Forthcoming and Online First Articles

International Journal of Manufacturing Research

International Journal of Manufacturing Research (IJMR)

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 Articles will not be published until the final proofs are validated by their authors.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

Regular Issues

  • A research on metal sealing ring forming process database system and its application   Order a copy of this article
    by Chang Fan, Wuyang Sun, Zhao Zhang, Dinghua Zhang, Zaifei Zhou, Ming Luo 
    Abstract: Aiming at the forming process of metal sealing ring, in order to enhance and strengthen the automation level of the forming process and the stability of the forming quality, it is necessary to develop the metal sealing ring forming process database system. In order to accumulate data, knowledge and production status during the forming process, it is necessary to collect and store data such as material properties, workpiece dimensions, process equipment, process parameters, forming quality, and forming performance based on the structure-process-performance hierarchical model. In order to facilitate the storage and query of data, we have developed a B/S-based metal sealing ring forming process database system. This database system integrates data management, materials error-proofing, production operation status analysis, and system management, which helps to improve design level, accelerate process iteration, and improve production efficiency.
    Keywords: metal sealing ring; database; structure-process-performance hierarchical model; data model; data management.
    DOI: 10.1504/IJMR.2024.10064273
     
  • TIG Weld Defect Prediction from Weld Pool Images using Deep Convolutional Neural Network and Transfer Learning   Order a copy of this article
    by Rachna Verma, Arvind Kumar Verma 
    Abstract: TIG welding is widely used in fabrication, but its quality depends on precise control of welding parameters. This study employs convolutional neural networks (CNNs) and transfer learning to predict welding defects from weld pool images. Six pre-trained CNNs (MobileNet, MobileNetV2, NasNetMobile, InceptionV3, ResNet50V2, and EfficientnetB0) are evaluated for their accuracy and real-time processing ability for a two-class problem (defective vs. non-defective welds) and a six-class problem (classifying good weld, burn through weld defect, contamination weld defect, lack of fusion weld defect, lack of shielding gas weld defect, and high travel speed weld defect). All the models except EfficientnetB0 achieved a very high accuracy. However, based on the inference time and memory size of the models, MobileNetV2 with 99.94% accuracy is recommended for developing an automated TIG welding systems, enabling real-time adjustment of parameters based on weld pool appearance, ensuring high-quality defect-free welds.
    Keywords: TIG welding automation; weld defects; machine learning in TIG; pre-trained networks; CNN in TIG.
    DOI: 10.1504/IJMR.2024.10064357
     
  • Influence of Cutting Parameters in Hard Turning 40X Steel with Self-Driven Rotary Tool on Surface Roughness using Genetic Programming Method and Artificial Ecosystem-based Optimisation   Order a copy of this article
    by Trung Nguyen Van, Bien Duong Xuan, Duong Dao Van, Dieu Hoang Thi 
    Abstract: This paper focuses on developing a roughness prediction model based on Genetic Programming (GP) method and evaluates the influence of cutting parameters (CP) on surface roughness (SR) of 40X steel after heat treatment in rotary tool hard turning process. Different GP models are considered and the best model is selected for comparison with the multi-variables regression analysis (MRA) model. Next, the optimal value of CP and their influence on SR are determined through artificial ecosystem-based optimisation algorithm. Two best models GP and MRA were used to investigate the effect of CP on SR value with R2 index higher than 98%. The error value from GP (MSE = 0.014; MAPE = 4.75%) is much smaller than MRA (MSE = 0.045; MAPE = 8.3%). Furthermore, research results show the superiority of GP over MRA in considering the mutual relationship between the input variables for the objective function.
    Keywords: hard turning; surface roughness; multi-variables regression; genetic programming; GP; artificial ecosystem.
    DOI: 10.1504/IJMR.2024.10064816