International Journal of Engineering Systems Modelling and Simulation (14 papers in press)

Regular Issues

• Intelligent correlation and optimisation of CNC machining parameters for enhanced 3D modelling accuracy in mould manufacturing  
  by Meihua Shi 
  Abstract: This study proposes an intelligent hybrid framework for determining optimal CNC machining parameters to improve the precision of 3D mould modelling. Using statistical experimentation, machine learning algorithms, and multi-objective optimisation, the research shows that machining inputs such as spindle speed, feed rate, depth of cut, and step over affect key performance indicators, including surface roughness (Ra) and dimensional deviation (Dd). A large factorial experimental design included 81 CNC trials. We have lots of data to train and test our models. Spindle speed and feed rate accounted for 61.6% of the variation in machining quality, as determined by statistical analysis and PCA. The study predicted machining outcomes using advanced modelling methods, such as RSM, RF, SVM, and ANN. The ANN model outperformed others (R2 = 0.948, RMSE = 0.0072 mm) by accurately representing nonlinear interactions. The SHAP explainable AI study indicated that spindle speed and feed rate most affect Ra and Dd. The study found the optimal parameter combinations using NSGA-II and Monte Carlo simulations. The optimal spindle speed was 3850 rpm, and the feed rate was 220 mm/min. These settings conserved energy and increased machining accuracy by 15.2%. This framework creates clever, eco-friendly, and adaptable CNC machining systems.
  Keywords: CNC machining optimisation; 3D modelling accuracy; machine learning; ML; surface roughness; Ra; multi-objective optimisation.
  DOI: 10.1504/IJESMS.2026.10079467
   
  
• Optimising railway delays: a comparative analysis of advanced algorithms for minimising delays  
  by Shyamal Sundar Banerjee, Avijit Bhattacharyya, Subhash Chandra Panja 
  Abstract: Railway delays can significantly impact the efficiency of train operations and the satisfaction of passengers. This paper provides a comparative analysis of advanced algorithms for minimising railway delays. The algorithms examined include the Hungarian method, integer linear programming, genetic algorithm, simulated annealing, Tabu search, ant colony optimisation, and frog optimisation. The objective is to determine the most effective algorithm for optimising railway schedules and minimising delays.
  Keywords: railway delays; optimisation algorithms; Hungarian method; integer linear programming; genetic algorithm; simulated annealing; Tabu search; ant colony optimisation; ACO; frog optimisation.
  DOI: 10.1504/IJESMS.2026.10080088
   
  
• Wavelet-enhanced deep learning framework for real-time defect identification in friction stir welding  
  by B. Sudha, Kathiravan Srinivasan 
  Abstract: Friction stir welding (FSW) is widely used in shipbuilding, automotive, and aerospace to weld light, high-strength materials without melting. The processs tendency to generate voids, tunnelling flaws, and insufficient fusion weakens the joint. Detecting subtle or subsurface faults with visual inspection, X-ray radiography, and ultrasonic testing is time-consuming, expensive, and ineffective. Traditional machine learning methods that use manually collected characteristics cannot generalise across weld settings. The work offers a wavelet-based deep learning model for autonomous FSW joint defect inspection to overcome these limits. Weld signal time-domain and spatial-frequency characteristics from sensor readings are modelled using the DWT. Multi-resolution characteristics fed into deep Q-network learning aid fault determination and spatial feature extraction. Wavelet decomposition greatly improves the models sensitivity to tiny signal fluctuations indicating hidden defects. A vast database of FSW samples with varying defects and process parameters was used to evaluate the suggested approach. The studies had 97.8% flaw-detection accuracy, much greater than many sophisticated baseline models. Results show that Wavelet-DQL enrichment improves results. This study validates the Wavelet-DQL as a scalable, effective, and highly accurate real-time, non-destructive FSW defect diagnosis method for Industry 4.0 production system quality assurance.
  Keywords: deep learning; defect identification; friction stir welding; FSW; non-destructive evaluation; NDE; wavelet transform; discrete wavelet transform; DWT.
  DOI: 10.1504/IJESMS.2026.10080121
   
  
• Energy consumption reduction in architectural by using smart thermobimetal to as a sun shading optimal transmission of natural light  
  by Xiaodan Liang, Dan Wang 
  Abstract: This study examines the application of smart thermobimetals to architectural facades to reduce energy consumption and enhance daylighting in buildings. Thermobimetals, which deform in response to temperature fluctuations, offer a passive approach to energy-efficient architectural design by adaptively responding to ambient conditions. This study integrates experimental data and computational software (DesignBuilder, EnergyPlus) to assess the efficacy of thermobimetal sunshades in a Tehran-based building, emphasising the reduction of cooling loads and enhancement of natural light infiltration. The research indicates that incorporating thermobimetals into the facade design achieved a 41% decrease in energy usage, equating to an annual savings of about 350,000 kWh, alongside a reduction of 6,000 kg of CO2 emissions. These findings underscore the considerable potential of thermobimetals in diminishing the energy requirements of buildings, especially in areas with elevated solar radiation. The findings further illustrate the advantages of employing thermobimetals in improving occupant comfort through the regulation of ideal daylighting levels and the mitigation of glare. This study provides significant insights into sustainable building design, demonstrating thermobimetals as an efficient alternative for energy-efficient architecture. The results are especially pertinent for structures in regions with significant sun exposure, where intelligent materials can greatly influence energy conservation and environmental sustainability.
  Keywords: natural light; reduction of energy consumption; architectural space; thermo bimetal; smart material.
  DOI: 10.1504/IJESMS.2026.10080426
   
  
• Enhancing smart metre security in smart grids using Spritz stream cipher  
  by S. Shabana Banu, M.S. Sujatha 
  Abstract: Supervisory control and data acquisition (SCADA) systems have been integrated into todays power grid infrastructure, achieving the automation of electricity generation, transmission and distribution and is now known as the Smart Grid. But this greater level of inter-connectivity also creates new cyber security risks such as spoofing, man-in-the-middle (MITM) attacks and data manipulation. The confidentiality, integrity and availability CIA of critical data transferred between smart metres and substation by these risk sensors and substations may be compromised resulting in financial loss, operations degradations, reduced public confidence. To address these concerns, the cryptographic stream cypher Spritz is proposed as a suitable encryption algorithm for using in this work. Spritz provides better security with higher randomness and is lightweight enough for smart metres. By integrating Spritz into smart grid communication systems, the proposed approach enhances data confidentiality and integrity while maintaining computational efficiency, thereby supporting a more secure and resilient smart grid infrastructure.
  Keywords: cybersecurity; data integrity; SCADA; Spritz encryption; smart metres.
  DOI: 10.1504/IJESMS.2026.10080474
   
  
• Adaptive protection strategies for critical infrastructure systems leveraging real-time threat intelligence  
  by P.V. Bhuvaneshwari, P. Sudhakar, Abhishek Julka, V.S. Devi Priya, G. Ragu, A. Anithalakshmi 
  Abstract: Critical infrastructure (CI) networks such as power grids, water supply systems, and transport networks become more and more susceptible to new-generation physical and cyber threats. A new adaptive protection policy architecture that uses real-time threat intelligence to dynamically adjust security is presented and examined in this study. A hidden feature of the proposed structure is an AI system that feeds threat data to predict attacks. We used a synthetically generated dataset, CI-Threat-Sim-2024, consisting of 480 threats against a simulation of a regional electric power grid model. Each instance includes a richer set of features, including network traffic abnormalities, sensor physical notices, and threat intelligence sources. Python was used to develop and train an adaptive defensive model utilising Scikit-learn for machine learning, Pandas for data manipulation, and Matplotlib/Seaborn for data visualisation. A random forest classifier classifies threats nature and intensity, and the model dynamically allocates defensive resources very accurately. The report states that real-time threat intelligence boosts critical infrastructure resilience by creating a dynamic, intelligent protection mechanism. This study is the best proof that threat intelligence and intelligent systems can create an invulnerable and more secure system for our nations most important assets.
  Keywords: adaptive security; critical infrastructure protection; machine learning; anomaly detection; cybersecurity; real-time threat intelligence; risk management; resilience.
  DOI: 10.1504/IJESMS.2026.10080600
   
  
• Implementation of experimental model of vertical farming using IoT technology  
  by D. Sindhanaiselvi, T. Shanmuganantham, S. Ashok Kumar 
  Abstract: Vertical farming is a potential solution to the current food crisis, as it enables crop cultivation using vertically stacked layers. Beyond addressing food scarcity, this approach promotes organic self-vegetation and can be customised for widespread adoption. This paper presents the motivation and development of a prototype vertical farming model. The vertical stack is designed to support both soil-based and soilless cultivation under automated conditions. The system is developed using a closed-loop control mechanism incorporating simple sensors and motors to ensure efficient year-round crop production. Sensor calibration for accurate measurements, local display of sensor data, and communication with a control room are also implemented. The prototype employs drip irrigation to conserve water, and essential parameters are remotely monitored using an IoT application. Additionally, the overall implementation cost is minimised through the use of MEMS sensors.
  Keywords: vertical farming; internet of things; IoT; MEMS sensor; remote monitoring.
  DOI: 10.1504/IJESMS.2026.10081116
   
  
• Design and implementation of an area-efficient piece-wise linear multiplier for MAC architectures  
  by K. Vijetha, R. Kiran Kumar, Naga Jyothi Grande, Arun Kumar Katkoori, Koteswararao Seelam, Kakarla Hari Kishore, S.A. Kumar 
  Abstract: Digital signal processors (DSPs), artificial intelligence (AI) machines, and other similar devices rely on multiply-accumulate (MAC) units as their backbone. In addition, the size of the multipliers also has significant effects on both area and power. This paper proposes a hardware efficient MAC unit based on a piecewise linear multiplier (PLM). The proposed design was developed using the Verilog HDL language and was synthesised and implemented in a 90 nm CMOS technology using the Cadence Genus and Innovus tools. Experimental results demonstrate that the proposed MAC architecture provides a 29% area saving and a 39% energy saving relative to traditional MAC architectures. While operating at 510 MHz with a maximum delay of 1.96 ns, it also maintains the necessary accuracy for most practical DSP applications. Therefore, the proposed PLM-MAC architecture demonstrates an effective trade-off between computation accuracy, area efficiency and power consumption.
  Keywords: piecewise linear multiplier; accumulate and signal processing.
  DOI: 10.1504/IJESMS.2026.10081117
   
  
• Barriers to Industry 4.0 adoption in the traditional textile industry - a structural equation modelling study of Rajasthans block-printing clusters  
  by Manoj Sharma 
  Abstract: Utilising Industry 4.0 can help boost competitiveness and sustainability of traditional production, but the use in the artisanal industry of emergent markets is under-researched. This paper examines adoption issues in the historic block-printing textile districts of Rajasthan, which is a culturally and economically important Indian sector. By applying systematical literature review and professional validation, twelve of these barriers were found and grouped into technical and infrastructure, organisational and human capital, financial and economic, and external and strategic clusters. The structural equation modelling (SmartPLS-4) was used to examine the survey data of 24 enterprises in the large printing centres (Sanganer, Bagru, Dabu, Ajrak, Sikar, Pali). The results indicate technical and infrastructure barriers as the most daunting challenge with a critical digital divide being the main issue among resource-restrained SMEs. Differentiated actions are given the strength of diagnostic framework, sustainable modernisation adoption plans.
  Keywords: textiles industries; sustainable; Industry 4.0; structural equation modelling; SEM; barriers; Rajasthan.
  DOI: 10.1504/IJESMS.2026.10081170
   
  
• A heuristic deep learning framework for brain tumour classification and segmentation in medical imaging  
  by M.S. Prasad, P.K. Poladi, A. Manjula 
  Abstract: Brain tumours are among the most critical health challenges, requiring accurate and timely diagnosis for effective treatment. This research presents a novel heuristic model leveraging artificial intelligence (AI) for the classification and segmentation of brain tumour images. The proposed model incorporates a hybrid approach, combining deep learning architectures with domain-specific heuristics to enhance precision and reliability. A comprehensive dataset comprising diverse tumour types, sizes, and imaging modalities was curated, pre-processed, and used for model training and validation. The results demonstrate significant improvements in classification accuracy and segmentation performance, supporting clinical workflows in radiology and oncology. The study emphasises the importance of collaboration between technology and healthcare professionals for translational AI in medical diagnostics.
  Keywords: brain tumour image; heuristic model; AI; segmentation.
  DOI: 10.1504/IJESMS.2026.10081358
   
  
• Advanced prediction of traffic flow using hybrid models and novel optimisation algorithms  
  by Dan Ji 
  Abstract: Despite transport growth, urban population rise worsens traffic issues, boosting interest in ML-based traffic prediction approaches. This study aims to propose new machine learning models for the prediction of traffic flow utilising extra trees regression (ETR), CATBoost regression (CATR), and adaptive neuro-fuzzy inference system (ANFIS). Novel optimisation algorithms are employed to optimise the performance of prediction models, including the botox optimisation algorithm (BOA) and orangutan optimisation algorithm (OOA). Integration of models with optimisation algorithms makes six hybrid models, including ETBA (ETR + BOA), ETOA (ETR + OOA), CABA (CATR + BOA), CAOA (CATR + OOA), ANBA (ANFIS + BOA), and ANOA (ANFIS + OOA). For feature selection and sensitivity analysis in this study, the variance inflation factor and the Cousin amplitude method, respectively, have been utilised. Among all single and hybrid models, CAOA, with RMSE and R2 values of 0.0325 and 0.9828, correspondingly, with a slightly better performance compared to CABA, is the best model of the study.
  Keywords: traffic flow; machine learning; ML; hybrid models; optimisation algorithms; cousin amplitude method.
  DOI: 10.1504/IJESMS.2026.10081468
   
  
• A novel method for thermal failure prediction and analysis in lithium-sulphur battery systems  
  by G. Sugin Lal, Swakantik Mishra, S. Leoni Sharmila, R. Regin, D. Kerana Hanirex, P.V. Bhuvaneshwari, Junshuai Lu 
  Abstract: The paper presents a forecasting model for determining the thermal breakdown temperature in lithium-sulphur battery systems, which have been frequently mentioned as the next generation of high-capacity energy storage. In contrast to conventional systems, lithium-sulphur chemistries exhibit complex thermal characteristics, including the polysulfide shuttle effect and variations in internal resistance. We employ a new ensemble learning technique to forecast temperature spikes before they develop into thermal runaway. A specialised dataset comprising 184 experimental cases is used in the experiment to measure voltage drops, discharge rates, and ambient temperatures. We were able to map the relationship between internal pressure and heat generation using sophisticated computational software, including Python-based predictive modelling and high-fidelity thermal simulation. The findings show that the suggested approach achieves high accuracy in early warning detection, providing a large safety margin for battery management systems. The study can inform the adoption of active safety measures in electric vehicles and aerospace systems, where thermal stability is the primary factor.
  Keywords: lithium-sulphur batteries; thermal runaway; failure prediction; energy storage; safety management; battery management systems; high-fidelity thermal simulation software; thermal stability.
  DOI: 10.1504/IJESMS.2026.10081535
   
  
• Capacity and SoC estimation of Li-ion battery based on enhanced sigma point Kalman filter with consideration of fading effect  
  by Geetha Ravali Gaddipati, Kuthuri Narasimha Raju 
  Abstract: Accurate models that closely represent the true physical behaviour of the cells are required for reliable management of lithium-ion batteries in modern electric vehicles as they are largely driven by internal states. Existing mathematical models can capture nonlinearities over short time scales including diffusion and hysteresis, but typically neglect the gradual, nonlinear capacity fade that occurs as cells age. This omission complicates simultaneous state of charge (SoC) and maximum available capacity estimation since short-term dynamics and long-term degradation are interdependent. A constant capacity assumption still persists in current battery management systems, leading to substantial tracking errors and uncorrected sensor drift with ageing. To overcome this, a new degradation-adaptable sigma point Kalman filter is implemented in the proposed framework. An innovative capacity-fade model with temperature-rate effect and cycle counts is incorporated into a second-order equivalent circuit. With deterministic sampling, the filter guarantees to be accurate with SoC error within 1% for up to 1,000 cycles, outperforming state of the art.
  Keywords: lithium-ion battery; state of charge; SoC; capacity degradation model; sigma point Kalman filter; SPKF; battery management system; BMS.
  DOI: 10.1504/IJESMS.2026.10081666
   
  
• Vision-based hand gesture classification using convolutional and recurrent neural networks  
  by Koteswara Rao Seelam, Venkata Reddy Adama, Rohini Hanchate, Roomana Hasan, Renuka Sandeep Gound, Ashok Kumar Srinivasan 
  Abstract: Hand gestures serve as a natural and intuitive form of nonverbal communication, playing a crucial role in applications such as sign language interpretation, human-computer interaction (HCI), robotics, home automation, and healthcare. Research in hand gesture recognition has explored various methodologies, including sensor-based techniques and computer vision approaches. Among these, vision-based recognition stands out for its ability to provide seamless interaction without the need for physical input devices like keyboards and mice. As immersive virtual environments gain prominence, traditional input methods become impractical, driving the need for efficient and adaptive gesture recognition systems. This paper presents a comprehensive review of computer vision-based gesture recognition techniques, examining key aspects such as hand segmentation, classification algorithms, datasets, detection range, and camera technologies. By leveraging advancements in deep learning and image processing, modern gesture recognition systems enhance user interaction across diverse applications, fostering more intuitive and efficient human-machine communication.
  Keywords: HCI; neural networks; hand gesture; convolutional method.
  DOI: 10.1504/IJESMS.2026.10081667