International Journal of Mechatronics and Manufacturing Systems (4 papers in press)

Regular Issues

• Life cycle cost model of remanufacturing-oriented retired engine product under uncertain conditions  
  by Junli Shi, Fangli Shu, Zhongchi Lu, Mengmeng Ren, Huanhuan Xu 
  Abstract: The life cycle cost of retired engine product present highly uncertainty due to the different working conditions, service time and damage status. This study proposed a life cycle cost model for remanufacturing-oriented engine product under uncertain conditions based on the theory of life cycle cost (LCC). In this model, the system boundary of the life cycle is established firstly, then the quantitative damage level of used product is defined, and next the cost calculation model in each life cycle stage is introduced, finally the nonlinear function model of life cycle cost with service time and damage level is established. The connecting rod of a retired engine is taken as the case study, the results demonstrate the validity of the proposed model, and the results can provide decision support for engine remanufacturing from the whole society resource consumption.
  Keywords: remanufacturing; life cycle cost; LCC; uncertainty; damage level; service time; engine connecting rod.
  DOI: 10.1504/IJEME.2023.10059105
   
  
• Deep Learning-Based Wafer Fabrication Quality Assessment in Semiconductor Manufacturing  
  by Jinxing Zhao, Quan Meng, Haolan Zheng, Yuhao Fan, Zinuo Zeng 
  Abstract: Semiconductor manufacturing relies heavily on wafer fabrication quality, as it directly determines the performance and reliability of downstream electronic and optoelectronic devices. To address the challenges of reliability and efficiency in wafer quality assessment, we propose a deep learning-based multi-label defect detection and classification method for wafer fabrication. The method employs the ShuffleNetV2 for feature extraction, and the Sigmoid activation function for multi-label outputs. The CB-Focal Loss was introduced to tackle class imbalance while the coordinate attention mechanism was integrated to enhance the model's focus on defect regions. Experiments on our own AFM-Wafer dataset demonstrate that, compared with the baseline model, new model achieves 0.51%, 1.39%, 1.31%, and 1.24% increases in Accuracy, L-Precision, L-Recall and L-F1 score, respectively while maintaining nearly the same parameter count and inference speed. This research demonstrates how advanced computer vision and deep learning complement traditional manufacturing and economic merits.
  Keywords: Wafer Fabrication; Deep Learning; Defect Detection; Quality Assessment; Semiconductor Manufacturing.
  DOI: 10.1504/IJMMS.2025.10077423
   
  
• A defect-aware experimentalnumerical framework for flexural design of graded TPMS lattices manufactured by FDM  
  by Mariangela Quarto, Flavio Caretto, Gianluca D'Urso 
  Abstract: This work investigates a key gap in current research by focusing on the flexural response of triply periodic minimal surface (TPMS) structures fabricated by fused deposition modelling technology. To provide a comprehensive understanding, experimental, tomographic, and numerical analyses were carried out. The study explores how cell size and graded relative density influence flexural performance. Three-point bending tests (based on ISO 178:2019) were combined with high-resolution micro-computed tomography (micro-CT) to quantify manufacturing-induced porosity, and with finite element (FE) simulations to validate the observed deformation mechanisms. Micro-CT analysis together with flexural characterisation and statistical analysis allow the definition of design guidelines connecting manufacturable wall thickness, relative-density gradients, and flexural efficiency. Finally, an industrial case study demonstrates the practical impact of this approach, applying TPMS infill to a pressurised tank to achieve substantial weight reduction while preserving mechanical integrity. These results highlight the potential of graded TPMS structures for lightweight, damage-tolerant components, and support their advancement toward Technology Readiness Level (TRL) 56.
  Keywords: TPMS; Additive Manufacturing; Mechanical Properties; Finite Element Analysis.
  DOI: 10.1504/IJMMS.2025.10079022
   
  
• Digital Technologies: Maturity Analysis and Future Trend Predictions  
  by Qingfeng Li, Hui Xiang, Xiao Liao, Lifeng Xing, Yueming Ji, Keer Ning, Hanwen Gu, Zaibin Jiao 
  Abstract: Aiming at the current problem of unclear development path of grid digitalization technology, we propose the development stage assessment and maturity prediction method of grid digitalization technology to explore its development trend. First, we build a technology maturity assessment model based on the technology maturity curve, categorizing it into three main areas: smart sensors, IoT technology, and AI, using literature research to establish a maturity prediction database. We then analyze and predict the future stages and overall development trends of grid digitization, offering maturity predictions influenced by both technological and thermal factors. Finally, the model's accuracy is validated through designed goodness-of-fit indices and cross-validation. The findings indicate that the volume of literature and patents can reflect technological trends, with China's digital grid technology research peaking around 2030-2040 and stabilizing by 2060, aligning with China's carbon neutrality goals.
  Keywords: Smart grid digital transformation; technology maturity curves; goodness-of-fit metrics; maturity prediction.
  DOI: 10.1504/IJMMS.2025.10079333