Forthcoming and Online First Articles

International Journal of Design Engineering

International Journal of Design Engineering (IJDE)

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 Articles will not be published until the final proofs are validated by their authors.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

Regular Issues

  • Physical, Mechanical and Thermal behaviour of 3D Printed PLA/Wood Biocomposites   Order a copy of this article
    by Ramesh Mari, Nirmal Raj Lainal Chinnappan, Rajeshkumar L 
    Abstract: The quest for developing environmentally friendly composites has forced researchers to go for natural materials for various engineering applications. Natural fibre composites have become the order of the day owing to their better characteristics like biodegradability which is lacking in synthetic fibre-based materials. Additive manufacturing (AM) or 3D printing has also been widely used for fabricating natural fibre composites owing to its adaptability to these materials. In the current work, polylactic acid (PLA) and wood composites were manufactured using the fused filament fabrication (FFF) technique with 30 % wood content in PLA, and the effect of wood content in PLA was evaluated using physical, mechanical, and thermal analysis. Water absorption test results indicated that the inclusion of wood particles increased the hydrophilicity of the PLA/wood biocomposites. Results portrayed that the mechanical and thermal properties were high for PLA/wood biocomposites. These composites find their application in low and medium-density semi-structural applications.
    Keywords: polylactic acid; PLA; wood particles; 3D printing; mechanical properties; thermal properties; water absorption.
    DOI: 10.1504/IJDE.2024.10064275
     
  • Taguchi's Technique for Optimising FSPCM Parameters for Drying Application   Order a copy of this article
    by Kalpeshkumar Patel, Tushar M. Patel 
    Abstract: Food preservation requires a significant amount of thermal energy to eliminate moisture from agricultural goods; solar energy is becoming increasingly popular. Phase change material (PCM) was identified through a decision-making approach for this study, based on selected characteristics, utilising TOPSIS strategies. Due to the multitude of factors and levels, the possible models are numerous. Consequently, a considerable amount of modelling and analytical work is necessary to select the best parameters among them, requiring additional time. In this experiment, a mixed-mode indirect solar dryer (MMISD) with three distinct combinations of size, position, mass flow rate of air, PCM and clay percentage is employed. To address this issue, the Taguchi approach is utilised to determine the most effective FSPCM combination for drying agricultural products. Based on S/N ratio and means of means analysis, the percentage of Kaolin is identified as the most effective and tray position is the least effective parameter.
    Keywords: mixed mode solar dryer; freeze storage phase change material; FSPCM; Taguchi method; MADM; TOPSIS.
    DOI: 10.1504/IJDE.2024.10064731
     
  • A Neural Network Modelling for Conventional and Stepped Solar Still Productivity using Design Parameters   Order a copy of this article
    by Krunalkumar Patel, Tushar M. Patel 
    Abstract: Potable water resources are seldom accessible in remote and arid regions, making solar stills a valuable asset for such areas. Productivity is a crucial consideration in solar stills. This interplay is influenced by even slight alterations in the input parameters of the solar still. Therefore, a modified solar still is being examined to enhance productivity. The development of a neural network (NN) is necessary for commercial applications to potentially predict productivity under varying water flow rates, depths, and glass thicknesses. Conventional and stepped solar stills were fabricated to enable a more effective comparison. Through experimentation and NN modelling, it has been observed that the NN model significantly reduces the time and effort required for conducting numerous experimental sets. The LM9TP training model yielded mean squared and R-squared values of 4.8718E-05 and 1 for training and 1.5315E-06 and 0.9998 for testing, respectively. Consequently, the ANN's projected productivity values closely align with the experimental data, demonstrating a high degree of similarity.
    Keywords: conventional solar still; stepped solar still; water depth; glass thickness; mass flow rate; neural network.
    DOI: 10.1504/IJDE.2024.10065853