Research Papers - Department of Mechanical Engineering
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Publication Open Access Topology and size optimization of trusses using graph neural networks: Towards efficient surrogate modeling(Elsevier Ltd, 2026-06) Ariyasinghe, N; Wickremasinghe, T; Weeratunge, H; Mallikarachchi, C; Herath, SReal-time structural optimization of trusses using machine learning techniques, incorporating both topology and size optimization, is more effective in discrete domains than in continuous ones, with Graph Neural Networks (GNNs) showing strong potential. However, the impact of convolutions in GNNs is not yet fully understood, limiting their full applicability. This paper presents a GNN-based surrogate model for real-time structural optimization and identifies the most suitable convolution type for this task. The study assesses the predictive performance of models trained on a dataset of optimized structures spanning a range of loads, boundary conditions, and design domain sizes. The resulting model effectively predicts both optimal topology and member sizes once the design parameters are provided. Eight graph convolution types are investigated to identify the most suitable method, alongside an evaluation of optimal network architectures. Among the tested approaches, Generalised Graph Convolution achieves the highest accuracy, followed by Topology Adaptive Graph Convolution, producing near-ideal topology predictions for most test cases and maintaining section size prediction errors within ±2% across all test data points. This framework demonstrates strong potential for broader applications.Publication Open Access Integration of industry 4.0 technologies to overcome lean manufacturing barriers in Sri Lanka’s apparel sector(Emerald Publishing, 2026-02-09) Silva, N; Hettiarachchi, D. I; Perera, P; Perera, CPurpose – This study aims to examine how Industry 4.0 (I4.0) technologies can enable Lean Manufacturing (LM) practices in Sri Lanka’s apparel industry. Although LM has been widely adopted to improve efficiency and reduce waste, persistent barriers such as frequent product changes, limited real-time visibility and infrastructural constraints have restricted its full potential. The purpose of this research is to explore how advanced digital solutions, including Internet of Things (IoT), real-time analytics and augmented/virtual reality (AR/VR), can address these barriers and enhance the competitiveness and sustainability of apparel manufacturing in a dynamic global market. Design/methodology/approach – A qualitative single-case study design was used to provide an in-depth understanding of digital–lean integration. The research was conducted in collaboration with a leading Sri Lankan apparel manufacturer. Data were collected through on-site factory observations, semi-structured interviews with managers and employees and examination of company records. Using Yin’s (2018) case study methodology as a guiding framework, the study analyzed how selected I4.0 technologies were implemented alongside lean tools and how these interventions addressed identified operational inefficiencies. Findings – The study found that I4.0-enabled solutions significantly enhanced lean practices by improving production workflow transparency, defect detection and downtime reduction. Tools such as IoT-linked dashboards, electronic Kanban systems and automated performance monitoring minimized non-value-adding activities and reduced bottlenecks. AR/VR applications demonstrated potential for training and machine setup, while predictive maintenance improved equipment reliability. However, the research also identified persistent shortcomings, including data confidentiality issues, workforce adaptability challenges and high capital investment requirements. The findings highlight both the opportunities and practical limitations of integrating digital technologies into lean environments. Research limitations/implications – The research was limited to a single case study of a large apparel manufacturer in Sri Lanka, which constrains the generalizability of findings. Data confidentiality policies restricted access to detailed financial information, preventing quantitative analysis of productivity gains and return on investment. Future studies could extend this research by including multiple firms across varying scales and geographies, enabling comparative insights. Broader empirical studies that quantify the financial outcomes of digital–lean integration would provide further validation and support for industry-wide adoption. Practical implications – For practitioners, the study offers a roadmap for integrating I4.0 technologies with lean practices in apparel manufacturing. The evidence suggests that digital lean tools can enhance transparency, improve workflow efficiency and support more accurate decision-making. Managers should prioritize investments in IoT-enabled monitoring, predictive maintenance and digital visual management systems while addressing workforce readiness through training programs. Attention must also be given to cybersecurity and change management to ensure sustainable implementation. These findings are particularly relevant for resource-constrained firms seeking to maximize operational efficiency while navigating global competitive pressures. Social implications – The integration of I4.0 and LM in Sri Lanka’s apparel sector holds broader social benefits by safeguarding employment in a critical export industry that provides livelihoods for over 300, 000 workers. Enhanced productivity and competitiveness contribute to economic stability and foreign exchange earnings. Moreover, digital lean practices can reduce waste, contributing to environmental sustainability and aligning with global sustainable development goals. By strengthening the resilience of the apparel sector, these advancements can help sustain jobs and improve working conditions, particularly in developing country contexts where apparel remains a cornerstone of industrial growth. Originality/value – This study provides one of the first in-depth examinations of how I4.0 technologies can act as enablers of LM in the Sri Lankan apparel industry. Unlike prior studies that treat lean and digital transformation as separate trajectories, this research highlights their synergies and tradeoffs in practice. By capturing both the benefits and shortcomings of digital lean tools, the paper contributes to theory by extending understanding of lean–I4.0 integration in emerging economy contexts. It also offers practical value by providing industry-specific insights that can inform managers’ strategic decisions on digital transformation.Publication Embargo Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study(Taylor and Francis Ltd., 2026) Fernando, W. J; Silva, N; Perera, CTimely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.Publication Open Access Self-starting characteristics and dynamic response of a free-spinning cross-flow air turbine for oscillating water columns under irregular wave conditions(Elsevier Ltd, 2026-02-24) Baddegamage B.H.B.P.D; Bae, S.J; Gunawardane S.D.G.S.P.; Lee, Y.H; Kim, K; Yoon, MThe cross-flow air turbine (CFAT) has been proposed as a self-rectifying device for oscillating water column (OWC) wave energy converters as an alternative to conventional Wells and impulse turbines. While previous studies have primarily focused on steady or regular flow conditions, the self-starting behavior and transient response of a free-spinning CFAT under irregular, bidirectional inflow representative of realistic sea states have not yet been investigated. This study presents a fully transient computational fluid dynamics analysis of a free-spinning CFAT operating under irregular airflow conditions derived from the JONSWAP spectrum. The simulations were performed under no-load conditions to isolate the intrinsic aerodynamic torque generation and evaluate self-starting capability. The effects of significant wave height and spectral peak period on turbine startup and unsteady aerodynamic response were systematically examined in both the time and frequency domains. The CFAT consistently initiates rotation without external assistance and reaches quasi-steady operation within 25–30 oscillation cycles. For significant wave heights ranging from 0.0375 m to 0.05 m, the mean instantaneous efficiency varies between 0.24 and 0.52, while efficiencies between 0.30 and 0.59 are obtained for spectral peak periods from 1.50 s to 1.88 s. Furthermore, wave-grouping effects play a decisive role in accelerating the turbine toward its equilibrium speed. Torque and pressure fluctuations closely follow the inflow velocity profile, with hysteresis-like behavior observed during flow reversals. These findings confirm the CFAT's suitability for practical OWC applications, demonstrating robust self-starting and stable performance under irregular conditions.Publication Embargo Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study(Taylor and Francis Ltd., 2026-02-18) Fernando, W. J; Silva, N; Perera, CTimely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.Publication Embargo Evaluating and prioritizing quality culture elements in the tire manufacturing industry: A case-based DEMATEL approach(Taylor and Francis Ltd., 2026-01-16) Silva, N; Bandara, S; Perera, CIn today’s competitive manufacturing environment, developing a robust quality culture that supports continuous improvement and defect prevention is critical for long-term operational excellence and customer satisfaction. This study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate and prioritize the key elements shaping quality culture in a leading tire manufacturing company in Sri Lanka, offering a rare empirical application of DEMATEL in this context. Using a two-phase approach, Phase 1 involved a survey of 127 employees to identify seven critical elements, while Phase 2 analyzed expert evaluations from 16 professionals to establish causal relationships among them. The findings reveal that leadership (R + C = 14.40) and employee empowerment (R + C = 13.31) are the most influential drivers, followed by teamwork (R + C = 12.67), while focus on customer satisfaction, planning for quality, improvements and innovation, and standardized processes approach are primarily dependent elements. The built-in quality maturity framework and relative importance index were applied to assess the current implementation level of each element and identify performance gaps. The gap analysis highlights that leadership and employee empowerment are underperforming relative to expected levels, requiring immediate strategic enhancement. Managers should prioritize leadership and empowerment development to strengthen organizational quality culture and sustain competitiveness.Publication Embargo Performance improvement of a cross-flow air turbine for oscillating water column wave energy converter by nozzle and blade optimization(Elsevier Ltd, 2025-01-15) Baddegamage B.H.B.P.D; Bae, S.J; Jang, S.H; Gunawardane S.D.G.S.P; Lee, Y.O; Kim, K; Yoon, MThe global pursuit of renewable energy solutions has highlighted the potential of wave energy converters (WECs), particularly oscillating water column (OWC) systems, as viable clean energy sources. This study focuses on optimizing a cross-flow air turbine (CFAT) through comprehensive numerical simulations, aimed at enhancing its performance as the power take-off system in OWC applications. The influences of various geometrical parameters, such as including nozzle entry arc angle, nozzle starting angle, and angle of attack, on the performance of the CFAT are investigated. The optimized turbine model achieves a peak efficiency (η) of 0.71 in unidirectional flow at a flow coefficient (Φ) of 0.41, representing a significant improvement over the reference model (η = 0.61 at Φ = 0.29). In addition, the performance of the optimized CFAT is evaluated under regular wave conditions, simulating the bidirectional flow typical of real-world OWC applications. Although the peak efficiency in reciprocating flow slightly decreases, shifting to 0.68 at Φ = 0.53, the turbine maintains a high mean efficiency throughout the operating cycle. The results demonstrate that the optimized CFAT model provides robust performance in both unidirectional and bidirectional flows, making it a promising candidate for enhancing the efficiency of OWC-based WECs.Publication Open Access Forecasting renewable energy for microgrids using machine learning(Springer Nature, 2025-05-03) Sudasinghe, P; Herath, D; Karunarathne, I; Weeratunge, H; Jayasuriya, LMicrogrids, comprised of interconnected loads and distributed energy resources, function as single controllable entities with respect to the main grid. However, the inherent variability of distributed wind and solar generation within microgrids presents operational stability challenges concerning voltage regulation and frequency stability. Accurate forecasting of renewable generation is crucial for mitigating these challenges. This work proposes a one-dimensional Convolutional Neural Network (1-D CNN) based approach to forecast photovoltaic (PV) generation and wind energy, using data from the University of California, San Diego microgrid and San Diego Airport weather records. The proposed method is evaluated against various forecasting methods using key metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared value. Results show that the 1-D CNN model achieves an improvement of up to 229.8 times in MSE and a 24.47 fold improvement in MAE compared to baseline models that use traditional statistical methods in forecasting. This demonstrates the potential of machine learning for enhancing microgrid management, particularly in short-term forecasting of renewable generation. © The Author(s) 2025. Evaluated ML-based renewable energy forecasting models by implementing 1-D CNN and LSTM models using real-world data. Proposed 1-D CNN performs better than LSTM and baseline models, achieving higher accuracy and computational efficiency. Accurate forecasting of PV and wind energy generation enhances grid stability, reduces backup power dependency, and supports sustainable energy integration.Publication Open Access Interpretable SHAP-bounded Bayesian optimization for underwater acoustic metamaterial coating design(Springer Science and Business Media Deutschland GmbH, 2025-09-08) Weeratunge, H; Robe, D. M; Hajizadeh, EWe present an interpretability-informed Bayesian optimization framework for the inverse design of underwater acoustic coatings composed of polyurethane elastomers with embedded metamaterial features. A data-driven model was used to capture the relationship between acoustic performance, specifically, sound absorption and the corresponding geometrical design variables. To interpret these relationships, we applied SHapley Additive exPlanations (SHAP), enabling the identification of key parameters influencing the objective function and providing both global and local insights into their effects. The insights from the SHAP analysis were used to automatically refine the bounds of the design space, guiding the optimization process toward more promising regions. This approach was tested on two polyurethane materials with different hardness levels and yielded improved optimal designs compared to standard Bayesian optimization without increasing the number of simulations. This work underscores the effectiveness of combining interpretability techniques with optimization for the efficient and cost-effective design of underwater acoustic metamaterials under strict computational constraints and can be generalized towards other materials and engineering optimization problemsPublication Open Access Sensitivity Analysis of Parameters Affecting Wetland Water Levels: A Study of Flood Detention Basin, Colombo, Sri Lanka(MDPI, 2023-04-02) Herath, M; Jayathilaka, T; Azamathulla, H.M; Mandala, V; Rathnayake, N; Rathnayake, UWetlands play a vital role in ecosystems. They help in flood accumulation, water purification, groundwater recharge, shoreline stabilization, provision of habitats for flora and fauna, and facilitation of recreation activities. Although wetlands are hot spots of biodiversity, they are one of the most endangered ecosystems on the Earth. This is not only due to anthropogenic activities but also due to changing climate. Many studies can be found in the literature to understand the water levels of wetlands with respect to the climate; however, there is a lack of identification of the major meteorological parameters affecting the water levels, which are much localized. Therefore, this study, for the first time in Sri Lanka, was carried out to understand the most important parameters affecting the water depth of the Colombo flood detention basin. The temporal behavior of water level fluctuations was tested among various combinations of hydro-meteorological parameters with the help of Artificial Neural Networks (ANN). As expected, rainfall was found to be the most impacting parameter; however, apart from that, some interesting combinations of meteorological parameters were found as the second layer of impacting parameters. The rainfall–nighttime relative humidity, rainfall–evaporation, daytime relative humidity–evaporation, and rainfall–nighttime relative humidity–evaporation combinations were highly impactful toward the water level fluctuations. The findings of this study help to sustainably manage the available wetlands in Colombo, Sri Lanka. In addition, the study emphasizes the importance of high-resolution on-site data availability for higher prediction accuracy.