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 Articles will not be published until the final proofs are validated by their authors.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 (2 papers in press)

Regular Issues

  • Contract Design and Implementation in the Era of Industry 4.0: a Systematic Analysis   Order a copy of this article
    by Cheng Wang, Mengna Hu, Zhuowei Zheng, Longyan Wei, Xun Xu 
    Abstract: Smart manufacturing under the background of Industry 4.0 requires organizations to carry out digital transformation from three dimensions: vertical integration, horizontal integration and end-to-end integration. Contract or mechanism design is needed when integration happens among different ownerships. To explore the contracts in smart manufacturing, with the help of bibliometrics, 5087 related literature were retrieved based on the Web of Science, covering the period from 2000 to 2024. Bibliometric results are presented from the perspectives of global contribution, leading countries/regions, related research areas, institutions, journals, authors, the most cited publications and keywords. Hot topics and trends are excavated from highly cited papers and hot papers. Based on the bibliometric results, we confirm that blockchain and smart contracts are key to the realization of contracts in smart manufacturing. Finally, this paper provides a systematic analysis of contract design and implementation driven by blockchain and smart contracts in horizontal and end-to-end integration.
    Keywords: Industry 4.0; contract design; contract implementation; smart manufacturing; vertical integration; horizontal integration; end-to-end integration; blockchain; smart contracts; bibliometrics.
    DOI: 10.1504/IJMR.2024.10069160
     
  • Deep Learning-Driven Parts Feature Extraction and Surface Reconstruction for Efficient Parts Pairing   Order a copy of this article
    by Xuezhen Li, Xiao Lu, Zhehan Chen, Ning Zhao, Lechang Yang 
    Abstract: Assembly stands as a crucial process in industrial manufacturing, but traditional manual parts pairing is often inefficient. Previous research has highlighted the potential of deep learning for feature extraction and 3D reconstruction from point clouds. We introduces an innovative method based on deep learning for high-precision feature extraction and surface reconstruction aimed at parts pairing. By defining essential assembly features and employing the Random Sample Consensus method, geometric dimensions and surface topography data are acquired. Subsequently, deep learning is utilised to directly regress the Surface Distance Function from point samples, enabling detailed surface modelling of parts and supporting assembly simulation within the digital twin framework. A case study for validation reveals that after optimisation, 30 shaft parts and 30 hole parts are successfully matched, with an average uniformity increase of 0.024. This demonstrates the proposed method's superior effectiveness and accuracy in feature extraction and surface reconstruction.
    Keywords: parts paring; features extracting; surface reconstruction; non-contact measuring; deep learning.
    DOI: 10.1504/IJMR.2024.10069271
     

 

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