Abstract
Mechanical and energy systems such as fused deposition modelling (FDM) 3D printing,
combined-cycle plants and solar-hybrids exhibit nonlinear, interaction-rich behaviour
that challenges both first-principles models and ad-hoc machine learning (ML). Results
for artificial neural network (ANN) or adaptive neuro-fuzzy inference system (ANFIS)
and metaheuristic hybrids with genetic algorithm (GA), particle swarm optimisation
(PSO), antlion optimisation (ALO), and grey wolf optimisation (GWO) are promising
yet fragmented, with inconsistent data handling, limited uncertainty reporting and little
guidance on compute–accuracy trade-offs.
This thesis proposes a general, reproducible framework for data-driven prediction of
nonlinear mechanical systems. It enforces leakage-safe data practice, compares
ANN/ANFIS and hybrids under a common rubric, integrates uncertainty and
physics-consistency diagnostics, and reports cost–accuracy frontiers for deployment
decisions.
The framework is comprised of four pillars: 1) Protocol: audited datasets,
group/time-aware splits, candidate set (ANN, ANFIS, + GA/PSO/ALO/GWO), fixed
seeds and hyperparameter logs; 2) Evaluation: unified regression value or coefficient
of correlation (R or R²), mean square error (MSE) or root mean square error (RMSE)
with 95% confidence intervals (CIs) residuals and calibration; 3) Interpretability and
Physics: global/local sensitivity, ANFIS rule/surface inspection, monotonicity checks
(e.g., net power vs ambient temperature and condenser vacuum pressure) and 4) Cost–
Accuracy: wall-time, iterations, model size/latency and Pareto charts to justify (or
reject) hybridisation.
The playbook is exercised on four systems, as follows: FDM 3D printing, combined
cycle power plant (CCPP), Parabolic-dish heater and Integrated solar combined cycle
power plant (ISCCPP). Each follows the same template: leakage-safe split; strong
baselines (ANN with Levenberg-Marquardt [LM]/Bayesian regularisation [BR]/Scaled
conjugate gradient [SCG], classical ANFIS); then hybrids with transparent settings;
standardised reports (train/validation/test/overall), residuals, sensitivity, and compute
profiles...