A review of artificial intelligence applications in manufacturing operations

A review of artificial intelligence applications in manufacturing operations

Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as secur...

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Bibliographic Details
Published in:Journal of advanced manufacturing and processing Vol. 5; no. 3
Main Authors: Plathottam, Siby Jose, Rzonca, Arin, Lakhnori, Rishi, Iloeje, Chukwunwike O.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01-07-2023
Wiley Subscription Services, Inc
Wiley Blackwell (John Wiley & Sons)
Subjects:
AI challenges
Artificial intelligence
Control systems
Data acquisition
Data acquisition systems
Efficiency
Industrial electronics
industry automation
industry operations
Machine learning
Manufacturing
manufacturing industry
Optimization
Predictive control
Predictive maintenance
Productivity
Quality assurance
Quality control
Risk
Security
Sustainability
Trust
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Description
Summary:Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems. AI and ML can improve efficiency, productivity, and sustainability in manufacturing, but using AI in manufacturing can be challenging due to issues with data, human resources, infrastructure, and other factors. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, and identifying areas where further research can yield transformational returns for the industry.
Bibliography:Rishi Lakhnori and Arin Rzonca were Research Intern at Argonne National Laboratory at the time of contribution.
USDOE
ISSN:2637-403X
2637-403X
DOI:10.1002/amp2.10159