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

ABSTRACT: 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 va

SUBMITTER: Fernandez A 

PROVIDER: S-EPMC10575229 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature