My research interests include design and manufacturing activities, systems thinking and systems engineering. More specifically, the research focuses on the early integration of simulation and decision support tools in the early design phases. Our team is currently developing predictive and control models for additive manufacturing and various manufacturing technologies. The models are derived from my ongoing research into dimensional analysis theory, which has led to the creation of the Dimensional Analysis Conceptual Model (DACM) framework. Three central ideas emerge from this research: first, a way to automatically detect weaknesses in systems. Second, a precursor model for a new form of Artificial Neural Network (ANN) belonging to the family of Knowledge-Based ANN. Third, a precursor model for Bayesian Networks is derived directly from the DACM architecture. All these developments have found applications in manufacturing, engineering design, optimisation and cyber security.DACM is a knowledge-encoding approach that is also used in Multi-Objective Optimization (MDO) problems. The DACM-based approach reduces the size of MDO problems, minimises the size of training data sets for ANN, and designs the topology of Bayesian networks.The overall objective is to develop a new category of computational support tools for early design, modelling, simulation and optimisation tasks. This should include the ability to simulate as early as possible with qualitative and limited information. The methods we use to investigate this research area coherently integrate systems thinking and systems engineering, graph and network theory, ANN, semantics and natural language processing, matrix factorization methods, dimensional analysis theory and statistical approaches.Take a look at some of our developments:https://link.springer.com/article/10.1007/s00170-020-06158-0https://www.astm.org/DIGITAL_LIBRARY/JOURNALS/SSMS/PAGES/SSMS20190026.htmhttps://www.cambridge.org/core/journals/ai-edam/article/function-modeling-combined-with-physicsbased-reasoning-for-assessing-design-options-and-supporting-innovative-ideation/40C241741F7CD7F018C6113423CE1295https://www.sciencedirect.com/science/article/abs/pii/S0951832018307634https://www.computer.org/csdl/mags/cs/2016/04/mcs2016040042-abs.htmlhttps://www.dynavio.com/