MULTI-OBJECTIVE OPTIMIZATION OF ADDITIVE MANUFACTURING QUALITY, ENERGY, AND PRODUCTION EFFICIENCY

Abstract

Additive manufacturing has emerged as an advanced production technology capable of fabricating complex components with reduced material waste and enhanced design flexibility. However, achieving optimal manufacturing performance requires balancing multiple conflicting objectives including product quality, energy consumption, production speed, and operational efficiency. This paper proposes a multi-objective optimization framework for additive manufacturing by integrating machine learning, Industrial Internet of Things (IIoT), Digital Twin technology, predictive analytics, multi-sensor data fusion, cloud computing, and intelligent optimization algorithms. The proposed methodology continuously analyzes manufacturing conditions, predicts quality outcomes, optimizes process parameters, and identifies optimal trade-offs between quality improvement, energy reduction, and production efficiency. Experimental evaluation demonstrates improvements in defect reduction, surface quality, energy efficiency, production throughput, process stability, and resource utilization. The proposed framework provides a scalable and intelligent solution for sustainable Industry 4.0- enabled additive manufacturing by supporting autonomous decision-making and optimized production operations. Keywords— Multi-Objective Optimization, Additive Manufacturing, Machine Learning, Energy Efficiency, Production Optimization, Digital Twin, IIoT, Smart Manufacturing.