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 Articles will not be published until the final proofs are validated by their authors.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 (3 papers in press)

Regular Issues

  • Optimal Compensation Approach of Roots Vacuum Pump Rotor considering Pumping Performance and Dimensional Accuracy   Order a copy of this article
    by Can Yuan, Sitong Xiang, Cheng Wu, Xin Pan, Jianguo Yang 
    Abstract: Roots vacuum pumps are vital in scientific and industrial applications. Traditional performance optimization focuses on rotor profile design but neglects machining errors, limiting real-world effectiveness. This paper proposes a rotor compensation optimization method considering both pumping performance and dimensional accuracy. Firstly, a nonlinear mapping model of rotor radial clearance and pumping performance is established. Then, the radial clearance of the traditional mirror compensation (MC) surface is optimized using a genetic algorithm to obtain the optimized clearance and compensation surface. Experimental results show a 91.83% reduction in average dimensional deviation, a 4.23% increase in flow rate, and a 7.01% decrease in exhaust temperature. Compared to MC, the optimized method improves dimensional error compensation by 30.23%, flow rate by 1.78%, and lowers exhaust temperature by 3.46%. This method significantly enhances rotor accuracy and performance.
    Keywords: Roots vacuum pump; Pumping performance; Dimensional accuracy; Rotor clearance; Optimal compensation.
    DOI: 10.1504/IJMMS.2025.10075043
     
  • Multi-Objective Optimisation of Selective U-Shaped Disassembly Lines for Sustainable Manufacturing Systems   Order a copy of this article
    by Jiaqi Hu, YongGao Fu, Chao Wan, ShaLi You, ManQian Xie, Rui Jin 
    Abstract: The research presented in the article directly contributes to the broader domain of manufacturing systems by addressing a critical aspect of sustainable production -end-of-life product disassembly. Specifically, it introduces a selective U-shaped disassembly line balancing model that integrates multiple objectives: operational efficiency, carbon footprint reduction, and worker safety. This model enhances conventional manufacturing system design by incorporating reverse logistics and environmentally conscious practices, thereby aligning with the goals of circular manufacturing. The application of an advanced dual-stage optimisation artificial bee colony
    Keywords: manufacturing systems; disassembly line balancing; multi-objective optimization; artificial bee colony algorithm.
    DOI: 10.1504/IJMMS.2025.10076231
     
  • Punch Wear Estimation through Burr Height Analysis with Mel-Frequency Cepstral Coefficients and Artificial Neural Network   Order a copy of this article
    by Tushar Badgujar, V.A. Kolhe, Swapnil D. Galande, Prashant S. Tile 
    Abstract: This study introduces a real-time punch wear monitoring system tailored for the sheet metal trimming process using acoustic emission and a data-driven interface. The approach uses burr height as a key indicator of tool degradation and categorizes punch wear into three distinct states: freshly ground, partially worn, and fully worn. The process acoustic signals are denoised and converted into Mel-Frequency Cepstral Coefficients (MFCCs). These features are subsequently fed into a feed-forward artificial neural network (ANN) to accurately classify the punch wear condition. The approach is inspired by experienced machine operators who can intuitively discern punch wear from the sound emitted during trimming operations. On a collected dataset the model achieved 99.26% accuracy during training and 97.45% accuracy during testing. The system tracked progressive punch wear, demonstrating robustness to process noise and repeatability across runs. The system enables continuous, non-invasive tool monitoring, reducing manual inspection, unplanned downtime and improving product quality.
    Keywords: Punch Wear Detection; Burr Formation; Sheet Metal Trimming; Acoustic Signal; Wavelet Transform; MFCC; ANN; Real-time Monitoring.
    DOI: 10.1504/IJMMS.2025.10076237
     

 

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