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An adaptive neuro-fuzzy approach using IoT data in predicting springback in ultra-thin stainless steel sheets with consideration of grain size

  • Jing Zhao (School of Computer, Wuhan Donghu University) ;
  • Lichun Wan (College of Management, Wuhan Donghu University) ;
  • Mostafa Habibi (Department of Biomaterials, Saveetha Dental College and Hospital, Saveetha Institute of Medical and Technical Sciences) ;
  • Ameni Brahmia (Department of Chemistry, College of Science, King Khalid University)
  • Received : 2021.12.21
  • Published : 2024.08.25
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Abstract

In the era of smart manufacturing, precise prediction of springback-a common issue in ultra-thin sheet metal forming- and forming limits are critical for ensuring high-quality production and minimizing waste. This paper presents a novel approach that leverages the Internet of Things (IoT) and Artificial Neural Networks (ANN) to enhance springback and forming limits prediction accuracy. By integrating IoT-enabled sensors and devices, real-time data on material properties, forming conditions, and environmental factors are collected and transmitted to a central processing unit. This data serves as the input for an ANN model, which is trained with crystal plasticity simulations and experimental data to predict springback with high precision. Our proposed system not only provides continuous monitoring and adaptive learning capabilities but also facilitates real-time decision-making in manufacturing processes. Experimental results demonstrate significant improvements in prediction accuracy compared to traditional methods, highlighting the potential of IoT and ANN integration in advancing smart manufacturing. This approach promises to revolutionize quality control and operational efficiency in the industry, paving the way for more intelligent and responsive manufacturing systems.

Keywords

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