A structured approach is fundamental in designing a complex multivariable process, while achieving specified product quality. Nominal values of process parameters of an injection molding process are defined by implementing DOE methodology. Optimisation of experimental phase is achieved by progressive information acquiring concerning influential input factors and DOE design. Hence, most influential machine process parameters are varied using a non-standard fraction-factorial design. A linear regression model with included elements of second order interaction is defined based on obtained data. Additional testing of its validation is in order before reaching a final conclusion. Firstly, model significance is tested by conducting an ANOVA analysis. Only significant models prove that DOE has been adequately planned and factors which affect the controlled output have been chosen. Finally, DOE is completed by adequacy analysis of the regression model. Lack-of-fit test is chosen to test whether the model is a proper representation of the real process.
Application of statistical methods in quality improvement of molded parts is presented in this paper. Implementation of two stages of DMAIC improvement cycle in a pre-production process is analysed in detail. DOE method is performed to define nominal values of process parameters of injection molding process. A fraction-factorial design with a single central point is used. A linear mathematical model with included elements of second-order interaction is defined. Finally, control stage is performed using prior defined nominal values. Process capability test is conducted in order to determine whether the process is capable of producing parts within specified tolerance field.
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