Maxim Dalton
Papers
1
Total Citations
6
H-Index
1
About
Maxim Dalton is a researcher at the intersection of manufacturing engineering and artificial intelligence, with a primary focus on advancing quality control in automated industrial processes. His key research areas include arc stud welding (ASW) defect detection, machine learning applications in manufacturing, and process automation. Dalton’s most notable contribution is his pioneering work on using machine learning algorithms to classify weld defects in automotive stud welding, a critical process where even minor flaws can lead to costly structural failures. His 2023 paper, "Application of Machine Learning in Automotive Stud Weld Defect Classification," has garnered 6 citations, establishing a foundation for data-driven quality assurance in high-stakes production environments. By demonstrating how ML models can replace traditional inspection methods, Dalton has helped reduce waste and improve reliability in automotive assembly lines. His work is particularly impactful for researchers and engineers seeking to integrate Industry 4.0 technologies into legacy manufacturing systems. Dalton’s research promises to reshape how factories approach defect prevention, making him a rising voice in smart manufacturing and applied machine learning.
Research Focus
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Top Papers
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