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The dawn of a new generation of CNC on the path to sustainable, precise production (smart machining)

meysam.mirkohi1

Abstract :
With the advent of a new generation of computer numerical control (CNC) machines, the machining industry has entered a new phase in which precision, intelligence, and sustainability converge as the three main pillars of production. This research examines the evolution of traditional machining to “smart machining,” an approach that integrates Industrial Internet of Things (IIoT) networks, artificial intelligence, and machine learning algorithms to enable CNC systems to monitor, predict, and optimize cutting parameters in real time. In this new generation, advanced sensors measure tool temperature, vibrations, and cutting force in real time, and the data is transmitted to control processors for automatic adjustment of spindle speed, feed rate, and thermal error compensation. This closed-loop adaptive machining mechanism increases dimensional accuracy down to the micron level, prevents tool failure, and extends tool life. From a sustainability perspective, smart machining makes a significant contribution to green manufacturing and the circular economy by reducing waste, optimizing energy consumption, minimizing polluting cooling (MQL and dry machining approaches), and increasing material efficiency. The analysis results show that the use of smart CNC architectures can reduce waste rates by about 30 percent, specific energy consumption by 20 percent, and unwanted downtime by 50 percent through predictive maintenance. Overall, the rise of the new generation of CNC represents a transition from mere automation to industrial autonomy; a transition that defines the future of sustainable precision manufacturing within the framework of Industry 5.0 and the synergy of intelligent humans and machines. Keywords: Smart machining, New generation CNC, Sustainable manufacturing, Predictive maintenance, Industry, Cutting optimization

Keywords : smart machining new generation cnc sustainable manufacturing predictive maintenance industry cutting optimization

زبان مقاله : انگلیسی
محور مقاله : مهندسی مکانیک
محل انتشار : چهارمین کنگره بین المللی علوم، مهندسی و فن آوری های نو
آدرس وب سایت کنفرانس : secongress.com
کد IOI مقاله : ACCA-ACZAKH
لینک مستقیم مقاله : https://isnac.ir/ACCA-ACZAKH

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