Injection molding industry is one of the crucial cogs of modern manufacturing and represents the classic batch process production. Traditionally, many parameter settings during production are tuned by sophisticated technicians before production. High experience-dependency and tuning cost inhibit the further development of injection molding. To realize the automation of parameter optimization tuning in injection molding process, this thesis proposes different methods to optimize parameter settings corresponding to different process phases.

For filling and packing phases in injection molding process, a production profit model is proposed for optimizing process parameters in order to improve the production profit and reduce energy cost with guaranteed product quality. Based on the...[ Read more ]

Injection molding industry is one of the crucial cogs of modern manufacturing and represents the classic batch process production. Traditionally, many parameter settings during production are tuned by sophisticated technicians before production. High experience-dependency and tuning cost inhibit the further development of injection molding. To realize the automation of parameter optimization tuning in injection molding process, this thesis proposes different methods to optimize parameter settings corresponding to different process phases.

For filling and packing phases in injection molding process, a production profit model is proposed for optimizing process parameters in order to improve the production profit and reduce energy cost with guaranteed product quality. Based on the data obtained from the experiment utilizing Response Surface Design method, an artificial neural network (ANN) is developed to describe the complicated and highly nonlinear relationship between process parameters and optimization objective in injection molding process. During optimization process genetic algorithm (GA) is applied to maximize production profit based on the established ANN model. Experiment result demonstrates that production profit could be maximized through parameter tuning. Since optimization result more approaching to lower boundary of satisfied interval, it indicates that optimization process obtains highest production profit energy based on cost and material cost reduction with the lowest quality satisfaction as searching standard. Sensitivity analysis of different model parameters demonstrates that pass rate has a greater influence on final optimized result than price of each product and unit price of material. With actual value of all model parameters when in application, this kind of parameter optimization model and counterpart method can maximize production profit with satisfied product quality.

In mold clamping phases, the 5-joint double-toggle mechanical structure of injection machine is analyzed. Due to the non-linear motion characteristic of moving plate, a motion model based on mold open process of moving plate is established and experimental result figured out different effects on time by counterpart parameter settings. In addition, according to the features of mold opening, optimization process is specified into four standard procedures for computer programing. At last, an innovation production optimization efficiency model is proposed to redefine working procedure for actual industrial process. Experiment result well proves the feasibility of proposed optimization flow paths. Simultaneously, optimization sequence tuning through production optimization efficiency model has a good performance in saving experimental cost before real production.