CFP: ASME JCISE Special Issue on Machine Learning Applications in Manufacturing

Official ASME Group

Manufacturing Engineering Division

Manufacturing Engineering Division (MED) is concerned with the knowledge base of manufacturing sciences and technology and its applications for improved production performance.
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  • CFP: ASME JCISE Special Issue on Machine Learning Applications in Manufacturing

    Dear Colleagues,

    We are pleased to announce that ASME Journal of Computing and Information Science in Engineering is calling for papers for a Special Issue on Machine Learning Applications in Manufacturing. The CFP flyer is attached. 

    This Special Issue aims to harvest the latest efforts in theoretical as well as experimental aspects of ML and their applications in manufacturing. The paper submission deadline is July 1st, 2019

    Potential topics include, but are not limited to:

    ML-based theoretical approaches for manufacturing
     ML for robotics and human-machine interaction
     ML for predictive maintenance, quality control, and process optimization
     ML for tasks scheduling and supply chain management
     ML for sustainable manufacturing
     ML for manufacturing process monitoring and control
     ML and data-driven design for manufacturing to enable better and faster fabrication of parts
     ML methods that provide insights for manufacturing process improvement
     ML methods that leverage material informatics for improved manufacturing

    ML-based experimental case studies for smart manufacturing
     Advanced diagnostics, prognostics and asset health management
     Energy consumption modelling and optimization
     Advanced robotics (collaborative and adaptive robots)
     Digital twin
     Leveraging ML for hybrid manufacturing (additive and subtractive manufacturing)
     Data acquisition for novel manufacturing processes

    Novel ML algorithm design for manufacturing
     Approaches to extract manufacturing knowledge using ML techniques
     Algorithms and approaches handling big data, data imbalance, uncertainty, data fusion, etc.
     Calibration and validation of ML-based patterns and models
     Addressing security, privacy, and cyber resilience/reliability issues
     Novel deep learning architecture for manufacturing domain problems
     Hybrid machine learning methods that combine data-driven and equation-based methods

    Creation and sharing of research data that supports ML applications in manufacturing

    Special Issue Editors
    Ying Liu, Cardiff University, UK,
    Bin He, Shanghai University, China,
    Mahesh Mani, Allegheny Science & Technology, US,
    Anurag Purwar, Stony Brook University, US,
    Rahul Rai, University at Buffalo SUNY, US,

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