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Six Sigma-Based Optimization of Shrinkage Accuracy in Injection Molding Processes

机译:基于六西格玛的注射成型工艺收缩率优化

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This paper focuses on using six sigma methodologies to reach the desired shrinkage of a manufactured high-density polyurethane (HDPE) part produced by the injection molding machine. It presents a case study where the correct shrinkage is required to reduce or eliminate defects and to improve the process capability index Cp and Cpk for an injection molding process. To improve this process and keep the product within specifications, the six sigma methodology, design, measure, analyze, improve, and control (DMAIC) approach, was implemented in this study. The six sigma approach was paired with the Taguchi methodology to identify the optimized processing parameters that keep the shrinkage rate within the specifications by our customer. An L9 orthogonal array was applied in the Taguchi experimental design, with four controllable factors and one non-controllableoise factor. The four controllable factors identified consist of the cooling time, melt temperature, holding time, and metering stroke. The noise factor is the difference between material brand 1 and material brand 2. After the confirmation run was completed, measurements verify that the new parameter settings are optimal. With the new settings, the process capability index has improved dramatically. The purpose of this study is to show that the six sigma and Taguchi methodology can be efficiently used to determine important factors that will improve the process capability index of the injection molding process.
机译:本文着重于使用六种sigma方法来实现由注射成型机生产的高密度聚氨酯(HDPE)零件的所需收缩率。它提供了一个案例研究,其中需要正确的收缩率以减少或消除缺陷并改善注塑工艺的工艺能力指数Cp和Cpk。为了改进此过程并使产品保持在规格范围内,本研究实施了六西格玛方法,设计,测量,分析,改进和控制(DMAIC)方法。六西格玛方法与Taguchi方法相结合,可以确定优化的加工参数,从而将收缩率保持在客户要求的规格范围内。在田口实验设计中采用了L9正交阵列,具有四个可控因素和一个不可控/噪声因素。确定的四个可控因素包括冷却时间,熔体温度,保持时间和计量冲程。噪声系数是物料品牌1和物料品牌2之间的差异。确认运行完成后,测量将验证新参数设置是否最佳。通过新设置,过程能力指数得到了显着提高。这项研究的目的是表明六西格玛和田口方法可以有效地用于确定重要因素,这些因素将改善注塑工艺的工艺能力指数。

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