the machining conditions. Such variations in machining conditions can be used to derive machining conditions outside of machine specifications and machining conditions not anticipated by humans.

[0048] Furthermore, the processing conditions exported by the processing condition export unit 100 are conditions used to simulate processing conditions not included in the processing information database 200. Therefore, the processing conditions exported by the processing condition export unit 100 are preferably processing information data that is not already stored in the processing information database 200. If the processing information data with a predetermined change applied is the same as the processing information data already stored in the processing information database 200, the processing condition export unit 100 can exclude that processing condition from the output objects.

[0049] pass Figure 1 The CPU 11 of the machining condition estimation device 1 shown executes the system program read from the ROM 12. The CPU 11 mainly performs computational processing using RAM 13 and non-volatile memory 14, thereby realizing the simulation unit 110. The simulation unit 110 performs a simulation of the wire EDM machine based on the machining conditions derived by the machining condition derivation unit 100 and outputs the machining results. The simulation unit 110 sets the parameters that can be set as simulation actions from the machining conditions derived by the machining condition derivation unit 100, the data related to the machining content input by the operator, and the data related to the required specifications, and then performs the simulation processing of the wire EDM machine. The simulation of the wire EDM machine performed by the simulation unit 110 can be performed using known methods disclosed in, for example, "Japanese Patent Application Publication No. 2002-160127", "Masanori Kunieda, 'Electrical Discharge Machining Simulation' Journal of the Institute of Precision Engineering, Vol. 76, No. 8, 2010, pp. 861-866", etc.

[0050] The simulation unit 110 outputs processing speed, wire breakage frequency, straightness accuracy, surface roughness, shape error, etc., as simulation results of the wire electrical discharge machining based on the set parameters.

[0051] pass Figure 1 The processing condition estimation device 1 shown has a CPU 11 that executes a system program read from ROM 12. The CPU 11 primarily performs computational processing using RAM 13 and non-volatile memory 14 to implement the processing condition evaluation unit 120. The processing condition evaluation unit 120 evaluates the degree to which the simulation results of each processing condition derived from the processing condition derivation unit 100 meet the data related to the requirements specifications input by the operator. For example, the processing condition evaluation unit 120 can set a score of 1 if the shape error of the simulation result is smaller than the shape error input by the operator, 0.5 if the shape error is less than twice the operator's input, and 0 if the shape error is more than twice the operator's input. It can also assign a high score to good results and a low score to bad results, thus scoring each item and evaluating them based on their total value. The scores can also be weighted according to the importance of each item. The processing condition evaluation unit 120 outputs each processing condition and its evaluation. The evaluation results of the processing condition evaluation unit 120 can be displayed on the display device 70 or sent to a host device such as a fog computer 6 or a cloud server 7 via the network 5.

[0052] pass Figure 1 The CPU 11 of the processing condition estimation device 1 shown executes the system program read from the ROM 12. The CPU 11 mainly performs computational processing using RAM 13 and non-volatile memory 14, thereby implementing the database update unit 130. The database update unit 130 updates the processing information database based on the processing conditions and their evaluations output by the processing condition evaluation unit 120. For example, the database update unit 130 can generate processing information data by associating the processing conditions that have received the highest evaluation from the processing condition evaluation unit 120 with the processing content input by the operator and the requirement specifications as a result of the simulation unit, and store this data in the processing information database 200. Alternatively, the database update unit 130 can generate processing information data for processing conditions that have received a specified or higher evaluation score from the processing condition evaluation unit 120, and store this data in the processing information database 200. Furthermore, the database update unit 130 can also generate processing information data for the processing conditions selected by the operator from the processing conditions displayed on the display device, and store this data in the processing information database 200.

[0053] As a variation of the processing condition estimation device 1 in this embodiment, the processing condition evaluation unit 120, when a predetermined or higher evaluation is not obtained for each processing condition, for example, when an evaluation point above a predetermined threshold is not obtained, instructs the processing condition derivation unit 100 to derive further processing conditions. The processing condition derivation unit 100, having received such an instruction, further derives processing conditions that result in multiple overlaps of predetermined variations, or derives processing conditions with further random number-based variations. In this way, the simulation unit 110 can continuously search for processing conditions that yield a predetermined or higher evaluation based on the newly derived processing conditions.

[0054] The processing condition estimation device 1 of this embodiment, equipped with the above-described structure, searches for processing conditions that satisfy the processing content and required specifications input from the operator. It then uses simulation techniques to evaluate the processing conditions predicted through the search, thereby enabling the evaluation of processing conditions without actual processing. Since actual processing is not required, the manual labor involved in actual processing can be reduced. Furthermore, by setting the computing resources used for simulation to high speed, more data can be obtained in a shorter time compared to actual processing. In the simulation, processing conditions that cannot be actually processed due to the specifications of the actual machine can also be evaluated. Therefore, processing conditions of unforeseen ranges and combinations can also be simulated, allowing for the evaluation of processing conditions outside the machine specifications and processing conditions unforeseen by the operator, thus providing a basis for machine specification improvement and new research and development.

[0055] Figure 4 This is a schematic hardware structure diagram showing the main parts of the processing condition estimation device according to the second embodiment of the present invention. The processing condition estimation device 1 of this embodiment uses a machine learning machine 2 to estimate the processing conditions.

[0056] The processing condition estimation device 1 of this embodiment, in addition to having the processing condition estimation device 1 of the first embodiment, also includes the processing condition estimation device 1 of the first embodiment. Figure 2 In addition to the structure it possesses, it also has interface 21 and machine learning device 2.

[0057] Interface 21 is used to connect CPU 11 to machine learning device 2. Machine learning device 2 includes: processor 201, which controls the machine learning device 2 as a whole; ROM 202, which stores system programs, etc.; RAM 203, which is used for temporary storage during various processes involved in machine learning; and non-volatile memory 204, which stores models, etc., that have learned data sets that have become learning objects. Machine learning device 2 can observe data generated by processing condition estimation device 1 (e.g., data related to processing content, data related to requirement specifications, data related to processing conditions, etc.) via interface 21. Furthermore, processing condition estimation device 1 obtains the processing results output from machine learning device 2 via interface 21, stores or displays the obtained results, or sends them to other devices via network 5, etc. In addition, in Figure 1 In this process, the machine learning machine 2 is built into the processing condition estimation device 1, but it can also be externally connected to the processing condition estimation device 1 via a predetermined interface.

[0058] Figure 5 The functions of the processing condition estimation device 1 of the second embodiment of the present invention are represented by a schematic block diagram.

[0059] The processing condition estimation device 1 of this embodiment possesses various functions through Figure 4 The CPU 11 of the processing condition estimation device 1 and the processor 201 of the machine learning device 2 execute the system program to control the actions of each part of the processing condition estimation device 1 and the machine learning device 2.

[0060] In addition to the processing condition estimation unit 100, simulation unit 110, processing condition evaluation unit 120, and database update unit 130, the processing condition estimation device 1 of this embodiment also includes a machine learning unit 205 configured on the machine learning device 2. Furthermore, a processing information database 200 is provided in the RAM 13 or non-volatile memory 14 of the processing condition estimation device 1, containing data related to predetermined processing content and data related to required specifications. This processing information database 200 is a region that stores processing information data that links processing condition-related data. Moreover, a model storage unit 209 is prepared in advance in the RAM 203 or non-volatile memory 204 of the machine learning device 2 as a region for storing models generated as results learned by the learning unit 206.

[0061] pass Figure 4The machine learning machine 2 shown has a processor 201 that executes a system program read from ROM 202. The processor 201 mainly performs computational processing using RAM 203 and non-volatile memory 204, thereby realizing the machine learning unit 205 of this embodiment. The machine learning unit 205 performs machine learning-related processing (learning, estimation, etc.) based on instructions from various functions of the processing condition estimation device 1.

[0062] The learning unit 206 of the machine learning unit 205 generates a model based on the processing information data stored in the processing information database 200. This model learns the correlation between data related to the processing content, data related to the requirements and specifications, and data related to the processing conditions. The generated model is then stored in the model storage unit 209. The learning performed by the learning unit 206 can be, for example, supervised learning. When the model generated by the learning unit 206 is input with data related to the processing content and data related to the requirements and specifications through machine learning, it can infer the data related to the processing conditions that satisfy the processing content and requirements and specifications. Examples of models generated by the learning unit 206 include regression learners and multilayer neural networks.

[0063] The estimation unit 208 of the machine learning unit 205 performs estimation processing of the processing conditions using the model stored in the model storage unit 209, based on data related to the processing content input from the processing condition derivation unit 100 and data related to the requirement specifications. The estimation processing performed by the estimation unit 208 can be based on known supervised learning and reinforcement learning estimation processing.

[0064] Furthermore, the processing condition export unit 100 is the same as the processing condition export unit 100 in the first embodiment ( Figure 2 Similarly, based on data related to the processing content and requirements input by the operator, predetermined processing conditions that are presumed to satisfy the processing content and requirements are derived. In this embodiment, the processing condition derivation unit 100, similar to the processing condition derivation unit 100 in the first embodiment, derives predetermined processing conditions based on processing information data stored in the processing information database 200 when the model in the learning unit 206 is not sufficiently learned. On the other hand, when the model in the learning unit 206 is sufficiently learned, the processing condition derivation unit 100 inputs data related to the processing content and requirements input by the operator to the machine learning unit 205 to obtain processing conditions that are presumed to satisfy the processing content and requirements input by the operator. Then, for the presumed processing conditions, multiple processing conditions with predetermined variations are derived as predetermined processing conditions that are presumed to satisfy the processing content and requirements.

[0065] Whether the learning of the model in the learning unit 206 is sufficient can be determined by the amount of processing information data stored in the processing information database 200. For example, the processing condition derivation unit 100 can estimate the processing conditions entrusted to the machine learning unit 205 if the amount of processing information data stored in the processing information database 200 is above a predetermined threshold.

[0066] The processing condition estimation device 1 of this embodiment, equipped with the aforementioned structure, searches for processing conditions that satisfy the processing content and required specifications input by the operator. It uses simulation techniques to evaluate the processing conditions predicted through the search, thereby enabling the evaluation of processing conditions without actual processing. By estimating the processing conditions that serve as the benchmark for the search using machine learning, it is expected that the computational load of the simulation processing involved in the search will be significantly reduced as the estimation accuracy of the machine learning machine improves.

[0067] The present invention has been described above as one embodiment, but the present invention is not limited to the example of the described embodiment and can be implemented in various ways by applying appropriate modifications.

[0068] Explanation of reference numerals in the attached figures

[0069] 1. Processing condition estimation device

[0070] 5. Network

[0071] 6 Fog Computers

[0072] 7 Cloud Servers

[0073] 8-wire electrical discharge machining

[0074] 11 CPU

[0075] 12 ROM

[0076] 13 RAM

[0077] 14. Non-volatile memory

[0078] Interfaces 15, 17, 18, 20, and 21

[0079] 22 bus

[0080] 70 Display devices

[0081] 71 Input Device

[0082] 72 External devices

[0083] 100 Processing Condition Export Section

[0084] 110 Simulation Department

[0085] 120 Processing Condition Evaluation Department

[0086] 130 Database Update Department

[0087] 2 Machine Learning Machines

[0088] 201 processor

[0089] 202 ROM

[0090] 203 RAM

[0091] 204 Non-volatile memory

[0092] 205 Machine Learning Department

[0093] 206 Study Department

[0094] 208 Presumption Department

[0095] 209 Model Storage Department.

Claims

1. A machining condition estimation device for estimating machining conditions in a wire electrical discharge machining (EDM) machine, characterized in that, The processing condition estimation device includes: The processing information database stores processing information data related to the processing content and the required specifications, as well as processing conditions related to satisfying the processing content and the required specifications. The processing information data is input by the operator or observed in past processing in the wire EDM machine. The processing condition derivation unit derives at least one processing condition that is presumed to satisfy the desired processing content and requirements based on the similarity search between the processing information data stored in the processing information database and the data related to the desired processing content and requirements input by the operator. The simulation unit uses the processing conditions exported by the processing condition export unit as parameters to perform a simulation of the wire electrical discharge machining, and outputs at least the values ​​of the items related to the required specifications as simulation results. The processing condition evaluation unit evaluates the processing conditions derived by the processing condition derivation unit based on the degree to which the simulation results performed by the simulation unit satisfy the data related to the requirement specifications input by the operator, without performing actual processing. as well as The database update unit, based on the evaluation results performed by the processing condition evaluation unit, generates processing information data that associates the processing conditions evaluated by the processing condition evaluation unit, the data related to the desired processing content, and the values ​​of items related to the required specifications obtained as simulation results, and stores the data in the processing information database.

2. The processing condition estimation device according to claim 1, characterized in that, The processing condition estimation device also includes a machine learning unit, which learns the correlation between data related to processing content, data related to requirements and specifications, and data related to processing conditions based on processing information data stored in the processing information database. The processing condition derivation unit inputs data related to the desired processing content and the required specifications, which are input by the operator, into the machine learning unit, obtains processing conditions estimated based on the learning results of the machine learning unit, applies predetermined changes to the obtained processing conditions, and thereby derives at least one processing condition estimated to satisfy the desired processing content and required specifications.

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