AI-ASSISTED SUSTAINABLE CNC MANUFACTURING FOR INDUSTRIAL PRODUCTIVITY AND ENERGY EFFICIENCY “Advancing Smart and Sustainable Manufacturing through AI-Driven CNC Machining Optimization”

Abstract

The rapid growth of industrial manufacturing and increasing global demand for sustainable production systems have intensified the need for intelligent and energy-efficient machining processes. Conventional CNC turning optimization studies primarily focus on maximizing material removal rate and improving surface quality using statistical methods such as Taguchi optimization and Analysis of Variance (ANOVA). However, limited attention has been given to the integration of artificial intelligence-assisted prediction models with sustainable manufacturing frameworks capable of improving industrial productivity while reducing energy consumption and operational costs. The present study proposes an AI-assisted optimization framework for CNC turning of mild steel using carbide cutting tools under varying machining conditions. Taguchi L9 orthogonal array is employed to design experiments considering cutting speed, feed rate, and depth of cut as major process parameters. Material Removal Rate (MRR) and machining energy consumption are selected as primary performance indicators. Statistical analysis through Signal-to-Noise ratio and ANOVA is used to identify the significance of machining parameters, while regression-based machine learning prediction is incorporated to improve process predictability and decision-making capability. The study further evaluates the techno-economic implications of optimized machining for sustainable industrial manufacturing and MSME productivity enhancement in India. The findings indicate that feed rate and depth of cut significantly influence machining productivity and energy consumption. The proposed framework contributes toward Industry 4.0-enabled smart manufacturing and supports India’s vision of sustainable industrial transformation under Viksit Bharat 2047.