塑料注塑成型中的质量与缺陷预测——基于机器学习算法的浇口系统及其数学模型

Aug 4, 2026

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Plastic Injection Molding (PIM) requires reliable, automated identification of defective operations to ensure high product quality while keeping time consumption low. This paper presents an ML-based approach for detecting complex faults during PIM, using an MLGS-PIM gating system with Artificial Neural Network (ANN) and Support Vector Machine (SVM). CATIA–MATLAB simulation supports technical evaluation and intelligent handling of data and measurements. Three thermoplastic materials are tested with 3, 4, and 5 gate configurations, and outputs are assessed via sum rate, bit error rate, and convergence plots.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Plastic Injection Molding overview and challenges

Materials and gating systems in the proposed study

Machine learning process for quality and defect prediction

Proposed MLGS-PIM approach

ML models (ANN and SVM)

CATIA-MATLAB simulation-based evaluation

Experimental setup and outputs

Tested materials and gate configurations

Performance metrics (sum rate, bit error rate, convergence)

Quality and Defect Prediction in Plastic Injection Molding using Machine Learning Algorithms based Gating Systems and Its Mathematical Models a.Ekta S Mehta, b.Dr S.N. Padhi a. Research Scholar,Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, Guntur, 522502, India. Email [id-er.ektajain@gmail.com](id-er.ektajain@gmail.com) b. Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, Guntur, 522502, India. Email [id-snpadhi@kluniversity.in](id-snpadhi@kluniversity.in) Abstract: 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. Index Terms: Plastic Injection Molding (PIM), Machine Learning (ML), Artificial Neural Network (ANN), Support Vector Machine (SVM), CATIA and MATLAB. I. Introduction In the world the plastic is one among the extensively used synthetic materials which maintain its unique properties through that the integration of user demanded products are easily produced. At the initial stages in most of the applications metal counterpart are used then after the intervention plastic the weight of the materials are high reduced and as well it is cost effective in the market. These days plastics hold a prevailing role in industrial applications such as healthcare (Medical equipment manufacturing), automotive (machine design), and home appliances etc [1] . More than 90% of the plastic oriented products are produced using the PIM process. The major advantages in PIM process is that through this process huge volumes of plastic products with varying complexity can able to produce in very short duration of time and maximum productivity can be achieved. PIM is a kind of process which consists of certain segmentations like shaping mold, cooled, solidified, and ejected etc. The major parts in the PIM machine is that the injection unit, the mold assembly unit, and the clamping unit [2] . The structure of PIM machine is shown in figure 1. Figure 1-Structure of PIM Machine Especially in the field of research, currently quality monitoring becomes very essentia

FAQs

为什么在塑料注塑成型中需要自动化的质量与缺陷预测？

PIM要实现高质量产品，关键在于以自动方式识别缺陷操作；同时还需要在保证质量的前提下降低时间消耗并提高过程稳定性。

文中提出的 MLGS-PIM 方法包含哪些机器学习模型？

采用了人工神经网络（ANN）和支持向量机（SVM）两类学习模型，并对其组合以在不同条件下获得更高质量表现。

评估模型性能时使用了哪些材料、浇口系统配置和输出指标？

选用三种热塑性材料（如PS、ABS、PVC），浇口系统配置包含3、4和5个浇口；输出分析使用sum rate、bit error rate以及收敛曲线（convergence plot）等指标。

Aug 4, 2026

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