Optimization of Manufacturing Processes for Enhancing Production Efficiency and Reducing Waste in High-Volume Mechanical Component Production

Abstract

High-volume production of mechanical components requires manufacturing systems that can consistently achieve high productivity, dimensional accuracy, quality and cost efficiency while minimizing material, energy, time and operational waste. Conventional production systems frequently experience losses arising from machine downtime, excessive setup time, inefficient process routing, tool wear, defects, unnecessary material movement and excessive energy consumption. Recent developments in lean manufacturing, Industry 4.0, artificial intelligence, digital twins, predictive maintenance and sustainable machining provide new opportunities for addressing these challenges. This paper examines process optimization strategies for high-volume mechanical component production, focusing on lean waste elimination, machining-parameter optimization, production scheduling, equipment effectiveness, digital monitoring and energy-efficient manufacturing. The literature indicates that integrating lean principles with data-driven technologies can improve process visibility and support real-time decision-making. Optimization of cutting parameters, tool paths and production schedules can simultaneously reduce machining time, energy consumption and material losses. Digital twins and artificial intelligence further enable predictive process control and equipment optimization. The paper proposes an integrated optimization framework combining process mapping, performance measurement, parameter optimization, predictive monitoring and continuous improvement. Such an approach can enhance productivity while supporting economically and environmentally sustainable manufacturing.