Advances and Applications in Statistics
Volume 53, Issue 5, Pages 519 - 535
(November 2018) http://dx.doi.org/10.17654/AS053050519 |
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ENHANCING CONFORMANCE OF INJECTION BLOW MOLDING BY INTEGRATING MACHINE LEARNING MODELING AND TAGUCHI PARAMETER DESIGN
Safwan Altarazi
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Abstract: 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. |
Keywords and phrases: injection blow molding, Taguchi parameter design, machine learning modeling, process parameters optimization.
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