AI- and IoT-Based CNC Optimization for Sustainable Production
Abstract
Manufacturing is being transformed by Industry 4.0, which uses advanced technologies like Internet of Things (IoT) and artificial intelligence (AI) to make it more efficient and environmentally benign. Computer numerical control (CNC) systems are essential to contemporary production, and this chapter explains various investigations about how these technologies can revolutionize them. The study presented created an AI-powered optimization system that gathers and analyzes real-time data from CNC machines using IoT sensors. This innovative approach aims to increase operational efficiency, decrease waste, and promote sustainable manufacturing practices. The ability of CNC machines to adapt and operate more efficiently is enabling smarter, cleaner production processes. This chapter presents research that could benefit several industries and is a significant step toward more ecologically friendly production. The main goal is to create a reliable system that supports sustainable production objectives by enhancing machining accuracy, lowering energy usage, and minimizing material waste. Because it integrates AI and IoT technologies for CNC optimization holistically, this research is unique. It goes beyond traditional methods to incorporate energy-efficient machining techniques, adaptive control, and predictive maintenance. The outcomes show how AI and IoT have the potential to completely transform CNC machining by demonstrating notable gains in sustainability metrics and production efficiency. Consequently, this work highlights the significance of advanced technologies in achieving sustainable production and provides a model for future research and industry applications in smart manufacturing environments.
Fields of Study & Subjects
Advanced machining processes and optimization
Digital Transformation in Industry
Energy Efficiency and Management
Publication details
- Journal / source
- AIoT-Driven Sustainable Manufacturing and Operational Initiatives
- Publisher
- CRC Press
- Pages
- 118–138
- Publication date
- 27 January 2026
- Language
- EN
- Document type
- Book chapter