Forthcoming Articles

International Journal of Mechatronics and Manufacturing Systems

International Journal of Mechatronics and Manufacturing Systems (IJMMS)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Mechatronics and Manufacturing Systems (2 papers in press)

Regular Issues

  • Intelligent Condition Monitoring of PMSM-Based Drive Systems using a Swin-CNN Fusion Network for Manufacturing Applications   Order a copy of this article
    by Hao Yu, Hao Zhang, Xiaojing Liu 
    Abstract: Permanent Magnet Synchronous Motors (PMSMs) are widely employed in advanced manufacturing and mechatronic systems, where early detection of mechanical faults is vital for system reliability and productivity. This paper presents a novel current-based radial misalignment diagnosis framework for PMSM-driven systems using a dual-branch Swin-CNN fusion network. The proposed method integrates time-frequency and spectral domain analysis through Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), respectively. A two-stream deep learning architecture is introduced, combining a 2D Swin Transformer for global time-frequency feature modeling and a 1D CNN enhanced with Convolutional Block Attention Module (CBAM) for frequency-domain emphasis. Experimental results on a lab-scale PMSM setup demonstrate an average classification accuracy of 98.125% under varying fault levels. The proposed approach offers a cost-efficient, non-invasive solution for condition monitoring in intelligent manufacturing systems, supporting predictive maintenance and enhanced equipment availability in Industry 4.0 environments.
    Keywords: Permanent Magnet Synchronous Motor (PMSM); Sliding Window Attention Network; Convolutional Neural Network (CNN); Attention Mechanism; Fault Diagnosis.
    DOI: 10.1504/IJMMS.2025.10073259
     
  • Identification of Tangential and Normal Forces in Micro End Milling Through Machine Learning Analysis of Force Signals   Order a copy of this article
    by Yiğit Karpat 
    Abstract: Developing digital twins of manufacturing processes, like computer numerical control (CNC) machining, is vital due to their importance for creating high value-added parts. Tool condition monitoring has been an important research topic within this context where a major focus is on analysing machining force signals. Micro-milling is a complex process due to contributing factors like tool runout, deflection, edge radius, elastic recovery of materials, microstructure effects, and machining dynamics. This paper focuses on machine learning analysis of force signals to identify normal and tangential forces acting on the micro end mill. A machine learning algorithm based on Gaussian Process Regression (GPR) has been used to identify normal and tangential forces as a function of uncut chip thickness. The novelty of this approach is that identified normal force variation as a function of uncut chip thickness reveals information on minimum uncut chip thickness and edge radius. Monitoring the variation of these characteristic points on the force curves can be used to identify tool wear and predict remaining useful tool life.
    Keywords: Micro-milling; Machine Learning; Gaussian Process Regression.
    DOI: 10.1504/IJMMS.2025.10074068
     

 

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