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Journal of Hybrid Modelling and Intelligent Engineering Systems
JGELCD
Journal of Hybrid Modelling and Intelligent Engineering Systems (JHMIES)
JII
ISSN (print): 3134-7185
ISSN (online): 3134-7193
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2026: Vol. 1
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Journal of Hybrid Modelling and Intelligent Engineering Systems (JHMIES) is a peer-reviewed, open-access journal that publishes research on hybrid modelling approaches within intelligent engineering systems. The journal focuses on how heterogeneous modelling paradigms—such as data-driven learning, physics-based modelling, optimisation, control, and system simulation—are combined and structured to address complexity in engineering environments. JHMIES welcomes contributions that examine the formulation, integration, and evaluation of hybrid modelling frameworks embedded in engineering systems. Emphasis is placed on methodological coherence, system-level reasoning, and the relationship between modelling structure and measurable engineering performance. Studies may involve analytical development, computational modelling, experimental validation, or structured system analysis. The journal considers interdisciplinary work spanning computational intelligence, systems engineering, applied modelling, and engineering optimisation, provided that the modelling integration logic and engineering relevance are clearly articulated. Application contexts may include manufacturing systems, energy and environmental systems, robotics and automation, transportation and infrastructure systems, aerospace systems, and other complex engineered environments where modelling integration plays a central role. JHMIES is published quarterly by Acadlore, with issues released in March, June, September, and December.

  • Professional Editorial Standards - All submissions are evaluated through a standard peer-review process involving independent reviewers and editorial assessment before acceptance.

  • Efficient Publication - The journal follows a defined review, revision, and production workflow to support regular and predictable publication of accepted manuscripts.

  • Open Access - JHMIES is an open-access journal. All published articles are made available online without subscription or access fees.

Editor(s)-in-chief（2）
oscar castillo
Division of Graduate Studies and Research, Tijuana Institute of Technology, Mexico
ocastillo@tectijuana.mx | website
Research interests: Type-2 Fuzzy Logic; Fuzzy Control Systems; Neuro-Fuzzy and Genetic-Fuzzy Hybrid Approaches; Computational Intelligence; Hybrid Intelligent Systems; Soft Computing; Fractal Theory in Intelligent Manufacturing; Granular Computing; Extensions of Type-1 Fuzzy Systems; Intelligent Modelling and Optimization
babak safaei
Department of Mechanical Engineering, Eastern Mediterranean University, Turkey
babak.safaei@emu.edu.tr | website
Research interests: Computational Mechanics; Micro and Nano Mechanics; Composite Materials; Addative Manufacturing; Energy Harvesting; Energy Storage and Lithium-Ion Battery; Biomechanics and Drug Delivery; Mechanical Vibration; Buckling and Bending

Aims & Scope

Aims

Journal of Hybrid Modelling and Intelligent Engineering Systems (JHMIES) is an international, peer-reviewed, open-access journal devoted to research on hybrid modelling approaches within intelligent engineering systems.

Contemporary engineering systems increasingly rely on the coordinated use of heterogeneous modelling paradigms. Data-driven learning models, physics-based representations, optimisation routines, control structures, simulation frameworks, and decision mechanisms are often combined to address structural complexity, operational variability, and performance constraints. JHMIES provides a forum for research that examines how such hybrid modelling configurations are formulated, integrated, and evaluated when embedded in engineering systems.

The journal does not prioritise isolated algorithmic refinement or purely theoretical modelling detached from an engineering context. Instead, it addresses the structural and operational implications of combining distinct modelling paradigms within engineered environments. Questions concerning model coordination, integration logic, system architecture, robustness, scalability, and performance under practical constraints are central to its scope.

Particular attention is given to studies that demonstrate how modelling structure influences measurable engineering outcomes, including system stability, reliability, resource efficiency, and operational behaviour under realistic conditions. Contributions are expected to clearly articulate integration strategies, modelling assumptions, and validation procedures, supported by analytical, computational, or experimental evidence.

JHMIES welcomes interdisciplinary work situated at the intersection of computational modelling, systems engineering, control engineering, industrial engineering, and applied optimisation, provided that the central contribution concerns the structured integration of modelling components within engineering systems.

The journal is published quarterly and follows a standard peer-review procedure to ensure methodological rigour and technical soundness.

Key features of JHMIES include:

  • The journal centres on hybrid modelling within engineering systems rather than isolated algorithmic development.

  • Emphasis is placed on structural integration, system architecture, and the coordination of heterogeneous modelling components.

  • Contributions must demonstrate clear engineering relevance, linking modelling design to measurable system performance.

  • Methodological transparency is required, including explicit modelling assumptions, integration logic, and validation procedures.

  • Comparative or cross-domain studies are encouraged where they clarify structural differences between alternative modelling configurations.

  • Application-oriented studies are considered only when they contribute to modelling methodology or integration structure; descriptive case reports without structural contribution fall outside the journal’s focus.

Scope

JHMIES welcomes original research articles, methodological analyses, theoretical studies, systematic reviews, and well-documented empirical or computational investigations in areas including, but not limited to, the following:

Hybrid Modelling Methodologies

Research addressing the structured combination of heterogeneous modelling paradigms within unified engineering frameworks, including:

  • Integration of data-driven and physics-based models

  • Coupling of learning, optimisation, and control mechanisms

  • Multi-model and ensemble modelling strategies

  • Model fusion and hierarchical modelling structures

  • Hybrid predictive–prescriptive modelling approaches

  • Coordination of deterministic and stochastic representations

Submissions in this area should emphasise modelling structure and integration coherence rather than isolated algorithmic enhancement.

System Architecture and Engineering Integration

Research examining how hybrid models are embedded within broader engineering systems, including:

  • System-level modelling and coordination strategies

  • Hybrid architectures in cyber-physical environments

  • Integration within simulation and digital twin frameworks

  • Embedded and real-time modelling systems

  • Component interoperability and modular system design

  • Interaction between modelling components and physical processes

Studies should demonstrate how modelling integration influences system behaviour and operational performance.

Performance, Robustness, and Operational Behaviour

Research concerned with how hybrid modelling systems perform under engineering constraints, including:

  • Operation under uncertainty, noise, and incomplete data

  • Stability and robustness analysis

  • Sensitivity studies and structured performance evaluation

  • Scalability and computational efficiency considerations

  • Reliability analysis in integrated modelling systems

  • Behaviour under dynamic or time-varying conditions

Emphasis is placed on systematic evaluation rather than anecdotal reporting.

Control, Optimisation, and Decision Structures

Research involving hybrid modelling in control and optimisation contexts, including:

  • Hybrid model-based control strategies

  • Learning-assisted optimisation frameworks

  • Adaptive and multi-objective optimisation

  • Predictive modelling integrated with control structures

  • Real-time decision-support modelling systems

Submissions should explain how modelling integration improves system structure or operational performance.

Validation, Comparative Studies, and Reproducibility

Research addressing the evaluation and validation of hybrid modelling systems, including:

  • Experimental validation of integrated modelling frameworks

  • Cross-domain comparative studies

  • Integration testing in simulated or operational environments

  • Reproducibility and transparency in hybrid modelling research

  • Lifecycle performance assessment

Studies should provide clearly defined evaluation criteria and methodological transparency.

Engineering Application Contexts

Hybrid modelling approaches applied within engineering domains such as:

  • Manufacturing and industrial systems

  • Energy production and power systems

  • Robotics and automation

  • Transportation and infrastructure systems

  • Aerospace and mechanical engineering

  • Environmental and sustainability engineering

  • Industrial cyber-physical systems

Application-based submissions must retain a clear modelling or integration-oriented contribution rather than presenting domain-specific case descriptions alone.

Articles
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Abstract

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High-entropy alloys (HEAs) exhibit high mechanical strength, phase stability, and damage tolerance, making them promising materials for advanced structural applications. However, reliable prediction of their mechanical response and microstructural failure remains challenging because alloy composition, phase constitution, grain characteristics, thermodynamic descriptors, and deformation behaviour are strongly coupled. This study investigates a physics-guided hybrid modelling framework that integrates physically meaningful alloy descriptors with Transformer-based representation learning for failure and mechanical response prediction in HEAs. Mixing entropy, atomic size mismatch, valence electron concentration, phase stability parameters, and microstructural and mechanical descriptors were incorporated into the data-driven modelling architecture to establish physically informed relationships between material characteristics and failure behaviour. The developed framework was used to predict ultimate tensile strength (UTS), failure strain, stress–strain response, and microstructural failure probability. The results showed close agreement between the predicted and measured UTS and failure strain and demonstrated higher $R^2$ values than random forest, extreme gradient boosting (XGBoost), convolutional neural network (CNN), long short-term memory (LSTM), and standard Transformer models. The predicted stress–strain curves also reproduced the principal deformation behaviour observed in the experimental data. Failure-probability mapping and attention analysis identified the material and microstructural features associated with failure, while the ablation study showed that incorporating physics-guided information improved predictive accuracy, reliability, and interpretability. These findings demonstrate that the structured integration of physics-based descriptors and Transformer learning provides an effective hybrid modelling strategy for relating material constitution to mechanical response and failure. The proposed framework provides a physically informed and interpretable approach to failure prediction and data-assisted design of HEAs and offers a transferable modelling strategy for intelligent engineering analysis of complex material systems.

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Reliable monitoring of chip formation and tool degradation is essential for machining quality, process stability, and condition-based maintenance in intelligent manufacturing systems. However, individual sensing modalities and isolated learning models provide incomplete representations of the heterogeneous and time-dependent interactions that characterize computer numerical control (CNC) milling. This study investigates a hybrid multimodal modelling framework that integrates heterogeneous sensing, complementary feature-learning mechanisms, and joint classification–regression tasks for chip morphology and progressive flank-wear monitoring. Vibration, acoustic emission (AE), cutting force, spindle current, and temperature signals were synchronized and fused within a unified modelling architecture. A convolutional neural network (CNN) was used to extract local signal characteristics, a bidirectional long short-term memory (BiLSTM) network modelled temporal dependencies, and Transformer-based self-attention captured long-range interactions within the fused representation. The resulting features were used jointly to classify continuous, segmented, discontinuous, and serrated chip morphologies and to estimate progressive flank wear. The framework was evaluated under variations in spindle speed, feed rate, and wet and dry machining conditions, together with sensor-fusion comparisons and architectural ablation experiments. The full multimodal configuration achieved approximately 97.2% chip-classification accuracy, while the complete hybrid architecture reached approximately 98% in the ablation analysis and maintained prediction accuracy above 94% across the investigated cutting conditions. Tool-wear estimates also showed close agreement with the experimentally measured values. These results indicate that coordinated integration of heterogeneous sensing with complementary local, sequential, and attention-based modelling mechanisms improves predictive stability and robustness under variable machining conditions. The proposed framework provides a structured hybrid-modelling approach for intelligent machining-process monitoring and offers a basis for predictive maintenance and adaptive CNC manufacturing systems.

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Accurate buckling prediction of functionally graded porous (FGP) nanobeams requires the simultaneous consideration of material gradation, porosity distribution, transverse shear deformation, and small-scale effects. Conventional analytical models provide a sound mechanical basis; however, they become less convenient when repeated evaluations and multi-objective design decisions are required. This study develops a hybrid analytical-machine-learning framework for predicting and interpreting the buckling behavior of FGP nanobeams. An analytical model was formulated using exponential shear deformation theory and Eringen’s nonlocal elasticity theory. The governing equations were derived from the minimum total potential energy principle and solved by Navier’s method for simply supported boundary conditions. The formulation was verified against published benchmark results and was subsequently used to generate 540 configurations covering different slenderness ratios, nonlocal parameters, porosity coefficients, and porosity distributions. A residual-learning surrogate was then constructed by combining a regularized Ridge baseline with nonlinear machine learning (ML) models. Nested cross-validation (CV) showed that the hybrid support vector regression model with a radial basis function produced the lowest prediction errors, with a root mean square error (RMSE) of 0.00729, a mean absolute error (MAE) of 0.00309, and an $R^2$ of 0.999900. The analytical and surrogate results showed that the buckling load decreases with increasing slenderness ratio, nonlocal parameter, and porosity coefficient, while the functionally graded X-pattern (FGX) distribution retains the highest buckling resistance. The conformal prediction intervals achieved 97.04% empirical coverage. SHapley Additive exPlanations (SHAP) analysis identified the slenderness ratio as the dominant input, and Nondominated Sorting Genetic Algorithm II (NSGA-II) yielded a Pareto-based compromise between porosity and buckling resistance. The proposed framework demonstrates how analytical mechanics, residual learning, uncertainty quantification, model interpretation, and multi-objective optimization can be coordinated within a unified engineering design procedure.

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In this study, an adaptive linear neuron (ADALINE) network is employed to adjust the parameters of a conventional proportional-integral (PI) controller. The proposed adaptive neuron controller (NEURON-PI) has been used here instead of the conventional power system stabilizer (PSS) to control the rotor speed deviation of the single-machine infinite-bus (SMIB) system. The rotor speed deviation is used as a control error fed to the neural network in order to adjust the PI controller gains. This strategy achieves an optimal response of the power system against a wide range of operational conditions. The linearized Heffron-Phillips model of the SMIB system with the proposed NEURON-PI controller is simulated using MATLAB R2025a/Simulink software. The simulation results have shown that the power system using the proposed control technique becomes more reliable for a wide range of operating conditions as the speed deviation and power angle deviation become less oscillatory compared to the conventional PSS controller tuned by the equilibrium optimizer algorithm (EOA) reported in a previous study.

Abstract

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The intrinsic variability associated with waste-derived bitumen modifiers poses persistent limitations for conventional deterministic pavement design approaches. This study establishes a hybrid modelling architecture that integrates physics-informed boundary constraints with stochastic simulation components to predict the rheological performance of asphalt binders modified with waste frying oil. Literature-derived parameter ranges are embedded within a Gaussian copula-based dependency structure and expanded through large-scale Monte Carlo simulation to generate a statistically convergent dataset comprising 1,000,000 realizations. The integrated modelling structure preserves the nonlinear relationships among key rheological indicators, including penetration, softening point, and modifier dosage, while maintaining physical consistency. The predictive capability of the proposed architecture is validated using standard statistical metrics, achieving coefficients of determination exceeding 0.98 for penetration and 0.93 for softening point. A mode-based master curve is further constructed to provide a stable and computationally efficient representation of binder behaviour across varying modifier contents. The results demonstrate that the proposed hybrid modelling structure offers a reliable alternative to extensive laboratory testing, reducing experimental effort while retaining predictive fidelity. From a system perspective, the framework provides a structured basis for incorporating material variability into pavement performance evaluation and can be interpreted as a simplified digital twin component for asphalt material systems. The study thereby contributes to the integration of stochastic modelling within engineering-oriented predictive structures for sustainable pavement design.
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