A review of artificial intelligence applications in manufacturing operations

Abstract

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.

Positioning AI as a Partner in Work and Action: A Library Research Journal

“…We are not left behind in the world of manufacturing (Armutak et al, 2022;Rus, 2019). AI has the potential to be very helpful in manufacturing, especially in applications such as predictive maintenance, quality assurance, and process optimization (Plathottam et al, 2023). Educators must adapt to AI's introduction in schools, educating students about its role in learning, privacy, security, and societal implications, while encouraging critical examination of potential risks (Forsyth et al, 2021;Zhang et al, 2023).…”

Bibliometric Analysis of Intelligent Systems for Early Anomaly Detection in Oil and Gas Contracts: Exploring Recent Progress and Challenges

“…It should align with the specific data characteristics and the objectives of anomaly detection to support decision-making processes once an oil project progresses into the execution phase [45,46]. Overview of machine learning applications in contract anomaly detection for the oil and gas industry [11,12,17,19,30,32,34,37,41,43]. While ML methodologies like ANNs, SVMs, decision trees, the Isolation Forest Algorithm, and K-means show promise in the early detection of anomalous behaviors in contracts, they also bring challenges that require careful parameter tuning and consideration of the data context.…”

“…This categorization, rooted in the technical literature and expert insights, showcases the flexibility of ML applications [17]. The classification and application of machine learning methods can vary between different studies [18,19]. Using machine learning for different public services has shown promising results in the health, security, and education sectors [20].…”

“…Figure 9. Overview of machine learning applications in contract anomaly detection for the oil and gas industry[11,12,17,19,30,32,34,37,41,43].…”

Editorial

“…Finally, Reference [5] is a contribution from Argonne National Lab. This reviews the state of the art in artificial intelligence for manufacturing and highlights specific types of applications that can benefit from its use.…”