Two recent editorials in the ASM Journal of Failure Analysis and Prevention have addressed the potential use of AI language model tool, such as ChatGPT, to support failure modes and effects analysis (FMEA).
Dan Thomas, Revolutionizing Failure Modes and Effects Analysis with ChatGPT: Unleashing the Power of AI Language Models. Journal of Failure Analysis and Prevention. Vol 23, 2023, p 911–913. https://doi.org/10.1007/s11668-023-01659-y
Partha Pratim Ray, Transforming Industrial Reliability: The Intersection of Failure Modes and Effects Analysis with Artificial Intelligence. Journal of Failure Analysis and Prevention, Vol 23, 2023, p 1383–1384. https://doi.org/10.1007/s11668-023-01697-6
Access to the articles is open to all readers through September 30.
Dan Thomas notes that
Traditional FMEA methods are often time-consuming and labor-intensive, requiring significant expertise and knowledge to be effective. Recent advancements in artificial intelligence (AI) and machine learning (ML) have opened new possibilities for FMEA, allowing organizations to harness the power of language models like ChatGPT to transform their FMEA process and unlock new insights.
He reviews how these models can be trained on FMEA data and then help identify potential failure modes for a given system or process, assess risks, and recommend mitigation strategies. He concludes that:
The potential of ChatGPT in FMEA is just beginning to be realized, and the future looks bright. As more organizations adopt AI and ML technologies, we can expect to see even greater improvements in the efficiency, accuracy, and effectiveness of FMEA processes.
The editorial by Partha Pratim Ray adds a cautionary note:
While the integration of AI undoubtedly has potential, any reliance on it should be tempered by a recognition of the limitations of such models. No AI, not even one as advanced as ChatGPT, can wholly supplant the nuanced understanding and judgement calls offered by human experts.
Dr. Ray argues for an integrated approach, where AI tools are used to supplement human intelligence, leveraging the capacity of these tools to process large amounts of data, thereby freeing the human failure analysis professional to focus on more complex tasks. Their combined efforts should foster "greater efficiency and, ultimately, reliability."
What are your thoughts about the opportunities and risks of using AI tools in failure analysis?
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