Welcome to the Solidification, Imaging & Mathematical Modelling of Materials & Processes (SIM3P) research laboratory of Dr. André Phillion in the Department of Materials Science & Engineering at McMaster University in Hamilton, Ontario, Canada.

Solidification Science & Application to Manufacturing

Our group focuses on mathematical modelling and 3D materials science, with emphasis on casting, welding, and additive manufacturing technologies. A major priority is continuous casting of steel, where we aim to understand and control solidification phenomena that govern product quality and defect formation. Much of this work is carried out in partnership with the McMaster Steel Research Centre (SRC), which unites leading steel producers to address both fundamental and applied challenges in steelmaking.

The study of solidification across multiple length scales is central to our research. We link heat transfer and fluid flow at the macro-scale with microstructure and defects at the micro-scale. Our approach combines 3D characterization of solidification morphology and defects, in-situ studies using high-temperature confocal microscopy, constitutive property assessment, and predictive modelling of industrial processes, structures, and defects. This integrated approach supports improved process control and defect reduction in continuous casting and related manufacturing technologies.

3D Imaging & Modelling of Materials & Processes

Complementing research work on steel, our group applies 3D imaging and image-based modelling to a wider set of materials challenges, always with the goal of linking structure and properties across length scales. Current projects include investigating the strength and structure of cellulose-based products, developing multimodal correlative tomography to study Li-ion batteries, creating 3D workflows for battery recycling, and applying in-situ imaging to Ni-superalloy repair, where we observe crack filling and solidification during advanced joining processes. In each case, novel imaging is paired with modelling to reveal mechanistic insight that cannot be captured by traditional approaches.