CN105117515A - Complicated plastic parts moulding secondary optimization method - Google Patents
Complicated plastic parts moulding secondary optimization method Download PDFInfo
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Abstract
The invention discloses a complicated plastic parts moulding secondary optimization method. According to features of an injection moulded part, an injection moulding scheme is determined; process description in the injection moulding scheme is subjected to orthogonal experiment; the orthogonal experiment is subjected to CAE analysis; a CAE analysis result is subjected to multi-target analysis; comprehensive indexes of buckling deformation and volume shrinkage of the injection moulded part are obtained; the comprehensive indexes of the injection moulded part are subjected to variance analysis; primary process parameters influencing the experiment are obtained; control variable experiment is performed according to the obtained primary process parameters; secondary optimization parameters are determined; simulated verification is performed with parameter values; and an optimal parameter combination is determined. According to the method, the problems of high time consumption and non-obvious optimization effect due to the fact that process parameters of the injection moulded part are simply optimized once depending on production experience of workers are solved; and a secondary optimization scheme determined through the scheme can better ensure the production quality of the injection moulded part.
Description
Technical field
The present invention relates to injection moulding production technical field, especially a kind of complicated plastic parts moulding double optimization method.
Background technology
At present, along with the development of processing manufacturing industry, the requirement of people to injecting products quality is more and more higher, and especially the more complicated plastic of feature, easily produces defect when shaping.A lot of enterprise still relies on the knowhow of workman to be tested to the technological parameter of moulding at present, only once simply optimizes, and consuming time more, is difficult to obtain preferably technological parameter for different parts.
Summary of the invention
The object of the invention is: a kind of complicated plastic parts moulding double optimization method is provided, the technological parameter it solving moulding relies on the knowhow of workman once simply to optimize, consuming time more, the problem of more excellent DeGrain, to overcome the deficiencies in the prior art.
The present invention is achieved in that complicated plastic parts moulding double optimization method, and according to the feature determination injection moulding scheme of injection-moulded plastic part, the technological parameter in injection moulding scheme is carried out orthogonal test, and the index of orthogonal test is: buckling deformation amount and volumetric shrinkage; Again cae analysis is carried out to each group orthogonal test, again the result of cae analysis is carried out multiobjective analysis, obtain the buckling deformation amount of injection-moulded plastic part and the overall target of volumetric shrinkage, again method of analysis of variance is carried out to the overall target of plastic buckling deformation amount and volumetric shrinkage, the main technologic parameters of the impact test drawn, control variable test is carried out according to the main technologic parameters obtained, determine the optimal value of the parameter of major influence factors, finally determine double optimization parameter combinations, and CAE simplation verification is carried out to double optimization parameter.
Owing to have employed technique scheme, compared with prior art, the present invention devises a set of simple plastic parts moulding double optimization scheme, and the technological parameter it solving moulding relies on the knowhow of workman once simply to optimize, consuming time more, the problem of more excellent DeGrain.The double optimization scheme determined by the program can ensure the quality of production of injection-moulded plastic part preferably.The present invention is simple, and result of use is good.
Accompanying drawing explanation
Accompanying drawing 1-3 is the structural drawing of the plastic of embodiments of the invention;
Accompanying drawing 4, accompanying drawing 5 are the results of analysis of variance figure of embodiments of the invention;
Accompanying drawing 6, accompanying drawing 7 are the comprehensive evaluation value broken line graph of embodiments of the invention;
Shrinkage factor analysis chart when accompanying drawing 8 is the plastic parts moulding of embodiments of the invention;
Amount of warpage analysis chart when accompanying drawing 9 is the plastic parts moulding of embodiments of the invention.
Embodiment
Embodiments of the invention: complicated plastic parts moulding double optimization method, for automobile lamp switch assembly parts:
1 Analysis of Forming Process
As shown in Figure 1, 2, the largest contours of plastic is of a size of 75mm × 67mm × 33mm to the structure of plastic, belongs to middle-size and small-size plastic, and average wall thickness is 2mm; The wall thickness of plastic is thinner, and minutia is more, plastic feature more complicated.
1.1 design injection moulding schemes
The material of plastic selects ABS plastic, and rule of thumb design running gate system and cooling system, running gate system adopts a mould two pieces, side gate and common flow passage injection form; Cooling duct diameter elects 8mm as, and flowing coolant water temperature is 25 DEG C, pipeline distance product surface 16mm; Injection moulding scheme as shown in Figure 3.
2 orthogonal tests
2.1 arrange empirical factor water-glass
In order to comparatively comprehensively analyze the affecting laws of molding proces s parameters to plastic warpage, have chosen following main technologic parameters in an experiment: (a) mold temperature, (b) melt temperature, (c) stuffing pressure, (d) dwell time, (e) cool time, (f) inject time, (g) moulding material.Each factor arranges three respectively affects level, as shown in table 1.
2.2 multi-index optimization
The influence degree of different tests index to plastic part quality is different, and its dimension is also inconsistent, test figure directly can not be superposed and evaluate.The π membership function less than normal of type distribution in ridge in application fuzzy mathematics, each desired value is all mapped to interval [0,1 ］ on, make the simplification of multi objective problem.Require according to the combination property of plastic and each index to the influence degree of plastic parts moulding quality, be weighted Comprehensive Evaluation by centesimal system, the weighted value of volumetric shrinkage and amount of warpage gets 40 and 60 respectively, and weighted synthetical evaluation value is as shown in table 2.
2.2 arrange orthogonal test table
Experimentally the related data of factor level table, selects in Orthogonal Experiment and Design
l 18 (3
7)type orthogonal test table, do not consider each factor reciprocation, testing program is as shown in table 2.
Note: GF20, SK30, UT10B are the plastics of ABS series.
3 data result optimums
3.1 variance analysis
The error size that method of analysis of variance can be estimated in test and certainly exist in test findings mensuration, compensate for the shortcoming of intiutive analysis method, can determine influence degree and the conspicuousness of each factors on test indicators simultaneously.Application variance analysis formula, calculates analysis of variance table, as shown in table 3.
3.2 Optimization analyses
According to the production and application requirement of the range analysis result in table 2 and plastic, the primary and secondary order of plastic combination property influence factor is: g>c>f>eGreatT. GreaT.GTa>d>b, order is consistent with F value size order in variance analysis, and namely variance method analysis is consistent with the result of range analysis.Therefore, determine that the optimal combination of technological parameter is: a2, b3, c3, d2, e3, f1, g1, mold temperature: 60 DEG C, melt temperature: 260 DEG C, dwell pressure: 120Mpa, dwell time: 15s, cool time: 30s, inject time: 2s, moulding material: GF20UMGABSLtd.The shrinkage factor of carrying out the plastic of sunykatuib analysis in MoldFlow is 5.369%, and amount of warpage is 0.0949, and comprehensive evaluation value is: 37.88, reaches optimal effectiveness.Result as shown in Figure 4,5.
4 double optimization tests
4.1 choose major influence factors carries out control variable test
According to the data of table 3, F (g)=45.896>F0.01 (2,5)=13.27, F (g), F (c) are all greater than F0.05 (2,5)=5.79, the F value of other factors is all less than F0.1 (2,5)=3.78, the known principal element affecting plastic combination property is moulding material and dwell pressure (c), and other factors are minor effect factor.Elect moulding material (g) as GF20, therefore affecting larger factor to part buckling deformation is dwell pressure (c), carries out first time control variable test to (c) factor, c () is centered by 120Mpa, spacing is 5Mpa, and maximal value is taken as 135Mpa, and test figure as shown in Table 4.By this test, tentatively determine the Optimal Parameters value of (c).
According to control variable test, utilize MoldFlow software to carry out sunykatuib analysis, and draw its broken line graph 6 according to comprehensive evaluation value, by this test, tentatively determine that the Optimal Parameters value of (c) is 130Mpa.
4.2 second time control variable tests
As seen from the figure, when (C) is for 130Mpa, the amount of warpage of part is minimum, in order to determine the Optimal Parameters value of (C) particularly, choosing spacing is 1Mpa, centered by 130Mpa, choose (C) respectively for 126Mpa, 127Mpa, 128Mpa, 129Mpa, 130Mpa, 131Mpa, 132Mpa, 133Mpa, 134Mpa and carry out simulation test, and determine optimal value of the parameter.As shown in table 5:
According to control variable test, utilize MoldFlow software to carry out sunykatuib analysis, and draw its broken line graph 7 according to comprehensive evaluation value, by this test, finally determine that the Optimal Parameters value of (c) is 132Mpa.Shrinkage factor during plastic parts moulding is 5.228%, and as shown in Figure 8, amount of warpage is 0.0818, and as shown in Figure 9, comprehensive evaluation value is: 34.969, and compared with the optimal value determined with orthogonal test, amount of warpage reduces 7.68%;
Conclusion: use orthogonal test and comprehensively analyze plastic parts moulding quality in conjunction with Multicriteria analysis, uses method of analysis of variance to determine to affect secondary factors and the first time Optimal Parameters combination of plastic parts moulding quality to experimental result analysis.Use control variable test to be optimized major parameter, finally obtain double optimization parameter combinations.Through double optimization, the comprehensive evaluation value of plastic improves 7.68% than a suboptimization.
Claims (1)
1. a complicated plastic parts moulding double optimization method, is characterized in that: according to the feature determination injection moulding scheme of injection-moulded plastic part, the technological parameter in injection moulding scheme is carried out orthogonal test, and the index of orthogonal test is: buckling deformation amount and volumetric shrinkage; Again cae analysis is carried out to each group orthogonal test, again the result of cae analysis is carried out multiobjective analysis, obtain the buckling deformation amount of injection-moulded plastic part and the overall target of volumetric shrinkage, again method of analysis of variance is carried out to the overall target of plastic buckling deformation amount and volumetric shrinkage, the main technologic parameters of the impact test drawn, control variable test is carried out according to the main technologic parameters obtained, determine the optimal value of the parameter of major influence factors, finally determine double optimization parameter combinations, and CAE simplation verification is carried out to double optimization parameter.
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Cited By (5)
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| CN106055787A (en) * | 2016-05-30 | 2016-10-26 | 广西科技大学 | Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network |
| CN107742048A (en) * | 2017-11-10 | 2018-02-27 | 贵州大学 | A kind of re-optimization method of overvoltage protector gold thread skew technological parameter |
| CN109721209A (en) * | 2018-11-05 | 2019-05-07 | 杭州司迈特水处理工程有限公司 | A kind of wastewater treatment method based on MAP and biochemical method combination removal ammonia nitrogen |
| CN116021735A (en) * | 2022-12-07 | 2023-04-28 | 南京晟铎科技有限公司 | Injection molding product parameter simulation detection system and method |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447219A (en) * | 2015-07-28 | 2016-03-30 | 贵州大学 | Twice optimizing method for plastic part molding |
| CN106055787A (en) * | 2016-05-30 | 2016-10-26 | 广西科技大学 | Automotive trim panel injection moulding technology based on BP (Back Propagation) neural network |
| CN107742048A (en) * | 2017-11-10 | 2018-02-27 | 贵州大学 | A kind of re-optimization method of overvoltage protector gold thread skew technological parameter |
| CN109721209A (en) * | 2018-11-05 | 2019-05-07 | 杭州司迈特水处理工程有限公司 | A kind of wastewater treatment method based on MAP and biochemical method combination removal ammonia nitrogen |
| CN116021735A (en) * | 2022-12-07 | 2023-04-28 | 南京晟铎科技有限公司 | Injection molding product parameter simulation detection system and method |
| CN116021735B (en) * | 2022-12-07 | 2023-09-19 | 南京晟铎科技有限公司 | Injection molding product parameter simulation detection system and method |
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