Bio

My research interests are including design and manufacturing activities, system thinking, and system engineering. More specifically the research focus is given to the early integration of simulations and decision support tools during the initial design phases. With my team, we are currently developing predictive and control models for Additive Manufacturing and different manufacturing technologies. The models are derived from my continuous research effort related to the Dimensional Analysis theory which has supported the creation of the Dimensional Analysis Conceptual Model (DACM) framework.

Three central ideas emerge from this research, first a way to detect automatically systems' weaknesses. Second, a precursor model for a new form of Artificial Neural Networks (ANN) belonging to the family of Knowledge-based ANN. Third, a precursor model for Bayesian Networks is directly derived from the DACM architecture. All those developments have found applications in Manufacturing, engineering design, optimization, and cyber security.

DACM is an approach to encode knowledge also used in Multi Objectives Optimization (MDO) problems. The DACM-based approach reduces the size of MDO problems, minimizes the size of training data sets for ANN, and designs the topology of Bayesian Networks.

The overall objective is the development of a new category of computer support tools for the early design phases, modeling, simulation, and optimization tasks. This should incorporate the capability to simulate as soon as possible with qualitative and limited information. The methods that we are using to investigate this research domain are integrating coherently system thinking and system engineering, graph and network theory, ANN, semantics and natural language processing, matrix factorization methods, dimensional analysis theory, and statistical approaches.