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Optimizing Industrial Manufacturing for Enhanced Efficiency and Quality
Abstract
This paper examines the optimization of industrial manufacturing processes for enhanced efficiency and quality through AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional manufacturing practices, including production planning, quality control, and predictive maintenance. The study highlights the application of AI techniques such as machine learning, computer vision, and robotics in improving productivity, reducing waste, and ensuring product consistency in manufacturing operations. Additionally, it discusses the integration of AI with Internet of Things (IoT) sensors, digital twins, and augmented reality to enable real-time monitoring, analysis, and optimization of manufacturing processes. The paper also addresses challenges such as workforce reskilling, data interoperability, and cybersecurity in the deployment of AI-driven engineering solutions in industrial manufacturing. It emphasizes the importance of collaboration among industry stakeholders, technology providers, and policymakers to harness AI's potential in driving innovation and competitiveness in the manufacturing sector.
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References
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