Author(s)

Dipak Kumar Banerjee ¹, Ashok Kumar ¹

Affiliation(s)

¹ Welspun Tubular Inc, Frazier Pike, Lr-72206, USA

Corresponding Author

Dipak Kumar Banerjee

ABSTRACT

This paper explores the integration of Artificial Intelligence (AI) in manufacturing lab testing systems, focusing on how AI can revolutionize traditional testing methods to enhance product quality, efficiency, and reliability. Traditional lab testing in manufacturing is often marred by inefficiencies, human error, and lengthy processing times, which can adversely affect production throughput and quality. With the advent of AI, new possibilities have arisen to automate and optimize these processes. This research provides a comprehensive review of current AI applications, case studies, and empirical data to demonstrate the potential of AI in addressing existing challenges in lab testing. By employing machine learning, neural networks, and computer vision, AI technologies enable enhanced precision, predictive insights, and reduced operational costs in lab testing. The paper further examines the future implications of AI integration in the industry, aiming to provide a clearer understanding of its benefits and the challenges that lie ahead. Through a systematic methodology that includes a robust literature review and data analysis, this study contributes significant insights into the transformative impact of AI on manufacturing lab testing systems.

KEYWORDS

AI, Manufacturing, Automation, Machine Learning, Predictive Maintenance

CITE THIS PAPER

Dipak Kumar Banerjee, Ashok Kumar, Integration of Artificial Intelligence in Manufacturing Lab Testing System. Journal of Materials, Processing and Design (2024) Vol. 8: 1-8. DOI: http://dx.doi.org/10.23977/jmpd.2024.080201.

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