AI-Driven Manufacturing Processes

Bidyut Sarkar and Rudrendu Kumar Paul

Chapter Chapter 2 in AI for Advanced Manufacturing and Industrial Applications, 2025, pp 19-59 from Springer

Abstract: Abstract This chapter explores the use cases and applications of AI in manufacturing, focusing on how AI-driven technologies are transforming industrial processes, enhancing efficiency, and improving product quality. Predictive maintenance emerges as a cornerstone application, leveraging machine learning to analyze time-series sensor data and enabling condition-based maintenance over traditional time-based models. These advancements reduce equipment downtime, extend machine lifespan, and foster operational reliability. AI-powered computer vision revolutionizes quality assurance by automating defect detection, part inspections, and dimensional monitoring, significantly improving accuracy and speed. The chapter also examines the role of natural language processing (NLP) in extracting actionable insights from unstructured text data, such as maintenance logs and technician notes, streamlining operations and supporting predictive maintenance. Advanced anomaly detection systems employ AI to identify irregularities in real-time, ensuring consistent product quality and operational efficiency. The integration of ARM architecture and digital twin simulations enhances computational efficiency, enabling real-time process monitoring, virtual scenario testing, and optimization. Digital twins, powered by real-time sensor data and AI, offer dynamic simulations to identify bottlenecks and proactively implement improvements. This chapter provides a comprehensive overview of benefits of AI application in manufacturing and its potential to revolutionize processes, reduce costs, and drive innovation, making it indispensable for modern industrial applications.

Keywords: Predictive maintenance; AI-driven quality control; Digital twin simulations; NLP in operations and maintenance; AI-based anomaly detection systems; ARM architecture in manufacturing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-86091-1_2

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DOI: 10.1007/978-3-031-86091-1_2

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