Medad C.C. Monu
Medad is an I-Form PhD researcher based in DCU, and working in the hybrid area of materials processing development and materials processing feedback and control. He completed his BEng and MEng degrees in Materials and Metallurgical Engineering at FUTO and FUTA, Nigeria, respectively. He specialised in Production and Metallurgy, with a thesis focused on correlating resultant fusion weld metallurgy and observed electrical conductivity of plain carbon steels used in DC cathodic protected weldments/structures. Thereafter, he completed a MSc in Mechanical Engineering at the University of Portsmouth, UK, where he specialised in Metal Additive Manufacturing (AM). The focus of this research was the process development for selective laser melting (SLM) of 17-4PH stainless steels.
Both SLM and conventional fusion welding techniques work on the same principle of material heating, full melting, and solidification. However, unlike the age-old fusion welding, SLM is relatively new, faster; but riddled with process and structural challenges.
Research Interests (Lay Summary)
Medad started his PhD with I-Form in March 2020, and his research is aimed at developing a digital twin system for metal AM, allowing for right-first-time production.
Fully dense metallic parts with high level of intricacies can be 3D printed to exceptionally high dimensional tolerances and good surface finishes. This process can now be completed relatively quickly and cost-effectively with respect to reduced amount of raw material and avoidance of lengthy post-manufacturing processes. Unfortunately, the quality assessment (QA) time for AM components is lengthy, often requiring several trials (prints) and resulting in many errors (defective AM parts). This inhibits a high-level of confidence for biomedical and aerospace applications. Developing a digital twin by the seamless integration of physics-based predictive modelling, in-situ sensing, and data analytics (machine learning), will allow for corrective action to be taken during fabrication, rather than allow an ill-fated process continue and result in a defective AM part.
The norm of fabricating structurally sound AM components with good mechanical properties is based solely on time-consuming, multiple and expensive experiments. However, my research, focused on synthesizing the available knowledge base of AM and welding to develop a digital twin (replica), can significantly minimize these expensive trial and error experiments.
Such a digital twin will integrate predictive FEA modelling and near real-time monitoring and modelling, into a tractable metal AM reference ontology (numerical framework). Specifically, this involves comparative data analytics from in-situ IR temperature measurements and the instantaneous spatiotemporal distribution of temperature of the part, validated against the produced part characteristics (e.g. dimensional, micro-CT, entrained stress, hardness) and the predicted FEA analysis. This research will provide a strong stepping stone to attaining fully functional digital twins for metal additive manufacturing.