International Journal of Simulation and Process Modelling (9 papers in press)

Regular Issues

• Open Access

  Simulation modelling of fashion colour harmonisation with visual transformers ( Free Full-text Access )

  by Bei Li 
  Abstract: In the field of fashion design, colour coordination is a critical factor in enhancing market competitiveness. This study aims to develop a simulation model for colour coordination within the fashion design process to improve its quality. First, the simulation model was constructed based on a visual transformer. This model treats the visual transformer as a simulation of the designer's decision-making process. By learning from a large dataset of fashion images, it captures intrinsic patterns between colours and simulates the decision logic designers employ when selecting and coordinating colours. As a workflow simulation model, it focuses not only on colour coordination within images but also on emulating the fashion design workflow itself. Simulation experiments conducted on the dataset demonstrate that the proposed model achieves an image quality distance of 5.09 and a colour richness of 40.14, outperforming the comparison model. Significant improvements are observed in colour harmony and image generation quality.
  Keywords: fashion design; image colour coordination; visual transformer; ViT; process simulation; workflow modelling.
  DOI: 10.1504/IJSPM.2026.10077921
   
  

• Open Access

  Generative adversarial networks for simulating emotional resonance in industrial product design ( Free Full-text Access )

  by Jie Hu 
  Abstract: This paper addresses the lack of emotion-oriented simulation in industrial product design by proposing a novel generative adversarial network framework integrated with a quantifiable emotional model. The core of this approach is an emotion-attention mechanism that dynamically guides the form evolution process. Emotional features are first extracted from e-commerce reviews and modelled via an improved support vector regression algorithm to establish a quantifiable mapping between design elements and user emotions. This emotional model is then integrated into a GAN through a multi-head component attention module, which simulates product form evolution by explicitly weighting the contribution of each component to the target emotional resonance. Experimental results demonstrate the effectiveness of this simulation, with the Fréchet inception distance reduced by at least 38.06%, enabling the generation of industrial products that accurately align with user emotional needs.
  Keywords: product form simulation; process modelling; emotional resonance; generative adversarial networks; GAN; support vector regression; SVR.
  DOI: 10.1504/IJSPM.2026.10077922
   
  
• Simulation-driven deep learning framework for early poultry disease detection using faecal image classification with optimised CNN architectures  
  by Nonita Sharma, Monika Mangla, Manik Rakhra, Baljinder Kaur, Raj Kumar Mohanta, Bijay Kumar Paikaray 
  Abstract: The present investigation aims to devise an optimized Convolutional Neural Network (CNN) framework to identify prominent poultry diseases based on faecal images. The proposed research model uses a tri-convolutional layer architecture to enhance the accuracy of poultry disease classification and improve feature extraction. Three major diseases, namely coccidiosis, salmonella, and Newcastle, have been considered. The prime objective of current research is to achieve early detection by employing advanced deep-learning techniques. The proposed model uses fecal images to identify pathological conditions using the TriConvLayer architecture accurately. In this work, custom CNN models, viz. SoloConvLayer, TriConvLayer, and FiveConvLayer models are used to achieve an accuracy of 97%, 98%, and 98%, respectively. The achieved result advocates the efficacy of the proposed approach. It thus has the potential to revolutionize early disease detection in poultry farming, a major step towards improving animal health and farm productivity.
  Keywords: deep learning; poultry disease detection; convolutional neural networks; CNN; faecal image analysis; poultry health monitoring; disease classification; simulation framework.
  DOI: 10.1504/IJSPM.2025.10073525
   
  
• Modelling and simulation of a non-contact electromagnetic damping process for enhanced band saw performance  
  by Xuexuan Tao, Zixuan Li, Qingbo Yu, Yundong Chen, Yi Wu, Zhiyi Wei 
  Abstract: This paper presents a simulation-driven methodology to address the issues of reduced machining precision, poor surface quality, and shortened service life in horizontal band saws caused by high-speed blade vibration. A coupled electromagnetic-structural process model was developed using finite element analysis to simulate the interaction between a novel non-contact electromagnetic vibration damper and the saw blade. Systematic parametric simulations identified an optimal relative air gap ratio (a=0.4) that maximizes the electromagnetic damping effect. Analysis indicated that a single-damper configuration alone could achieve a magnetic flux density of 1.48 T in the blade's vibration zone and reduce vibration displacement by 66.7%. The optimized symmetrical dual-damper configuration, derived from simulation insights, generated a higher magnetic flux density and induced an eddy current distribution that decreased from the surface inward, resulting in faster vibration decay and significantly lower residual amplitude. Experimental validation confirmed the model's accuracy. The study demonstrates how simulation-based process modelling can replace trial-and-error in damper design and optimization, providing a transferable framework for vibration suppression in high-speed cutting tools.
  Keywords: eddy current; manufacturing processes; non-contact damping; parametric optimisation; process modelling; vibration suppression.
  DOI: 10.1504/IJSPM.2026.10078820
   
  
• Process-oriented simulation and optimisation of 2D trabecular bone via fractal modelling  
  by Yuhong Liu, Liyan Jia, Zheng Wang, Jincai Chang 
  Abstract: A process-oriented method for simulating and reconstructing 2D trabecular bone microstructures is proposed, based on fractional Brownian motion (FBM) and multi-parameter morphological constraints. By adjusting the Hurst exponent, the fractal dimension (FD) of trabecular structures can be controlled, enabling the reconstruction of biomimetic porous geometries with tunable porosity and thickness. A complete modelling framework is developed, including image pre-processing, parameter extraction, simulation generation, and post-optimisation, and is validated using real femoral cross-sectional images. Comparative results show that this method outperforms existing graph convolutional networks based approaches in preserving topological fidelity and geometric fidelity. This study provides a novel, simulation-driven pathway for customisable bone scaffold design and process modelling in bioengineering.
  Keywords: fractal modelling; trabecular bone structure; 2D geometric reconstruction; parameter-driven simulation; morphological optimisation.
  DOI: 10.1504/IJSPM.2025.10076820
   
  
• Modelling herd behaviour in traffic jams using Markov chains-based reinforcement learning  
  by Yamina Heddar, Youcef-Oussama Fourar, Mébarek Djebabra 
  Abstract: Traffic congestion remains a persistent problem that compromises road safety. This phenomenon is often amplified by driver behaviours, particularly those characterised by the herd effect. This study aims to model the emergence and dynamics of the herd effect in traffic jams and to simulate a strategy for mitigating this behaviour among drivers. To achieve these objectives, reinforcement learning (RL) was employed within the frameworks of memoryless Markov chains and multi-phase Markov chains. The results demonstrate the effectiveness of Markov chains in accurately modelling the collective behaviour of specific drivers. Likewise, the simulations illustrate RL's capacity to regulate the herd effect and optimise individual decision-making during congestion. The findings suggest that traffic authorities may consider implementing RL-based strategies to mitigate herd behaviour, improve traffic flow, and enhance road safety.
  Keywords: herding behaviour; traffic jams; Markov chains; reinforcement learning; agent.
  DOI: 10.1504/IJSPM.2026.10077670
   
  
• Optimising U-turn efficiency at median openings: a simulation study of traffic-responsive signal control  
  by Weidong Liu, Shanshan LI, Runtong Qiao 
  Abstract: This study proposes a methodologically novel approach to optimise the simulation and control of U-turn movements at central median openings. By integrating traffic dynamics in opposing lanes, we develop a parameterised signal control logic that determines optimal headway detector placement and calculates minimum green duration for U-turn phases, enabling adaptive phase adjustments. The model enhances simulation fidelity by directly linking field-observed traffic parameters to signal control logic in VISSIM. A calibrated micro simulation framework is established using real-world data, allowing for dynamic calibration and validation of control strategies. Comparative simulations demonstrate that the proposed method significantly improves traffic efficiency - reducing vehicle delays and increasing travel speed - particularly during peak periods. This work advances the simulation-driven optimisation of urban median openings, offering a replicable framework for adaptive signal control in complex traffic environments.
  Keywords: adaptive signal control; median openings; U-turn; microscopic simulation; traffic management.
  DOI: 10.1504/IJSPM.2026.10077669
   
  
• Enhanced redundancy allocation in biomedical manufacturing using opposition-based grey wolf optimiser  
  by Alina Banerjee, Deepika Garg, Mir Mohsin John 
  Abstract: In the current environment of epidemics like COVID-19, biomedical gadgets are crucial. The production of biomedical devices greatly contributes to the community's ability to combat these diseases. This study aims to optimise redundancy in biomedical device production facilities due to globalisation and sophisticated machinery. Reliability optimisation is crucial for complex systems, and the redundancy allocation problem (RAP) is a significant optimisation problem in reliability engineering. Scholars are increasingly interested in RAP due to its significance in reliability and system engineering. This study compares grey wolf optimiser and opposition-based learning in high-tech sectors. Grey wolf optimiser enhances exploratory behaviour while maintaining a rapid convergence rate. The modified version of grey wolf optimiser solves the redundancy allocation problem, comparing outcomes with modified particle swarm optimisation. The study highlights the importance of utilising metaheuristic algorithms for optimisation.
  Keywords: redundancy allocation; opposition-based learning; OBL; particle swarm optimisation; PSO; grey wolf optimisation.
  DOI: 10.1504/IJSPM.2026.10077844
   
  
• A hybrid PSO-BP-PSO method for DEM parameter calibration of moist granular materials  
  by Qiansheng Tang, Haibo Yang, Wuxu Sheng, Zhaoqing Cui, Liangyuan Xu 
  Abstract: To explore an efficient method to calibrate the discrete element model (DEM) parameters of piglet feed, a typical moist granular material, an experimental study under different water-to-feed ratios was conducted using the stacking angle as the evaluation index. The relation between the stacking angle and the water-to-feed ratio was obtained by the physical tests. With the Hertz-Mindlin with Johnson-Kendall-Roberts (JKR), the DEM of piglet feed was established. The Plackett-Burman and steepest ascent method was utilised to select significant parameters and their ranges. Finally, the particle swarm optimisation (PSO) - back propagation (BP) - PSO algorithm was used to implement the parameter calibration of the piglet feed with arbitrary water-to-feed ratios. The results show: the stacking angles of the DEM of piglet feed are highly consistent with those from physical tests and the relative error is less than 1.01%. The proposed method shows better accuracy than response surface methodology (RSM).
  Keywords: parameter calibration; PSO-BP-PSO algorithm; piglet feed; stacking angle; EDM; simulation; response surface methodology.
  DOI: 10.1504/IJSPM.2026.10078262