PREDICTIVE MODELING OF MANUFACTURING DEFECTS USING MACHINE LEARNING AND MULTI-PARAMETER PROCESS OPTIMIZATION

Abstract

Manufacturing quality is influenced by complex interactions among machine settings, material characteristics, tool condition, thermal behavior, environmental conditions, equipment health, production sequence, and process variability. Conventional quality control strategies frequently depend on end-of-line inspection, fixed process limits, isolated statistical indicators, and retrospective defect analysis. These approaches can identify visible nonconformities after production but may provide limited capability for predicting defects before they occur or identifying nonlinear relationships among multiple process parameters. This paper proposes a Predictive Modeling framework for Manufacturing Defects using Machine Learning and Multi-Parameter Process Optimization that integrates heterogeneous manufacturing data acquisition, process parameter synchronization, sensor fusion, data quality assessment, contextual feature engineering, machine-learning-based defect prediction, multi-parameter optimization, digital process profiles, predictive maintenance information, secure API integration, automated model operations, and closed-loop production feedback. The proposed methodology collects machining variables, temperature, vibration, acoustic emissions, electrical behavior, tool condition, material characteristics, production settings, environmental information, inspection outcomes, and historical defect records. Data are synchronized according to machine, batch, product, and process stage before analytical modeling. Machine-learning models estimate defect risk and identify parameter combinations associated with surface irregularity, dimensional deviation, porosity, incomplete fusion, toolrelated defects, and other quality problems. A multi-parameter optimization component evaluates candidate process configurations while considering quality, equipment health, production stability, and operational constraints. Digital process profiles maintain historical context and support comparison between current production conditions and previously validated operating patterns. Automated model operations continuously monitor model behavior, identify data and concept drift, support controlled retraining, and preserve deployment traceability. Secure APIs enable distributed interoperability, while communication integrity controls protect analytical interactions. A comparative analytical evaluation indicates that the proposed framework can improve defect prediction accuracy, reduce defect rate, lower false alarms, improve early quality-risk identification, decrease scrap, reduce rework, and improve process stability compared with conventional quality control and standalone machine-learning approaches. The findings demonstrate that machine learning combined with multi-parameter process optimization provides a scalable foundation for proactive, adaptive, and data-driven manufacturing quality management.