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AI-Driven Adoption and Management Strategies for Advanced Smart Fabrication in Global Manufacturing Enterprises
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Author(s): Swati Jain (Sanskar College of Management and Professional Studies, India), Vishakha Aggarwal (Sanskar Educational Group, India), Mansi Agarwal (Sanskar College of Management and Professional Studies, India), Priyanka Devi (Sanskar College of Management and Professional Studies, India), Soniya (Hitech Institute of Engineering and Technology, India)and Ramendra Singh (Raj Kumar Goel Institute of Technology, Ghaziabad, India)
Copyright: 2026
Pages: 30
Source title:
Next-Generation Electronic Textiles and Conductive Materials for Smart Wearables
Source Author(s)/Editor(s): Pranshu Saxena (Bennett University, India), Mandeep Singh (Bennett University, India), Sanjay Kumar Singh (University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi, India)and Mamoon Rashid (Bahrain Polytechnic, Bahrain)
DOI: 10.4018/979-8-3373-4287-0.ch014
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Abstract
We propose an AI-driven smart manufacturing framework for real-time monitoring, predictive maintenance, and decision-making in CNC machines. Temperature and vibration sensor data are preprocessed through filtering, normalization, and feature extraction to generate high-quality inputs. Four models ResNet-50 CNN, Stacked Bi-LSTM, Double Deep Q-Network (DDQN), and Random Forest analyze the data from complementary perspectives. ResNet-50 and Random Forest efficiently classify faults and anomalies, while Bi-LSTM captures temporal dependencies to forecast machine states, enabling proactive maintenance. DDQN leverages dynamic sensor feedback to learn adaptive maintenance policies. Evaluations using accuracy, precision, recall, F1-score, and mean absolute error confirm reliability and robustness. The hybrid integration of these models enhances fault detection, prediction, and decision-making, offering a scalable solution for CNC-based smart manufacturing that reduces downtime, improves productivity, lowers costs, and ensures operational safety.
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