Quality and Defect Prediction in Plastic Injection Molding using Machine Learning Algorithms based Gating Systems and Its Mathematical Models

To achieve high quality products from Plastic Injection Molding (PIM) process it is very essential to identify the defective operations in automatic manner which is most challenging task. This paper proposes a Machine Learning (ML) approach to detect the complex faults occurrence during the PIM process. During initial sampling process of molding to achieve high quality and low time consumption it is essential to concentrate on the suitable determination of parameter values by considering the properties of injection molding process. For that purpose, a novel machine learning algorithms based gating system is introduced in PIM (MLGS-PIM). Technical evaluation can be done using simulation which combines the CATIA and MATLAB. Therefore in MLGS-PIM, a holistic approach is introduced to improve and predict the process quality of the parameters which is based on machine learning approaches. The considered machine learning approaches for this process are Artificial Neural Network (ANN) and Support Vector Machine (SVM). This two learning models are combined to achieve high quality under various conditions. Such novel ML based technique helps to increase the quality characteristics of the injection molding process and it is predicted with various parameter values where the simulation data and measurements are handled in an intelligent manner. The materials which are considered in the PIM process are thermoplastic polystyrene, thermoplastic acrylonitrile butadiene styrene and thermoplastic polyvinyl chloride where three types are gating systems are applied with it and consists of 3, 4 and 5 gates and as well the parameters which are measured for the output analysis are sum rate, bit error rate and convergence plot. The results show that the performance of the proposed MLGS-PIM approach significantly increases the performance when compared with the earlier approaches such as AntLion Optimization and PSO-MSQPA

Machine learning methods for quality prediction in thermoplastics injection molding

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksNowadays, competitiveness is a reality in all industrial fields and the plastic injection industry is not an exception. Due to the complex intrinsic changes that the parameters undergo during the injection process, it is essential to monitor the parameters that influence the quality of the final part to guarantee a superior quality of service provided to customers. Quality requirements impose the development of intelligent systems capable to detect defects in the produced parts. This article presents a first step towards building an intelligent system for classifying the quality of produced parts. The basic approach of this work is machine learning methods (Artificial Neural Networks and Support Vector Machines) and techniques that combine the two previous approaches (ensemble method). These are trained as classifiers to detect conformity or even defect types in parts. The data analyzed were collected at a plastic injection company in Portugal. The results show that these techniques are capable of incorporating the non-linear relationships between the process variables, which allows for a good accuracy ( ˜ 99%) in the identification of defects. Although these techniques present good accuracy, we show that taking into account the history of the last cycles and the use of combined techniques improves even further the performance. The approach presented in this article has a number of potential advantages for online predicting of parts quality in injection molding processes.This work was partially supported by the Spanish State Research Agency through the project CHLOE-GRAPH (PID2020-118649RB-l00) and by FCT—Portuguese Foundation for Science and Technology under project grant UIDB/00308/2020.Postprint (author's final draft

Warpage optimization of a name card holder using neural network model

Injection molding has become widely process that used in plastic manufacturing. To produce high quality product, it has to consider the process condition. In this study, optimum parameters for injection molding of a name card holder are determined. Finite element software MoldFlow, statistical design of experiment and artificial neural network are used in finding optimum value. The process parameter influencing warpage is determined using finite element software based on data using full factorial design. By exploiting finite element analysis result, a predictive model using artificial neural network is created. Optimum value is determined by comparing result by using finite element analysis and optimization using artificial neural network and choose the smallest percentage of error

COMPOSITES 4.0: ENABLING THE MODERNIZATION OF LEGACY MANUFACTURING ASSETS IN SOUTH CAROLINA

Composites 4.0 is the implementation of Industry 4.0 concepts to plastics and composites manufacturing with the goal to overcome the complexities associated with these materials. Due to very complex process-structure-property relationships associated with plastics and composites, a wide range of process parameters need to be tracked and monitored. Furthermore, these parameters are often affected by the tool and machinery, human intervention and variability and should thus, be monitored by integrating intelligence and connectivity in manufacturing systems. Retrofitting legacy manufacturing systems with modern sensing and control systems is emerging as one of the more cost-effective approaches as it circumvents the substantial investments needed to replace legacy equipment with modern systems to enhance productivity. The goal of the following study is to contribute to these retrofitting efforts by identifying the current state-of-the-art and implementation level of Composites 4.0 capabilities in the plastics and composites manufacturing industry. The study was conducted in two phases, first, a detailed review of the current state-of-the-art for Industry 4.0 in the manufacturing domain was conducted to understand the level of integration possible. It also helped gain insights into formulating the right questions for the composites manufacturing industry in South Carolina. Second, a survey of the plastics and composites manufacturing industries was performed based on these questions, which helps identify the needs of the industry and the gap in the implementation of Composites 4.0. The study focuses on the three leading composite manufacturing industries: injection molding, extrusion, and 3D printing of thermoset and thermoplastic materials. Through the survey, it was possible to identify focus areas and desired functionalities being targeted by the industries surveyed and concentrate research efforts to develop targeted solutions. After analyzing the survey responses, it was found that updating old protocols using manufacturer support and customized integration of cost-effective solutions like retrofit kits, edge gateways, and smart sensors were identified as best-suited solutions to modernize the equipment. Composites 4.0 is already being implemented for Preventive Maintenance (PM), Manufacturing Execution System (MES), and Enterprise Resource Planning (ERP) to some extent, and the focus is on process optimization and equipment downtime reduction. The inferences drawn from this study are being used to develop highly targeted, supplier-agnostic solutions to modernize legacy manufacturing assets

An Integration Of Taguchi Method And Grey Relational Analysis In Optimizing Injection Moulding Processing Parameter Of Recycled Plastic

In this research, the effect of blending compositions of recycled polyamide 66 with 33% glass fiber filled (PA66-GF) is investigated and evaluated based on the dimensional stability, mechanical and rheological properties. Dalam kajian ini, kesan pengadunan komposisi poliamida 66 yang mengandungi 33% gentian kaca yang dikitar semula, disiasat dan dinilai berdasarkan kestabilan dimensi, sifat mekanik dan reologi