Forthcoming and Online First Articles

International Journal of Mechatronics and Manufacturing Systems

International Journal of Mechatronics and Manufacturing Systems (IJMMS)

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International Journal of Mechatronics and Manufacturing Systems (3 papers in press)

Special Issue on: Artificial Intelligence for Smart Manufacturing and Mechatronics

  • Collaborative Resilience: Taxonomy-Informed Neural Networks for Smart Assets’ Maintenance in Hostile Industry 4.0 Environments   Order a copy of this article
    by Vagan Terziyan, Oleksandra Vitko 
    Abstract: This article explores knowledge-informed machine learning and particularly Taxonomy-Informed Neural Networks to enhance data-driven smart assets’ maintenance by contextual knowledge. Focusing on assets within the same class that may exhibit subtle variations, we introduce a weighted Lehmer mean as a dynamic mechanism for knowledge integration. The method considers semantic distances between the asset-in-question and others in the class, enabling adaptive weighting based on behavioral characteristics. This preserves the specificity of individual models, accommodating heterogeneity arising from manufacturing and operational factors. In the adversarial learning context, suggested method ensures robustness and resilience against adversarial influences. Our approach assumes a kind of federated learning from neighboring assets while maintaining a detailed understanding of individual asset behaviors within a class. By combining smart assets with digital twins, federated learning, and adversarial knowledge-informed machine learning, this study underscores the importance of collaborative intelligence for efficient and adaptive maintenance strategies in Industry 4.0 and beyond.
    Keywords: neural networks; knowledge-informed machine learning; taxonomy; smart asset; digital twin; maintenance; federated learning; adversarial learning; robustness; Industry 4.0.
    DOI: 10.1504/IJMMS.2024.10064064
    by Ejiofor Matthew Dialoke, Hongrui Cao, Jianghai Shi 
    Abstract: During the milling process, chatter is one of the most uncontrollable and unwanted occurrences. To prevent damage to the workpiece and to monitor and detect chatter as quickly as possible, a reliable indicator is essential. This paper proposes a robust root mean square (RRMS) indicator for online chatter identification. Using weighted techniques, the two-time domain indicators Root Mean Square (RMS) and Kurtosis (K) are combined to develop the proposed indicator, RRMS, with improved detection accuracy. The Short-Time Fourier Transform (STFT) was used to visualize the changing frequency components in the Time-Frequency Representation (TFR). The efficacy of the proposed indicator for online detection was confirmed by a series of milling tests. The 3-sigma rule is used to calculate the threshold, and the RRMS is employed for detection. Because the results demonstrate a heightened sensitivity to chatter, we concluded that RRMS is extremely suitable for online detection.
    Keywords: Chatter detection; Acoustic signal; Weighted technique; Variable cutting depth; Variational mode decomposition (VMD).
    DOI: 10.1504/IJMMS.2024.10064065
  • Multivariate analysis of AISI-52100 steel machining: A combined finite element-artificial intelligence approach   Order a copy of this article
    by Anastasios Tzotzis, Nikolaos Efkolidis, César García, Panagiotis Kyratsis 
    Abstract: The present study focuses on the analysis of AISI-52100 steel hard-turning with standardized square inserts. The process is being studied in terms of the resultant cutting force and the cutting power under a wide range of four key machining parameters: the cutting speed, the feed rate, the depth of cut and the tool nose radius. First of all, an updated Finite Element Method (FEM) model has been used to generate a data set, which in turn was used to train an artificial Neural Network (ANN), minimizing this way the required experimental work and the utilization of high amounts of computing resources. The developed networks were evaluated with regard to their reliability, revealing increased levels of accuracy. The Mean Absolute Percentage Error (MAPE) was calculated equal to 8.1% for the force prediction network and 11.2% for the power prediction network respectively. Furthermore, the multivariate interaction was evaluated and visualized.
    Keywords: AISI-52100 turning; cutting forces; cutting power; 3D FEM; ANN; DEFORM-3D; artificial intelligence.
    DOI: 10.1504/IJMMS.2024.10064869