Forthcoming 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 Articles will not be published until the final proofs are validated by their authors.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

  • AI Tools for Advancement of Manufacturing Sector: a Systematic Review, Bibliometric Analysis, Thematic Synthesis, and Technological Framework Development   Order a copy of this article
    by Kunal Sharma, Vikrant Sharma, Mukheshwar Yadav 
    Abstract: The burgeoning field of artificial intelligence (AI) has had a profound impact on a wide range of industrial sectors. This transformative paradigm shift aims to fundamentally change traditional manufacturing methodologies. The primary goal of this study is to provide a comprehensive overview of the current state and future path of AI applications in manufacturing settings. A bibliometric analysis is carried out to determine the evolutionary path of AI-centric research in the manufacturing domain. Subsequently, a thematic synthesis is performed to categorise and combine the findings, revealing overarching themes. Furthermore, this study adds significantly to the field by providing a technological framework for the integration and deployment of AI tools aimed at augmenting manufacturing processes. Research into AI in the manufacturing sector has increased dramatically since 2019, concentrating mainly on machine learning, deep learning and computer vision. The framework outlines major barriers, factors that help and practical uses of AI, giving industry partners valuable ideas. The combined insights from this study provide a solid foundation for informed decision making, strategic planning, and the development of future innovations at the intersection of AI and manufacturing.
    Keywords: Artificial Intelligence (AI); AI Tools; Manufacturing Sector; Bibliometric Analysis; Thematic Analysis; Framework Development.
    DOI: 10.1504/IJMR.2025.10072918
     
  • New Method for Estimating Additive Blooming on the Surface of Vulcanised Rubbers   Order a copy of this article
    by Jose D.E. Jesus Cabrera-Castro, Roberto Zitzumbo Guzmán, María Blanca Becerra Rodriguez, Anayansi Estrada Monje 
    Abstract: The blooming of additives on the surface of vulcanised rubber samples has been evaluated. Blooming is the accumulation of additives that have migrated from inside the rubber sample to the surface, manifesting themselves as whitish stains. The determination of the surface bloom was carried out using two methods, a traditional method and a newly developed method. The traditional method is the gravimetric method and the new method developed is the Euclidean distance method, which consists of a system composed of artificial vision, image processing and a mathematical model. The Rvalues confirmed that a Fick-type diffusion model (linear with t1/2) suitably describes the blooming process. Both Spearman ( = 1.0, p = 0.000) and Pearson (r > 0.91) correlations were strong, validating the method has ability to accurately capture the temporal dynamics of blooming.
    Keywords: -test statistics; blooming; vulcanised rubber; image processing; euclidean distance; aesthetic quality of rubber; mechanical properties.
    DOI: 10.1504/IJMR.2025.10072983
     
  • A Meta-Heuristic-Based Framework for Sustainable P-hub Network Design of Perishable Items under Fuzzy Time Uncertainty   Order a copy of this article
    by Saeed Zameni, Seyed Esmaeil Najafi, Seyed Mohammad Hajimolana, Seyed Mojtaba Sajadi 
    Abstract: In this paper, a novel mathematical model is presented for designing a sustainable hub network for perishable commodity transportation, taking into account social responsibility, environmental impact, and economic viability. As many real world problems have non deterministic parameters, the time parameters are considered fuzzy numbers in the model. To validate the model, the model is solved on a small scale using GAMS software after linearisation. However, due to the nondeterministic polynomial time nature of the problem, an efficient meta-heuristic algorithm is proposed using MATLAB software. The algorithm has been validated on small and medium scale instances using the AP and CAB datasets. The results show that the proposed NSGA-II algorithm achieves an average solution gap of 0.017% while significantly reducing computational time compared to exact methods. The proposed model and algorithm can assist decision makers in designing sustainable and efficient supply chain networks for perishable products.
    Keywords: P-Hub location problem; Sustainable supply systems; Food logistics network; Fuzzy multi-objective nonlinear optimisation; Perishable goods.
    DOI: 10.1504/IJMR.2025.10073024
     

 

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