Optimal design system for machining processes

Abstract

To provide a machining process optimal design system which, with respect to a machining process where a plurality of machining steps are combined, can achieve an appropriate machining goal of the entire machining process and further can design the individual machining steps from the goal of the entire machining process.SOLUTION: In the optimal design system for a machining process where a plurality of machining steps from a first step to a prescribed n-th step are combined in time sequence, (i-1)th step quality information after an (i-1)th step before machining in an i-th step, an i-th machining parameter including a machining condition in the i-th step, an i-th step quality function as a function for calculating quality after the i-th step, and an i-th step cost function of the i-th step are set, and quality information after machining based on the i-th machining parameter and the i-th step quality function is outputted as i-th step quality information in the next i-th step, and i-th step cost information in the i-th step for a workpiece, which is based on the i-th machining parameter and the i-th step cost function, is outputted.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an optimal design system for machining processes that combines multiple machining steps, and that can achieve the quality, cost, etc. required for the entire machining process, taking into account the target quality, cost, etc. for each machining step.

[0002] Conventional Technology

[0003] When machining products using processes such as cutting, grinding, polishing, and friction stir welding, the process is carried out by executing a complete machining process that combines each process step in chronological order, such as rough machining, semi-rough machining, finish machining, and internal grinding. Conventionally, in each process, the machining equipment used to set the optimum tool specifications (material, shape, number of flutes, cutting angle, etc.), rotation speed (rotational speed), cutting speed, and other machining conditions as empirical values according to the material, thickness, and shape of the workpiece, and then verify the quality and cost of the product after the fact.

[0004] In response to this, the present inventors and the applicant have developed devices and systems that measure the temperature, vibration, load, etc. of processing tools such as tools and lubricants during each processing in real time, and can properly evaluate and verify processing conditions during processing and correct them from the processing device or an external device (see Patent Documents 1 to 3). In addition, real-time data during processing is individually stored, updated, and accumulated as a data group to form various types of advance reference data, making it possible to control new processing using appropriate processing condition data (Patent Document 4, JP 2019-42831 A).

[0005] However, in actual machining, as described above, various machining steps are combined in chronological order to achieve the process targets (process targets) of the final processed product, such as the appropriate quality and cost (including the number of steps). It is not enough to simply satisfy the process targets of each step; each step must be adjusted and the overall process must ultimately be designed while taking into account the impact on other steps. For example, setting high processing targets for roughing and semi-roughing can reduce the cost of finishing, while setting excessively high processing targets for roughing and semi-roughing can significantly increase the costs of these steps, resulting in an imbalance in the cost relative to the appropriate quality of the overall process. Furthermore, the quality and cost requirements of final processed product suppliers vary widely, and each step must be adjusted individually to prioritize meeting these requirements when designing the overall process. Therefore, in actual processing sites, simply designing the quality and cost of each processing step will result in "seeing the trees but not the forest," and it is necessary to design the entire processing process and keep it within the required range of quality and total cost of the final product. Currently, this design is left to the experience of on-site operators, and it is expected to become an important issue in the future from the perspective of securing human resources, finding time for human resource training, and promoting the transfer of manufacturing bases overseas. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] International Publication WO2015-022967 [Patent Document 2] International Publication WO2016-136919 [Patent Document 3] Japanese Patent Application Publication No. 2018-54611 [Patent Document 4] Japanese Patent Application Publication No. 2019-42831 Summary of the Invention

[0007] [Problem to be solved by the invention]

[0008] The present invention has been created in view of the above-mentioned circumstances, and aims to provide an optimal design system for a machining process that combines multiple machining steps, utilizing a database of measurement results measured in real time in each machining step, and that is capable of achieving appropriate machining targets for the entire machining process based on the correlation between each machining step. [Means for solving the problem]

[0009] The present invention is an optimal design system for a machining process that combines multiple machining processes from a first process to a predetermined nth process in a time series, The quality information of the i-1th process (Qp i-1 )and, The i-th processing parameter (Pp i )and, The i-1st process quality information (Qp i-1 ) and the i-th processing parameter (Pp i ) and the ith process quality function (f i )and, The i-1st process quality information (Qp i-1 ) and the i-th processing parameter (Pp i ) and the i-th process cost function (g i ) and set i-1st process quality information (Qp i-1 ) input, the ith processing parameter (Pp i ) and the ith process quality function (f i ) is used as the material information for the next i-th process, which is the i-th process quality information (Qp i ), i-1st process quality information (Qp i-1 ) input, the ith processing parameter (Pp i ) and the i-th process cost function (g i ) is used as the ith process cost information (Cp i )

[0010] In addition, the present machining process optimization system is provided with a storage means for a group of machining data that accumulates, stores, and updates quality information as material information of the workpiece measured in real time during the machining process and machining results for predetermined machining parameters including machining conditions, and stores the ith process quality information (Qp i ) and ith process cost information (Cp i )teeth, The (i-1)th order process quality information (Qp i-1 ) and the i-th processing parameter (Pp i ) and the corresponding ith process quality function (f i ) and the i-th process cost function (g i ) and the processing results that match the i-th process quality information (Qp i ) and the ith process cost information (Cp i ) can be output.

[0011] Usually, in the machining process of a workpiece, a plurality of machining processes from the first process to the nth process, such as rough machining, semi-rough machining, finish machining, and internal diameter polishing, are combined in time series to form the entire machining process. In the optimal design system for machining processes of the present invention, quality information (Qp i-1 ) and enter the entered quality information (Qp i-1 ) and machining parameters such as tools and machining conditions (Pp i ) and the quality information (Qp i ) and cost information required for processing, such as processing costs and processing time (Cp i) is output. At this time, a function (f i ), and this function (f i ) is the quality information (Qp i-1 ) and processing parameters (Pp i ) and calculate the quality information (Qp i ) is calculated. In addition, a function (g i ), and this function (g i ) is the quality information (Qp i-1 ) and processing parameters (Pp i ) and calculate the cost information (Cp i ) is also calculated.

[0012] Furthermore, this machining process optimization system reads in the processing data accumulated in the past and calculates the quality information (Qp i-1 ,Qp i ), cost information (Cp i ), quality function (f i ), cost function (g i ) can be extracted and set, and optimal design is performed from a group of machining data (database) measured during actual past machining, making it highly practical. Furthermore, the group of machining data is updated each time measurements are taken in real time for each machining step during the machining process, so the data group expands over time, improving the precision of the optimal design.

[0013] In addition, in this machining process optimization system, for example, material information including at least the quality of the workpiece before machining in the first process is preset as the initial state zero-order process quality information (Qp0), and n-order process quality information (Qp n ) from the first process quality information (Qp1) to the nth process quality information (Qp n ) is output by calculating each subsequent process in sequence.

[0014] In other words, the quality of each processing step is calculated based on the quality of the previous processing step (initial quality in the case of the primary process), and this calculation is carried out in order from the primary process to the final process to calculate the quality after the entire processing process is completed.

[0015] In addition, in the present manufacturing process optimization system, for example, the output ith process cost information (Cp i ) is summed from the first process where i=1 to the nth process where i=n, and output as the total cost information (ΣCp) for the entire processing process.

[0016] As described above, the cost information for each processing step is calculated based on the quality information after the previous processing step, and the cost information for the entire processing process can be calculated by sequentially adding up this cost information from the initial step to the final step.

[0017] Therefore, this machining process optimization design system can calculate the cost of the entire machining process (machining time, machining cost) while outputting the quality and cost for each machining step, and can evaluate, optimize, and minimize the final quality and total cost of the entire machining process. In other words, it is possible to optimally design the final quality and total cost of the entire machining process according to the goal (requirement) after final machining.

[0018] Furthermore, in this manufacturing process optimization system, the nth process quality information (Qp n ) and the total cost information (ΣCp) are output, and then the i-th processing parameter (Pp i ) and / or the ith process quality function (f i ) and / or the i-th order process cost function (g i ) and reset the nth process quality information (Qp n ) and the total cost information (ΣCp) can also be corrected and output.

[0019] In other words, in this manufacturing process optimization system, after first calculating the quality information and total cost information for the entire manufacturing process, the conditions for any manufacturing process (Pp i ,Qp i ,f i ,g i ) can be changed and reset, and the quality information and total cost information for the entire machining process can be recalculated. The conditions for resetting at this time can be set from real-time measurement data for the current machining, extracted from different data in a machining data group (database), or manually input by a worker on-site based on experience.

[0020] As mentioned above, this optimal design system for machining processes outputs quality information and cost information for each machining step, and can minimize and optimize the cost and machining time of the entire machining process. i It is preferable to weight the processing time, processing cost, and required resources for each processing step. This is because the optimization goal for the manufacturing equipment required for each processing step varies depending on the status of required resources, such as the operating status of manufacturing plants that manufacture and process other products and the status of material inventory. Therefore, weighting is performed for each processing step to evaluate the total required cost, and conversely, each processing step can be designed and selected based on the total cost. [Effects of the Invention]

[0021] In the machining process optimization design system of the present invention, in a machining process that combines multiple machining steps, it is possible to design each machining step so that the quality and cost of the workpiece after that machining step are adjusted to within an acceptable range based on the quality after the previous machining step, and it is also possible to optimize targets for quality and total cost when the entire machining process is completed, taking into account the correlation between each machining step within the entire machining process, and conversely, it is also possible to optimize the quality and cost at each machining step based on the acceptable quality and total cost at the end of the entire machining process. [Brief explanation of the drawings]

[0022] [Figure 1] This is a schematic diagram of a processing process in which multiple processing steps are combined in chronological order. [Figure 2] (a) shows an image of the solution space of the input quality (Qpi) of an arbitrary machining process in the optimal design system for machining processes of the present invention, and (b) shows an image of the solution space of the cost (Cpi) required for an arbitrary machining process. [Figure 3] A schematic diagram is shown that visualizes the mapping of the solution space of the quality information (Qpi) and cost information (Cpi) described above using the conceptual diagram of the solution space in Figure 2. [Figure 4] A flow chart showing a method for outputting quality information (Qp1 to Qpn) after the nth process and quality information (Cp1 to Cpn) and cost information (Cp1 to Cpn) for each processing process that is appropriate for the total cost information (ΣCp) for the entire processing process from a processing data group in the optimal design system for processing processes of the present invention is shown. [Figure 5] (a) is a photograph of the vicinity of the rotating spindle of a machining center as an example of a machining device that utilizes the machining process optimization design system of the present invention, and (b) is an example photograph of an external terminal that receives and analyzes data from the tool holder unit 1 in (a). BEST MODE FOR CARRYING OUT THE INVENTION

[0023] 1 to 4 show schematic diagrams of the machining process optimization system of the present invention. First, as shown in Fig. 1, a machining process usually comprises a plurality of steps such as a first step, a second step, ... the (n-1)th step, and an nth step, for example, a first step is rough machining, a second step is medium-rough machining, a third step is finish machining, and a final fourth step is inner diameter polishing, and these steps are combined to form one machining process.

[0024] In this example of a machining process optimization system, the primary process is the first machining process, and the material quality (initial quality) of the workpiece in its initial state before machining, such as dimensional error, surface roughness, and scratch depth, is input as the 0th (1-1)th process quality information (Qp0). Furthermore, the machining conditions in the primary process, such as the tools and cutting conditions, are set as variables as the primary machining parameters (Pp1). Furthermore, a primary process quality function (f1) is set as a function for calculating the target quality of the workpiece after the primary process, and a primary process cost function (g1) is set as a function for calculating the target cost of the workpiece in the primary process (machining time and machining cost). Then, for a combination of the 0th process quality information (Qp0) as the material quality (initial quality) and the 1st processing parameters (Pp1) as the processing conditions, the 1st process quality function (f1) and the 1st process cost function (g1) are used to calculate (output) the quality information (Qp1) after the 1st process (= before the 2nd process) as shown in the following formula (1) and the 1st process cost information (Cp1) as the cost required for the 1st process (processing cost, processing time) as shown in the following formula (2).

[0025] Qp1=f1[Qp0×Pp1]...Equation (1) Cp1=g1[Qp0×Pp1]...Equation (2)

[0026] Next, in the secondary process, quality (Qp1) such as dimensional error, surface roughness, and scratch depth after the primary process is input as primary process quality information (Qp1). In addition, processing conditions such as tools and cutting conditions in the secondary process are set as variables as secondary processing parameters (Pp2). In addition, a secondary process quality function (f2) is set as a function for calculating the quality of the workpiece that is the target for the secondary process, and a primary process cost function (g2) is set as a function for calculating the cost (processing time and processing cost) of the workpiece that is the target for the second process. Then, as in the case of the first process, the second process quality function (f2) and the second process cost function (g2) are used for each combination of the first process quality information (Qp1) before processing (= after the first process) and the second processing parameters (Pp2) as processing conditions to calculate (output) the quality information (Qp2) after the second process (= before the third process) as shown in the following equation (3) and the second process cost information (Cp2) as the cost required for the second process (processing cost, processing time) as shown in the following equation (4).

[0027] Qp2=f2[Qp1×Pp2]...Equation (3) Cp2=g2[Qp1×Pp2]...Equation (4)

[0028] As shown in the above formulas (1) to (4), when the output of the quality after processing and the cost required for processing in each processing step is repeated up to the nth step, the quality information after the final nth step (Qp n ) and cost information required for the nth process (Cp n ) is output. The following equations (5) and (6) show the quality information (Qp n ) and cost information required for the nth process (Cp n ) evaluation function is shown.

[0029] Qp n =f n [Qp n-1 ×Pp n ]...Equation (5) Cp n =g n [Qp n-1 ×Pp n ]...Equation (6)

[0030] Therefore, if we generalize this to any ith process (1≦i≦n) from the initial process to the final process, the quality information after the ith process (Qp i ) and cost information required for the i-th process (Cp i ) is the quality information after the i-1th process (Qp i-1 ) is used as the ith process quality information (Qp i―1 ), and the machining conditions such as the tool and cutting conditions in the ith process are input as the ith machining parameters (Pp i ) as a variable, and the ith process quality function (f i ), the i-th process cost function (g i ) and the quality information after the i-th process (Qp i ), the cost information required for the generalized ith process as in equation (8) (Cp i ) is output.

[0031] Qp i =f i [Qp i―1 ×Pp i ]...Equation (7) Cp i =g i [Qp i―1 ×Pp i ]...Equation (8)

[0032] In other words, the quality information (Qp i : Figure 2(b)) and cost information (Cp i As shown in the conceptual diagram of the solution space in Fig. 2(a), quality information (Qp i ) and cost information such as processing time and processing costs (Cp i ) is the quality information (Qp i-1 ) as input information, and machining parameters (Pp i ) is determined by mapping from a set of vector solutions (solution space) combined with the quality information after each processing step (Qpi ) is the tolerance (function that calculates the quality of the workpiece after the processing process (i-th process quality function: f i )), that is, quality information (Qp i-1 ) solution space is constrained and the quality information of the next processing step (Qp i ) is mapped to the solution space of (Qp i =f i [Qp i―1 ×Pp i ]). Also, cost information such as processing time and processing costs (Cp i ) also has a function to calculate the allowable range (the cost required for each processing step (processing cost, processing time) (i process cost function: g i ), that is, quality information (Qp i―1 ) solution space is constrained, and the cost information of the next processing step (Cp i ) is mapped to the solution space of (Cp i =g i [Qp i―1 ×Pp i ]). Then, the cost information of each mapped processing step (Cp i The total cost information (ΣCp) is calculated by adding up all the costs involved in the entire manufacturing process, and this information is then minimized in response to requests from manufacturers, product suppliers, etc., in order to reduce the total cost of the entire manufacturing process.

[0033] Regarding the total cost information (ΣCp) for the entire processing process, the total cost information from the first process to the nth process is ΣCp n The calculation formula is as shown in the following formula 1.

number

[0034] In Fig. 3, the above-mentioned quality information (Qp i ), cost information (Cp i ) solution space is shown in the left to center columns of Fig. 3. In (a-1), the quality information (Qp i-1 ) solution space is shown, and the quality information (Qp i-1) solution space is within a given tolerance (i-th order process quality function: f i ) and the processing conditions (processing parameters (Pp i )) after the i-th process (= before the i+1-th process) i ) solution space is shown, and (b) shows the quality information (Qp i-1 ) solution space is within a given tolerance (i-th process cost function: g i ) and the processing conditions (processing parameters (Pp i )) i ) solution space is shown.

[0035] In addition, the middle to right columns of Figure 3 show the quality information (Qp i ) solution space is within a given tolerance (i+1 order process quality function: f i+1 ) and the processing conditions (processing parameters (Pp i+1 )) after the i+1th process (= before the i+2nd process) i+1 ) solution space is shown, and the quality information (Qp i ) solution space is within a given tolerance (i+1 order process cost function: g i+1 ) and the processing conditions (processing parameters (Pp i+1 )) i+1 ) solution space is shown.

[0036] The arrows (i)(i)' in Fig. 3(a-1) to (a)(b) indicate the machining conditions (machining parameters (Pp i As an example of the above, when the processing cost and processing time are slightly higher than the allowable range, refer to the following formulas (9) and (10): Qp i =f i [Qp i-1 ×Pp i ]...Equation (9) Cp i =g i [Qp i-1 ×Pp i ]...Equation (10) Quality information before the i-th process (Qp i-1 ) is the solution space of the quality information after the i-th process (Qp i-1 ) solution space and the cost information of the i-th process (Cp i ) solution space, and the arrows (ii)(ii)' indicate the machining conditions (machining parameters (Pp i As another example of (i), when the processing cost and processing time are slightly less than the allowable range, the solution space is mapped in the same way as the arrows (i)(i)'.

[0037] In addition, the arrows (iii)(iii)' in Figure 3(a) to (a+1)(b+1) indicate the machining conditions (machining parameters (Pp i As an example of the above, when the processing cost and processing time are slightly higher than the allowable range, refer to the following equations (11) and (12): Qp i+1 =f i+1 [Qp i ×Pp i+1 ]...Equation (11) Cp i+1 =g i+1 [Qp i ×Pp i+1 ]...Equation (12) The quality information (Qp i ) and the solution space is the quality information after the i+1th process (Qp i+1 ) solution space and the cost information of the i+1th order process (Cp i+1 ) solution space, and the arrows (iv)(iv)' indicate the machining conditions (machining parameters (Pp i As another example of (iii), when the processing cost and processing time are slightly less than the allowable range, the solution space is mapped in the same way as the arrows (iii)(iii)'.

[0038] As can be seen from Figure 3, each machining process is correlated in order, and the quality of any machining process (i-th process: e.g., rough machining) significantly affects the quality and cost of subsequent machining processes (i+1-th process: e.g., finish machining). As a result, if a small amount of cost and time is spent in the previous process, the desired quality can be achieved even with reduced cost and time in the next process. Conversely, if cost and time are reduced in the previous process, the desired quality cannot be achieved unless more cost and time are spent in the next process. Therefore, by accumulating measurement data from the entire 1st to nth process and storing it in a database, it is possible to design the quality of the final process and the total cost of the entire machining process within the required range, taking into account the correlation between processes. For example, if the nth process is the final process and quality within an acceptable range is desired, it is possible to extract each machining process from the database (machining data group) and determine the machining conditions that will achieve the shortest machining time and lowest machining cost for the entire machining process before machining. As a result, it is possible to determine which machining process should have its quality improved and how much machining time and cost should be spent by calculating backward from the acceptable range of the final process.

[0039] Next, in the optimum design system for this machining process, each machining process is read from the machining data group and the quality information (Qp n ) and the quality information (Qp1 to Qp n ) and cost information (Cp1~Cp n ) will be illustrated and described with reference to the flow chart of FIG.

[0040] First, the number of processes assumed in advance, n, the quality information of the initial state (Qp0), the 1st order process quality function (f1) to the i-th order process quality function (f i ), the first-order process cost function (g1) to the i-th-order process cost function (g i ) is input and set (ST1). Then, the set quality information (Qp0), the quality functions of each processing step (f1 to f i ), each processing step cost function (g1~g i) is extracted from the processed data group (database) and output (ST2).

[0041] Next, set i=1 as the initial processing step (first process) (ST3), and set i=1 as Qp i =f i [Qp i-1 ×Pp i ] Cp i =g i [Qp i-1 ×Pp i ] and calculates the first process quality information (Qp1) and the first process cost information (Cp1) (ST4).

[0042] When the first process quality information (Qp1) and the first process cost information (Cp1) are output, the set value of i is incremented by 1 to make i=2 (ST6), and after calculating the second process quality information (Qp2) and the second process cost information (Cp2) in the second process (ST4), the set value of i is incremented by 1 again (ST6), and the third process quality information (Qp3) and the third process cost information (Cp3) in the third process (ST5) are calculated. These ST6 to ST4 are repeated until i=n, and when i≧n (ST5), the nth process quality information (Qp n ) and the cost information from the initial process to the final process are added together to calculate the total cost information (ΣCp) (ST7). Note that Qp calculated based on the data measured in real time during actual processing in each process i =f i [Qp i-1 ×Pp i ], Cp i =g i [Qp i-1 ×Pp i ] are successively accumulated and updated in the processed data group (database) (ST4 to ST2).

[0043] Then, the calculated nth process quality information (Qp n) and the total cost information (ΣCp) are determined to be within a predetermined desired range (tolerance range) (ST8). If they are within the desired range, it is determined that the design is optimal, and the quality information (Qp1 to Qp n ) and cost information (Cp1~Cp n ) is output (ST10).

[0044] In addition, the nth process quality information (Qp n ) and the total cost information (ΣCp) are not within the predetermined desired range (tolerance range), the quality function (f i ) and cost function (g i ) is reset (ST9). In FIG. 4, it is assumed that the i-th process designated in this resetting (ST9) is arbitrarily selected by the operator, but it is also assumed that the whole or part of the machining process is re-extracted (ST2) from different stored data stored in the machining data group. Then, the n-th process quality information (Qp n Steps ST4 to ST8 are repeated until the total cost information (ΣCp) and the quality information (Qp1 to Qp n ) and cost information (Cp1~Cp n ) is output (ST10).

[0045] Next, Figure 5(a) shows the vicinity of the rotating spindle 2 of a machining center as an example of machining equipment that utilizes this machining process optimization design system, and shows a photograph of a tool holder unit 1 equipped with sensors that measure the temperature and acceleration of the tool that is held and attached in real time. Like a normal tool holder, this tool holder unit 1 is held at its top by the rotating spindle 2 and holds the tool at its bottom, but unlike a normal tool holder, it is formed as a unit with the function of being able to detect the condition near the tool during machining in real time.

[0046] This unit measures the tool temperature and acceleration (vibration) during machining, as well as the load (stress (load meter value)) on the motor attached to the machining center's rotating spindle. The data is then digitized and transmitted to an external device (external PC 32), where it is received and analyzed. FIG. 5(b) is a photograph showing an example of an external device that receives and analyzes data from the tool holder unit 1 shown in FIG. 5(a). The receiver 31 receives the digital data from the tool holder unit 1 and transmits it to the external PC 32 and / or a server or cloud server (not shown). The external PC 32 or the server or cloud server (not shown) acts as storage means, storing and updating the measurement data for each machining process in a database (machining data group). The external PC 32, which receives the digital data from the receiver 31 of the tool holder unit 1 via the external PC 32 or the server or cloud server (not shown), also has a display function that processes (or calculates) the data using dedicated software and displays it as a time-series graph in window 10 on the display.

[0047] When optimizing and designing a new machining process, the (i-1)th order process quality information (Qp i-1 ) and the i-th processing parameter (Pp i ) or the i-1st process quality information (Qp i-1 ) and the i-th processing parameter (Pp i ) and the corresponding ith process quality function (f i ) and the i-th process cost function (g i ) and the processing results that match are extracted and the ith process quality information (Qp i ) and ith process cost information (Cp i ) will be used.

[0048] Although an embodiment of the machining process optimization system of the present invention has been described above, the present invention is not limited to this embodiment, and those skilled in the art will understand that there are various improvements and modifications based on the spirit and teachings of the claims. [Explanation of symbols]

[0049] 1...Tool holder unit 2...Main rotating shaft 10...Window 31...Receiver 32...External PC

Claims

1. In an optimal design system for a machining process that combines multiple machining processes from a first process to a predetermined nth process in a time series, The (i-1)th process quality information (Qp ｉ－１ )and, The i-th order processing parameters (Pp ｉ )and, The i-1st process quality information (Qp ｉ－１ ) and the i-th order processing parameter (Pp ｉ ) and the i-th process quality function (f ｉ )and, The i-1st process quality information (Qp ｉ－１ ) and the i-th order processing parameter (Pp ｉ ) and the i-th process cost function (g ｉ ) and set i-1st process quality information (Qp ｉ－１ ) for the input of the i-th processing parameter (Pp ｉ ) and the i-th order process quality function (f ｉ ) is used as the material information in the next i-th process. ｉ ) and i-1st process quality information (Qp ｉ－１ ) for the input of the i-th processing parameter (Pp ｉ ) and the i-th step cost function (g ｉ ) is used as the ith process cost information (Cp ｉ ) and Material information including at least the quality of the workpiece before processing in the first process is preset as initial zero-order process quality information (Qp 0 ); n-th order process quality information (Qp n ) as quality information after the final process of the manufacturing process where i=n is output by sequentially calculating each subsequent process from the first order process quality information (Qp 1 ) to the n-th order process quality information (Qp n ); The optimal design system for a machining process sums up the output i-th process cost information (Cp i ) from the first process where i=1 to the n-th process where i=n, and outputs it as total cost information (ΣCp) for the entire machining process.

2. a storage means for storing a group of processing data that accumulates, stores, and updates processing results for predetermined processing parameters including quality information as material information of the workpiece measured in real time during the processing process and processing conditions; The i-th process quality information (Qp ｉ ) and i-th process cost information (Cp ｉ )teeth, The (i-1)th order process quality information (Qp ｉ－１ ) and the i-th processing parameter (Pp ｉ ) and the corresponding i-th order process quality function (f ｉ ) and the i-th step cost function (g ｉ ) and the processing results that match the quality information of the i-th process (Qp ｉ ) and the i-th process cost information (Cp i 2. The system for optimally designing a machining process according to claim 1, wherein the system outputs the following:

3. The n-th process quality information (Qp n ) and the total cost information (ΣCP), the i-th order processing parameter (Pp i ) and / or the i-th order process quality function (f i ) and / or the i-th step cost function (g i ) is reset to the nth process quality information (Qp n 3. The system for optimal design of a machining process according to claim 1, wherein the system corrects and outputs the total cost information (ΣCP) and the total cost information (ΣCP).

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