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Enhancing conformance of injection blow molding by integrating machine learning modeling and Taguchi parameter design. (English) Zbl 1423.62163
Summary: This study proposes an integrated parameters optimization methodology for the single-stage injection blowing molding process. The methodology aims at maximizing conformance percentages of the process output. The injection pressure, cure time, blow air delay, melting temperature, first blow air, and the neck, center and bottom temperatures of the cooling water that circulates around the mold at the neck, center and bottom positions, respectively; were regarded as the operational process parameters. Machine learning algorithms were used to build an approximate function relationship between conformance percentages and operational parameters, thereafter; Taguchi parameter design was adopted to set the optimal values of parameters. The proposed methodology was implemented for the production of high density polyethylene bottles and the results showed significant enhancements in the conforming percentages of the injected blown molded bottles.
MSC:
62P30 Applications of statistics in engineering and industry; control charts
62P35 Applications of statistics to physics
68T05 Learning and adaptive systems in artificial intelligence
62K25 Robust parameter designs
Software:
Orange
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References:
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