Papers
2
Total Citations
17
H-Index
2
About
Ayoub Chakroun is an emerging researcher specializing in predictive maintenance, machine learning, and smart manufacturing systems, with a particular focus on industrial robotics. His work sits at the intersection of artificial intelligence and Industry 4.0, addressing one of modern manufacturing's most pressing challenges: anticipating equipment failures before they disrupt production. Chakroun's most notable contribution, published in 2024, introduces a machine learning-based predictive maintenance model designed to assess the health of assembly robots within smart plant environments, garnering 14 citations and signaling strong early interest from the research community. Building on this foundation, his 2023 work extended these methodologies to packaging robots, further demonstrating the versatility and applicability of his predictive frameworks across different robotic systems, accumulating an additional 3 citations. Though early in his career, Chakroun's research addresses a critical industrial need — reducing downtime, optimizing maintenance schedules, and enhancing operational efficiency through data-driven decision-making. His growing citation record reflects the relevance of his contributions to both academia and industry practitioners seeking to build more resilient, intelligent manufacturing environments. Researchers working in smart factories and industrial AI will find his work particularly valuable.
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Top Papers
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