Predictive Methodology for Quality Assessment in Injection Molding Comparing Linear Regression and Neural Networks

The use of recycled polypropylene in industry to reduce environmental impact is increasing. Design for manufacturing and process simulation is a key stage in the development of plastic parts. Traditionally, a trial-and-error methodology is followed to eliminate uncertainties regarding geometry and process. A new proposal is presented, combining simulation with the design of experiments and creating prediction models for seven different process and part quality output features. These models are used to optimize the design without developing additional time-consuming simulations. The study aims to compare the precision and correlation of these models. The methods used are linear regression and artificial neural network (ANN) fitting. A wide range of eight injection parameters and geometry variations are used as inputs. The predictability of nonlinear behavior and compensatory effects due to the complex relationships between this wide set of parameter combinations is analyzed further in the state of the art. Results show that only Back Propagation Neural Networks (BPNN) are suitable for correlating all quality features in a single formula. The use of prediction models accelerates the optimization of part design, applying multiple criteria to support decision-making. The methodology is applied to the design of a plastic support for induction hobs. Furthermore, this methodology has demonstrated that a weight reduction of 27% is feasible. However, it is necessary to combine process parameters that differ from the standard ones with a non-uniform thickness distribution so that the remaining injection parameters, material properties, and dimensions fall within tolerances.

Geometry Optimization of the Runner Insert for Improving the Appearance Quality of the Injection Molded Auto Part

Appearance quality is one of the most important indexes for many injection-molded products, like optical parts, automotive parts etc., especially at the area near the injection gate. Different from the work that focused on designing the dimensions of the runner, this work proposed a method which is based on the insert technology to improve the appearance quality of a standard automotive part. An insert was introduced into the runner system which located before the gate. Three different shapes of this insert (circular, rectangular and diamond) were used to study the effect of geometrical factors on the appearance quality in this paper. All inserts were parameterized to describe their location and dimensions. Based on the geometrical design parameters, expected improvement optimization problem about the appearance quality were solved by using sequential approximate optimization method. The appearance qualities of three cases are improved by 13.77%, 21.56%, 14.37% respectively. Results showed that the best geometrical design scheme of the insert is rectangular with the optimal geometrical location and dimensions. The reasons were discussed by investigating the flow and thermal history in detail. Compared with the design case without any insert, the heat was absorbed and the velocity field was changed by the insert before the polymer melts ran into the cavity. It changed the complicated thermo-mechanical history inside the material during the entire processing history, which improved the final appearance quality of this auto part.

Comparison of Numerical and Experimental Data in Multi-objective Optimization of a Thermoplastic Molded Part

Warpage reduction, dimensional tolerance accomplishment and time and cost saving are some of the most important problems in the injection molding production process. In this study, a real case of a thermoplastic injected part is analyzed. The mold for a pipeline connecting element has been designed, according to known technical and economical criterion to fulfill customer requirements. Particularly for this element, warpage of a specified surface and two diameter sizes are fundamental for correct part functionality and assembling. The analysis is centered on the effects of four process parameters, i. e. packing pressure, packing time, melt temperature and cooling time that heavily influence final results. A finite element model has been used to evaluate their effects on four important final objectives that are the geometrical entities and production cycle time. Each variable has been varied into its proper range, in accordance with a central composite design DoE plan; 25 simulations have been executed and results have been represented using response surfaces. Pareto Front for above listed objectives has been extracted using a genetic algorithm and the best set of parameters has been determined after application of specific selection criteria and a weighted objective function. After numerical evaluation, the CCD DoE plan has been experimentally repeated. Results have been measured on real components, and then represented with response surfaces as well. The same algorithm and objective function have been used for optimization, to determine the experimental optimum parameter set. Finally, the two parameter sets have been compared.