TWI814471B - Processing parameters optimizing system of precision component product and method thereof - Google Patents

Abstract

A processing parameters optimizing system of precision component product for optimizing processing parameters of the precision spindle of tool machine includes a quality property input module for the user to input quality assessment items and quality property data of a component product; a processing ability index module converting the quality property data into a processing ability index value and obtaining an unqualified value according to the ability index analysis table; an essential factor analysis module analyzing the unqualified value through the essential factor analysis method to obtain the failing factor of the process; a significant factor analysis module analyzing the failing factor of the process through the Taguchi method to obtain the significant effect factor; and an optimal parameter analysis module analyzing the significant effect factor through the response surface method to obtain the optimal processing parameters combination value.

Description

Process parameter optimization system and method for precision parts products

The invention relates to an optimization system, in particular to a process parameter optimization system and method for precision parts products.

The application development trend of global machine tools has currently transformed from heavy industry in the past to industries such as optoelectronics, information communication technology, and biotechnology. my country's machinery industry, with the machine tool industry as its basic core, is facing the development of future mechanical precision manufacturing technology. Trends, the demand for related equipment and important components will be even more intense.

The machine tool owner is a power device used to process workpieces and is an important equipment in the manufacturing process of mechanical parts. Among them, the precision spindle in the machine tool is the core of the machine tool. The machine tool needs a precision spindle with stable quality and proper operation to operate normally.

However, during the processing of today's precision spindle parts, it is easy to cause the rotation center to deviate due to factors such as asymmetric shape, processing and assembly errors, uneven materials, poor inner and outer diameter concentricity, incorrect moving lathe methods, etc., causing the machine tool to deviate. Centrifugal vibration is generated, which in turn causes the machine tool to have problems with low processing precision and poor quality.

In order to solve the above problems, the present invention discloses a process parameter optimization system for precision parts products. Its use characteristic diagram analyzes the key factors that affect the processing capability of the precision spindle of the machine tool, and uses the Taguchi method and the reaction surface method to help find out The optimal combination of process parameters for the machining process of precision spindles of machine tools, in order to improve the process and more efficiently manufacture key precision spindles that meet the high precision, high speed and high stability of the workpiece, and at the same time enhance industrial competitiveness purpose.

To achieve the above object, one embodiment of the present invention provides a process parameter optimization system for precision parts products, which is used to optimize the process parameters of a precision spindle of a machine tool. The process parameter optimization system for precision parts products includes a quality characteristic input module, a process capability index module, a factor analysis module, a significant factor analysis module and an optimal parameter analysis module that are coupled to each other. The quality characteristic input module allows users to input multiple quality evaluation items of a part product, and allows users to set multiple quality characteristic data correspondingly according to the quality evaluation items; the process capability index module has a process capability index conversion unit and a process capability The index analysis unit and the process capability index conversion unit use an index conversion equation to correspondingly convert the quality characteristic data into a plurality of process capability index values. The process capability index analysis unit distinguishes the process capability index value into a standard value and a standard value according to a capability index analysis table. The non-standard value; the factor analysis module uses a factor analysis method to perform factor analysis on the non-standard value to obtain complex process poor factors; the significant factor analysis module sets complex factor levels corresponding to the process poor factors, and the significant factor analysis module The process poor factors are analyzed based on factor levels and using the Taguchi method, so that the process poor factors are divided into a significant factor and a non-significant factor; the optimal parameter analysis module uses a reaction surface method to analyze the poor process factors. Significant influencing factors are analyzed to obtain an optimal process parameter combination value.

One embodiment of the present invention provides a process parameter optimization method for precision parts products, which includes a quality characteristic input step, a process capability index analysis step, a factor analysis step, a significant factor analysis step and an optimal parameter analysis step. . In the quality characteristic input step, a quality characteristic input module is used to provide the user with the ability to input multiple quality evaluation items of a part product, and to provide the user with the ability to set multiple quality characteristic data corresponding to the quality evaluation items; in the process capability index analysis In the step, an index conversion equation of a process capability index module is used to convert the quality characteristic data into a plurality of process capability index values, and a capability index analysis table of the process capability index module is used to divide the process capability index values into one. The standard value and a non-standard value; in the factor analysis step, a factor analysis module is used to perform a factor analysis on the non-standard value using a factor analysis method to obtain the complex process failure factors; in the significant factor analysis step, the A significant factor analysis module is used to set complex factor levels corresponding to the process poor factors. The significant factor analysis module analyzes the process poor factors based on the factor levels and uses the Itaguchi method to classify the process poor factors into one influence. A significant factor and a factor with insignificant influence; in the optimal parameter analysis step, the significant factor is analyzed using a response surface method through an optimal parameter analysis module to obtain an optimal process parameter combination value.

Through this, the present invention analyzes the key factors that affect the machining process capability of the precision spindle of the machine tool, and uses the Taguchi method and the reaction surface method to help find the optimal combination of process parameters for the machining process of the precision spindle of the machine tool. In this way, we can improve the process and more efficiently manufacture the key precision spindle that meets the high precision, high speed and high stability of the workpiece, and at the same time enhance the industrial competitiveness.

In order to facilitate the explanation of the central idea of the present invention expressed in the above creative content column, specific embodiments are hereby expressed. Various objects in the embodiments are drawn according to proportions suitable for enumeration and description, rather than according to the proportions of actual components, and will be described first.

Please refer to FIGS. 1 to 6 , which illustrate a process parameter optimization system and method for precision parts products according to an embodiment of the present invention, which is applied to optimize the process parameters of a precision spindle of a machine tool. The process parameter optimization system 100 for precision parts products includes a mutually coupled quality characteristic input module 10, a process capability index module 20, a factor analysis module 30, a significant factor analysis module 40 and an optimal parameter analysis Mod 50.

The quality characteristic input module 10 allows the user to input multiple quality evaluation items 11 of a part product, and allows the user to correspondingly set multiple quality characteristic data 12 based on the quality evaluation items 11 . In the embodiment of the present invention, the component product is a precision spindle of a machine tool. The quality evaluation item 11 includes spindle length, spindle outer diameter, spindle inner diameter, spindle hardness, spindle concentricity and spindle big end thickness, and quality characteristics. The data 12 includes large-sized quality characteristic data, large-sized quality characteristic data, and small-sized quality characteristic data.

The process capability index module 20 has a process capability index conversion unit 21 and a process capability index analysis unit 22 . The process capability index conversion unit 21 uses an index conversion equation to correspondingly convert the quality characteristic data 12 into complex process capability index values; the process capability index analysis unit 22 distinguishes the process capability index values into a standard according to a capability index analysis table. value and a value that did not meet the standard.

The index conversion equation includes a one-mesh type index conversion equation, a one-mesh large-scale index conversion equation, and a one-mesh small-scale index conversion equation. The process capability index values include a one-mesh type process capability index value, a one-mesh large process capability index value, and one Wang small-sized one. Process capability index value.

The conversion equation of the looking-type indicator is: ;The large-scale indicator conversion equation is ;The small indicator conversion equation is , where C _pk is the value of the large-scale process capability index; C _pl is the value of the large-scale process capability index; C _pu is the value of the small-scale process capability index; μ is the process average; σ is the process standard. Difference; USL is the upper specification limit; LSL is the lower specification limit.

In the embodiment of the present invention, the capability index analysis table is obtained through the following steps:

(1) Since the process capability index values are all defined based on yield, the present invention integrates the t process capability index values based on Yield% (yield) = (3C _pki ) The quality index of precision spindle products is formed, and its formula is: ,in, is the value of the process capability index, is the quality index of the precision spindle product;

(2) Since the process capability index value of each precision spindle is independent of each processing process, the present invention can propose a relationship that can reflect the entire precision spindle product quality index and product yield as follows: : ,in, , is the yield rate;

(3) In order to ensure that the product quality and yield of precision spindles meet the requirements, therefore The following relationships must be met: ;

(4) The present invention can obtain a minimum process capability index value C ₀ based on the relationship obtained in (3). The formula is as follows: ;

(5) The present invention can sort out the capability index analysis table (as shown in Table 1 below) based on the formula obtained in (4). Table 1 t The process performance index value of the entire precision spindle product 0.50 0.75 1.00 1.20 1.25 1.50 1.75 2.00 1 0.5000 0.7500 1.0000 1.2000 1.2500 1.5000 1.7500 2.0000 2 0.6057 0.8345 1.0683 1.2588 1.3068 1.5484 1.7921 2.0372 3 0.6631 0.8810 1.1066 1.2921 1.3390 1.5761 1.8163 2.0586 4 0.7019 0.9129 1.1331 1.3152 1.3614 1.5954 1.8333 2.0737 5 0.7311 0.9370 1.1533 1.3329 1.3786 1.6103 1.8463 2.0854 6 0.7544 0.9564 1.1695 1.3473 1.3925 1.6224 1.8570 2.0948 7 0.7737 0.9725 1.1831 1.3593 1.4041 1.6325 1.8659 2.1028 8 0.7901 0.9862 1.1948 1.3696 1.4142 1.6412 1.8736 2.1097 9 0.8044 0.9983 1.2050 1.3786 1.4229 1.6489 1.8804 2.1157 10 0.8170 1.0089 1.2141 1.3867 1.4308 1.6557 1.8864 2.1211 11 0.8283 1.0185 1.2222 1.3939 1.4378 1.6619 1.8918 2.1260 12 0.8386 1.0271 1.2296 1.4005 1.4442 1.6675 1.8968 2.1304 13 0.8479 1.0350 1.2364 1.4065 1.4501 1.6726 1.9013 2.1345 14 0.8564 1.0423 1.2426 1.4121 1.4555 1.6773 1.9056 2.1383 15 0.8643 1.0490 1.2484 1.4172 1.4605 1.6817 1.9095 2.1418 16 0.8717 1.0553 1.2538 1.4220 1.4652 1.6858 1.9131 2.1451

The factor analysis module 30 uses a factor analysis method to perform factor analysis on the unqualified values to obtain complex process failure factors. In the embodiment of the present invention, the factor analysis method is Fishbone Diagram; the poor process factors include the workpiece soft claw concentricity factor, the center frame concentricity factor, the tool hardness factor and the machining feed factor. .

The significant factor analysis module 40 sets a complex factor level corresponding to the process defect factor. The significant factor analysis module 40 analyzes the process defect factor based on the factor level and uses the Itachiguchi method. The process poor factors are divided into a significant factor and a non-significant factor.

In the embodiment of the present invention, the significant factor analysis module 40 uses the signal-to-noise ratio analysis of the Taguchi method to distinguish the poor process factors into the significant factors and the insignificant factors, and the significant factor analysis The module 40 uses the variation analysis of the Taguchi method to confirm whether the significant influencing factors are correct.

The optimal parameter analysis module 50 uses a response surface method to analyze the significant influencing factors to obtain an optimal process parameter combination value. In the embodiment of the present invention, the optimal parameter analysis module 50 uses the Box-Behnken design of the reaction zone method to analyze the significant influencing factors.

The following is a further explanation of the process parameter optimization method 200 of the precision part product process parameter optimization system 100. As shown in Figure 2, the precision part product process parameter optimization method 200 includes a quality characteristic input step S1 and a process capability. Index analysis step S2, a factor analysis step S3, a significant factor analysis step S4 and an optimal parameter analysis step S5.

In the quality characteristic input step S1, the quality characteristic input module 10 is used to allow the user to input the quality evaluation items 11 of the component product, and to provide the user to set the quality characteristic data 12 correspondingly based on the quality evaluation items 11.

In the embodiment of the present invention, the component product is a precision spindle of a machine tool. The quality evaluation item 11 includes spindle length, spindle outer diameter, spindle inner diameter, spindle hardness, spindle concentricity and spindle big end thickness, and quality characteristics. The data 12 includes the large-sized quality characteristic data, the large-sized quality characteristic data, and the small-sized quality characteristic data.

For example, in the embodiment of the present invention, as shown in Table 2 below, the quality evaluation item 11 is detailed into the spindle length in the cutting process, the spindle outer diameter and spindle inner diameter in the lathe processing process, and the spindle inner diameter in the heat treatment process. There are a total of 8 quality evaluation items including spindle hardness, spindle outer diameter and spindle inner diameter in the grinding process, spindle concentricity and spindle big end thickness in the cutting process11. The user sets the quality characteristic data 12 according to the specifications of each quality evaluation item 11. For example, the spindle length is set as the large-size quality characteristic data, and the spindle hardness is set as the large-size quality characteristic data, etc. Table 2 process quality assessment project Quality Characteristics Data Specifications Cut Spindle length Look for large quality features mm Lathe Spindle outer diameter Look for large quality features mm Spindle inner diameter Eye-catching quality characteristics mm heat treatment Spindle hardness Eye-catching quality characteristics HRC Grind Spindle outer diameter Eye-catching quality characteristics mm Spindle inner diameter Look for large quality features mm cutting Spindle concentricity Look for compact quality features 0.01-0.005mm Spindle big end thickness Eye-catching quality characteristics mm

In the process capability index analysis step S2, the quality characteristic data 12 is correspondingly converted into the process capability index value through the index conversion equation of the process capability index module 20, and the capability of the process capability index module 20 is utilized. The index analysis table divides the process capability index values into the standard value and the non-standard value.

In the embodiment of the present invention, the process capability indicator module 20 converts the target-type quality characteristic data into the process capability index value through the target-type index conversion equation of the indicator conversion equation. The process capability index value of the target type is obtained; the process capability index module 20 converts the size quality characteristic data into the process capability index value through the size index conversion equation of the index conversion equation. The large-scale process capability index value; the process capability index module 20 converts the small-scale quality characteristic data into the small-scale process of the process capability index value through the small-scale indicator conversion equation of the indicator conversion equation. Capability index value.

Among them, the conversion equation of the sight-type indicator is: ;The conversion equation of the large-scale indicator is ;The conversion equation of the small-scale indicator is . Wherein, C _pk is the value of the large-scale process capability index; C _pl is the value of the large-scale process capability index; C _pu is the value of the small-scale process capability index; μ is the process average; σ is the process standard deviation. ;USL is the upper specification limit; LSL is the lower specification limit.

For example, in the embodiment of the present invention, the process capability indicator module 20 is configured in each of the large-scale quality characteristic data, each of the large-scale quality characteristic data and each of the small-scale quality characteristics. From the data, 30 sample data were extracted respectively, and the aforementioned sample data were converted into the process capability index values through the indicator conversion equation, and the obtained process capability index values were organized in Table 3 below. table 3 process quality assessment project Quality Characteristics Data _cpu C _p C _p Cut Spindle length Look for large quality features ─ 1.37 ─ Lathe Spindle outer diameter Look for large quality features ─ 1.37 ─ Spindle inner diameter Eye-catching quality characteristics ─ ─ 1.24 heat treatment Spindle hardness Eye-catching quality characteristics ─ ─ 1.38 Grind Spindle outer diameter Eye-catching quality characteristics ─ ─ 1.37 Spindle inner diameter Look for large quality features ─ 1.38 ─ cutting Spindle concentricity Look for compact quality features 1.40 ─ ─ Spindle big end thickness Eye-catching quality characteristics ─ ─ 1.37

For example, in the embodiment of the present invention, the precision spindle has a total of 8 process capability index values, and the precision spindle product quality index of the precision spindle of the present invention needs to reach 1.2 (i.e. C _T =1.2), so using According to the capability index analysis table (shown in Table 1) and the content of Table 3, the researcher can obtain that the value of each process capability index needs to reach 1.3696 (i.e., C ₀ =1.3696), and thereby obtain the standard value and the stated unmet values. Among them, the standard values include the spindle length in the cutting process, the spindle outer diameter in the lathe processing process, the spindle hardness in the heat treatment process, the spindle outer diameter and spindle inner diameter in the grinding process, and the spindle concentricity in the cutting process. and the thickness of the big end of the spindle; the unqualified value is the inner diameter of the spindle in the lathe machining process.

In the factor analysis step S3, the factor analysis module 30 is used to perform factor analysis on the unqualified value using the factor analysis method to obtain the process defect factor. In the embodiment of the present invention, the factor analysis method is Fishbone Diagram; the poor process factors include the workpiece soft claw concentricity factor, the center frame concentricity factor, the tool hardness factor and the machining feed factor. .

For example, in the embodiment of the present invention, the factor analysis module 30 analyzes the unqualified value (the inner diameter of the spindle in the lathe processing process) through the Fishbone Diagram method, and thereby obtains the result. The process defect factors include the workpiece soft claw concentricity factor, the center frame concentricity factor, the tool hardness factor and the machining feed factor.

In the significant factor analysis step S4, complex factor levels are set corresponding to the process poor factors through the significant factor analysis module 40. The significant factor analysis module 40 analyzes the process based on the factor levels and using the Taguchi method. Analyze the defective factors, thereby distinguishing the process defective factors into the factors with significant influence and the factors with insignificant influence. In the embodiment of the present invention, the significant factor analysis module 40 uses the signal-to-noise ratio analysis of the Taguchi method to distinguish the poor process factors into the significant factors and the insignificant factors, and the significant factor analysis The module 40 uses the variation analysis of the Taguchi method to confirm whether the significant influencing factors are correct.

For example, in the embodiment of the present invention, the significant factor analysis module 40 sets three factor levels for each process defect factor, and the factors are organized as shown in Table 4 below. Table 4 Project Level one Level 2 Level 3 A. Workpiece soft claw concentricity (mm) A1 0.01 A2 0.02 A3 0.03 B. Center frame concentricity (mm) B1 0.015 B2 0.025 B3 0.035 C. Tool hardness/shock resistance (HRC) C1 78 C2 70 C3 65 D. Processing feed (mm) D1 0.2 D2 0.25 D3 0.3

Continuing from the above, the significant factor analysis module 40 is based on the factor levels in Table 4 and uses the L ₉ (3 ⁴ ) orthogonal table in the Taguchi method to organize the obtained data as shown in Table 5 below. table 5 Experiment number Factor A Factor B Factor C Factor D S/N ratio average 1 1 1 1 1 2.72296 2 1 2 2 2 2.66602 3 1 3 3 3 2.68613 4 2 1 2 3 2.55121 5 2 2 3 1 2.28100 6 2 3 1 2 2.08116 7 3 1 3 2 2.21758 8 3 2 1 3 2.18065 9 3 3 2 1 1.80423

Continuing from the above, since the unqualified value (the inner diameter of the spindle in the lathe machining process) is a quality characteristic, the significant factor analysis module 40 can calculate the value based on the content of Table 5 and using a signal-to-noise ratio formula. The signal-to-noise ratio of poor process factors is shown in Figure 3. Among them, the signal-to-noise ratio formula is .

Continuing from the above, the significant factor analysis module 40 can organize the data according to the content of Figure 3 as shown in Table 6 below. Table 6 level A B C D 1 2.692 2.497 2.328 2.269 2 2.304 2.376 2.340 2.322 3 2.067 2.191 2.395 2.473 difference 0.624 0.307 0.067 0.203

Continuing from the above, according to the contents of Table 6, the significant factor analysis module 40 can know that the value of the tool hardness factor (factor C) is less obvious. Therefore, the significant factor analysis module 40 can obtain that the significant influencing factor includes the soft claw concentricity. degree factor, center frame concentricity factor and machining feed factor, and the factor with insignificant influence is the tool hardness factor. And the significant factor analysis module 40 can use the variation analysis of the Taguchi method to sort out the data in Table 7 below, and thereby know that the significant influencing factors indeed include the soft claw concentricity factor and the center frame concentricity factor. and processing feed factor. Table 7 Source of variation degrees of freedom sum of square mean square F value net sum of squares Contribution rate A* 2 0.004479 0.002240 56.58 0.0044 71.36% B* 2 0.00095 0.000475 12 0.000871 14.13% C - - - - - - D* 2 0.000579 0.00029 7.32 0.0005 8.11% combined error 2 0.000079 0.00004 0.00004 7.69% sum 8 0.006087 100.0%

In the optimal parameter analysis step S5, the significant influencing factors are analyzed using the response surface method through the optimal parameter analysis module 50 to obtain the optimal process parameter combination value. In the embodiment of the present invention, the optimal parameter analysis module 50 uses the Box-Behnken design of the reaction zone method to analyze the significant influencing factors.

For example, in the embodiment of the present invention, the optimal parameter analysis module 50 organizes the data on the significant factors according to the factor levels in Table 4 as shown in Table 8 below. Table 8 Experiment number Factor A Factor B Factor D S/N ratio average 1 1 1 1 24.8776 2 1 2 2 25.9691 3 1 3 3 18.3455 4 2 1 3 27.5904 5 2 2 1 22.7070 6 2 3 2 18.1888 7 3 1 2 21.2339 8 3 2 3 19.8855 9 3 3 1 20.9543

Continuing from the above, the optimal parameter analysis module 50 analyzes the data content in Table 8 using the Box-Behnken design of the reaction zone method, and organizes the analysis results as shown in Table 9 below. Among them, A is the workpiece soft claw concentricity factor, B is the center frame concentricity factor, C is the processing feed factor, A*B, A*C, B*C are the analysis factors to see whether there is interaction, A*A, B *B and C*C are the discriminant factors for whether there is inverse curvature for quality. Table 9 Source degrees of freedom SS MS F P Return 9 161.316 17.924 10.56 0.009 Linear A 1 14.761 14.761 27.63 0.003 B 1 46.913 46.9132 1.08 0.347 C 1 1.832 1.8317 10.8 0.013 square A*A 1 6.48 9.1648 8.4 0.068 B*B 1 0.29 0.000283 0.017 0.998 C*C 1 48.22 48.22 28.4 0.003 interaction A*B 1 2.411 2.4108 1.42 0.287 A*C 1 6.93 6.93 4.08 0.099 B*C 1 33.479 33.479 19.72 0.007 Error 5 8.49 total 14 169.806 Coefficient of determination R 86%

Continuing from the above, as shown in Figures 4 to 6, the optimal parameter analysis module 50 can draw a contour diagram of the interaction of various factors in the spindle deep hole machining process based on the contents of Table 9, and find out all the factors on the contour diagram. The optimal combination of process parameters. Among them, as shown in Figures 4 to 6, the optimal process parameter combination values that can be obtained are the workpiece soft claw concentricity factor equal to 0.0925mm, the center frame concentricity factor equal to 0.015mm, and the processing feed factor equal to 0.3mm.

Continuing from the above, according to the contents of Table 10 below, the processing capability of the spindle inner diameter in the lathe processing process is significantly improved through the combination of the optimal process parameter combination values. Table 10 quality assessment project Specifications Quality Characteristics Data _cpu C _p C _p Before improvement Lathe Spindle inner diameter 37.5 mm Look at your eyes ─ ─ 1.24 After improvement Lathe Spindle inner diameter 37.5 mm Look at your eyes ─ ─ 1.41

Through this, the present invention analyzes the key factors that affect the machining process capability of the precision spindle of the machine tool, and uses the Taguchi method and the reaction surface method to help find the optimal combination of process parameters for the machining process of the precision spindle of the machine tool. In this way, we can improve the process and more efficiently manufacture the key precision spindle that meets the high precision, high speed and high stability of the workpiece, and at the same time enhance the industrial competitiveness.

Although the present invention has been described with a preferred embodiment, those skilled in the art can make various modifications without departing from the spirit and scope of the present invention. The above embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. All modifications or changes that do not violate the spirit of this creation are within the scope of the patent application for this creation.

100: Process parameter optimization system for precision parts products

200: Process parameter optimization method for precision parts products

10:Quality characteristic input module

11:Quality Assessment Project

12:Quality characteristics information

20: Process capability indicator module

21: Process capability index conversion unit

22: Process capability index analysis unit

30: Factor analysis module

40: Significant factor analysis module

50: Best parameter analysis module

S1: Quality characteristics input step

S2: Process capability index analysis steps

S3: Factor analysis steps

S4: Significant factor analysis steps

S5: Optimal parameter analysis steps

[Fig. 1] is a schematic block diagram of the system of the present invention. [Fig. 2] is a schematic flow diagram of the method of the present invention. [Figure 3] is a schematic diagram of the S/N ratio factor effect of each process defect factor of the present invention. [Fig. 4] is a contour diagram of the center frame concentricity factor-workpiece soft claw concentricity factor and processing feed factor of the present invention. [Fig. 5] is a contour diagram of the processing feed factor of the present invention - the workpiece soft claw concentricity factor and the center frame concentricity factor. [Figure 6] is a contour diagram of the processing feed factor of the present invention - the center frame concentricity factor and the workpiece soft claw concentricity factor.

100: Process parameter optimization system for precision parts products

10:Quality characteristic input module

11:Quality Assessment Project

12:Quality characteristics information

20: Process capability indicator module

21: Process capability index conversion unit

22: Process capability index analysis unit

30: Factor analysis module

40: Significant factor analysis module

50: Best parameter analysis module

Claims (8)

A process parameter optimization system for precision parts products, which is used to optimize the process parameters of precision spindles of machine tools. The process parameter optimization system includes: a quality characteristic input module for users to input multiple qualities of a part product Evaluation items, and allow users to set multiple quality characteristic data based on these quality evaluation items. Among them, the part product is a precision spindle of a machine tool. These quality evaluation items include spindle length, spindle outer diameter, spindle inner diameter, spindle Hardness, spindle concentricity and spindle big-end thickness, and the quality characteristic data include one mesh-type quality characteristic, one large-scale quality characteristic and one small-scale quality characteristic; a process capability indicator module coupled to the quality characteristic input module The process capability index module has a process capability index conversion unit and a process capability index analysis unit. The process capability index conversion unit uses an index conversion equation to correspondingly convert the quality characteristic data into complex process capability index values. The process capability index analysis unit distinguishes the process capability index values into a standard value and a non-standard value according to a capability index analysis table; a factor analysis module coupled to the process capability index module, the factor analysis module The group uses a factor analysis method to perform factor analysis on the unqualified value to obtain multiple process failure factors; a significant factor analysis module is coupled to the factor analysis module, and the significant factor analysis module corresponds to the factors The process poor factors set complex factor levels. The significant factor analysis module analyzes these process poor factors based on these factor levels and uses the Taguchi method to classify these process poor factors into one with significant influence. factors and a factor with insignificant influence; and an optimal parameter analysis module coupled to the significant factor analysis module. The optimal parameter analysis module uses a response surface method to analyze the significant factor, so as to Obtain an optimal process parameter combination value. The process parameter optimization system for precision parts products as described in request item 1, wherein the index conversion equation includes a one-size-fits-all index conversion equation, a one-size large index conversion equation and a one-size small index conversion equation, and the process capability index values include one one-size-fits-all index conversion equation. Mesh type process capability index value, Yiwang large process capability index value and Yiwang small process capability index value. The process parameter optimization system for precision parts products as described in claim 2, wherein the visual index conversion equation is:

, the large-scale index conversion equation is

, the small-scale indicator conversion equation is

, where C _pk is the value of the large-scale process capability index; C _pl is the value of the large-scale process capability index; C _pu is the value of the small-scale process capability index; μ is the process average; σ is the process standard deviation; USL is the upper specification limit; LSL is the lower specification limit. The process parameter optimization system for precision parts products as described in claim 1, wherein the significant factor analysis module uses the signal-to-noise ratio analysis of the Taguchi method to distinguish the poor process factors into the significant factor and the impact factor. There is no significant factor, and the significant factor analysis module uses the variation analysis of the Taguchi method to confirm whether the significant factor is correct. A process parameter optimization method for precision parts products, which is used to optimize the process parameters of a precision spindle of a machine tool. The process parameter optimization method includes: a quality characteristic input step: through a quality characteristic input module, the user is provided to input a Multiple quality assessment items for component products, and provide users with the ability to set multiple quality characteristic data based on these quality assessment items. The component product is a precision spindle for a machine tool. These quality assessment items include spindle length, spindle outer diameter , spindle inner diameter, spindle hardness, spindle concentricity and spindle big end thickness, and these quality characteristics data include one type of quality characteristics data, one type of large quality characteristics data and one type of small quality characteristics data; A process capability index analysis step: convert the quality characteristic data into complex process capability index values through an index conversion equation of a process capability index module, and use a capability index analysis table of the process capability index module to convert the quality characteristic data into complex process capability index values. Some process capability index values are divided into a standard value and a non-standard value; 1. Factor analysis step: use a factor analysis module to use a factor analysis method to perform factor analysis on the non-standard value to obtain multiple process poor factors; 1. Significant factor analysis step: Set complex factor levels corresponding to these process defect factors through a significant factor analysis module. The significant factor analysis module analyzes these process defect factors based on these factor levels and uses the Itachiguchi method. , in order to distinguish these process poor factors into a significant factor and a non-significant factor; and an optimal parameter analysis step: use a best parameter analysis module to use a response surface method to analyze the significant factor. Analysis to obtain an optimal combination of process parameters. The process parameter optimization system for precision parts products as described in claim 5, wherein the index conversion equation includes a 1-mesh type index conversion equation, a 1-mesh large-scale index conversion equation and a 1-millimeter small-scale index conversion equation, and the process capability index values include a 1-mesh index conversion equation. Mesh type process capability index value, Yiwang large process capability index value and Yiwang small process capability index value. The process parameter optimization system for precision parts products as described in claim 6, wherein the visual index conversion equation is:

, the large-scale index conversion equation is

, the small-scale indicator conversion equation is

, where μ is the process average; σ is the process standard deviation; USL is the upper specification limit; LSL is the lower specification limit. The process parameter optimization system for precision parts products as described in claim 5, wherein the significant factor analysis module uses the signal-to-noise ratio analysis of the Taguchi method to distinguish the process poor factors. These are the significant factor and the insignificant factor, and the significant factor analysis module uses the variation analysis of the Taguchi method to confirm whether the significant factor is correct.