Amirhossein Ghobadi

This project focuses on the experimental characterisation and modelling of CFRP adhesive bonding, with emphasis on the relationship between surface condition, interfacial response, and bonded-joint behaviour. It combines surface analysis of treated CFRP specimens with cohesive zone modelling of relevant mechanical-test geometries, supporting a more reliable interpretation of damage initiation and fracture behaviour. The work contributes to a broader effort to improve process understanding and predictive capability in bonded composite structures.

This industrial innovation project focused on improving the stability, reliability, and quality performance of a CNC machining line by addressing recurrent tool failures and process variation that were contributing to downtime, tolerance risk, and scrap. The work combined in-process sensing, a physics-informed digital twin, and machine learning to detect early indicators of tool degradation and support more timely, evidence-based intervention. By linking live machining data with calibrated engineering models and historical production records, it enabled a shift from reactive troubleshooting towards more predictive and explainable process control. The approach was validated through iterative comparison of predicted and actual behaviour and contributed to reduced tool-related downtime, lower scrap, and a more scalable framework for predictive maintenance and quality protection in high-precision manufacturing.

This research examined how key laser-processing parameters influence the microstructural integrity and mechanical performance of Ti-6Al-4V components produced by laser powder bed fusion. Focusing on the interaction between laser power, scanning speed, residual stress, and geometric distortion, the study combined process simulation in Hexagon Simufact Additive with ML Analysis to evaluate and optimise competing performance criteria. The work identified an effective parameter window, achieving reduced residual stress and part distortion while demonstrating the critical role of energy density in governing build quality and structural reliability. Overall, the project contributed to a more systematic understanding of parameter–property relationships in metal additive manufacturing and supported more informed process optimisation for high-performance titanium components.

This industry-linked project focused on the development and validation of a sustainable fibre-reinforced composite prototype for a novel engineering application with tightly balanced mechanical, thermal, electrical-insulation, cost, and sustainability requirements. Working in collaboration with a composite manufacturing company, the project translated functional design needs into material-selection and lay-up strategies through structured trade-off analysis across fibre systems, resin options, manufacturability, and lower-waste processing routes, including consideration of bio-derived and recyclable alternatives. The work combined design evaluation with rapid prototyping, resin infusion, compression moulding, and industrial-quality inspection, supported by iterative interpretation of mechanical and thermal test data to refine the concept. Overall, the project strengthened the link between materials knowledge, manufacturing practicality, and validation-led decision-making in an applied composite R&D setting.

This project focused on the numerical and multi-scale modelling of metal matrix composites to better understand their stress–strain behaviour and energy-absorption performance under mechanical loading. Using representative volume element-based analysis in Digimat-FE and Abaqus, the work examined the influence of lightweight aggregates such as LECA, pumice, and perlite on composite response, and linked micromechanical behaviour to the overall structural performance of the material. The modelling was complemented by quasi-static compression testing to generate experimental stress–strain data for validation, allowing direct comparison between simulation and physical behaviour. The close agreement between numerical and experimental results, with only limited deviation near the end of the stress plateau, confirmed the reliability of the modelling approach and provided a robust basis for future optimisation of lightweight, energy-absorbing metal matrix composite systems.

This international collaborative project focused on the manufacture and mechanical evaluation of aluminium-based syntactic foams incorporating lightweight aggregates, with the aim of improving matrix infiltration while addressing practical challenges such as poor wettability, particle damage, and high processing cost. Conducted in collaboration between Brunel University London and the Technical University of Berlin, the work involved the development of an innovative casting route under vacuum with dynamic argon flow to enhance molten-metal penetration and process stability. Experimental validation included both non-destructive and destructive testing to assess the quality, integrity, and mechanical behaviour of the resulting structures. Overall, the project contributed to a more reliable and process-aware approach to the development of lightweight metal matrix syntactic foams for structurally efficient engineering applications.

This industry-linked research project investigated the nonlinear dynamic behaviour of Shape Memory Alloy components operating within the pseudo-elastic regime, with direct application to active morphing structures in aerospace flight-control surfaces and self-recovering automotive systems. Conducted in collaboration between TADAF Co. and Babol Noshirvani University of Technology, the work established a physics-based modelling framework that simultaneously captured coupled material nonlinearities — including stress-induced phase transformation and hysteretic dissipation — alongside geometric nonlinearity, to faithfully represent conditions encountered in real operating environments. Governing equations were derived through Euler–Bernoulli and Timoshenko beam theories integrated with the three-dimensional Souza constitutive model, discretised via Galerkin's method, and solved for both free and forced vibration responses using Newmark time-integration. The analysis quantified hysteresis-driven effective damping under free vibration, characterised the influence of pre-strain and temperature history on residual deformation and equilibrium offset, and employed bifurcation and jump-phenomenon analysis of the forced response to identify critical excitation thresholds — collectively providing a predictable, physics-grounded performance envelope to support the reliable industrial integration of SMA-based adaptive components.