The Marine Additive Manufacturing Centre of Excellence consists of a dedicated research and development team within the Faculty of Engineering at the University of New Brunswick in Fredericton.
Dr. Mohsen Mohammadi is director of research and development for the Marine Additive Manufacturing Centre of Excellence. He is also an assistant professor of mechanical engineering, and director of the Cognitive Performance Optimization Lab at the University of New Brunswick.
Mohammadi develops advanced materials using additive manufacturing techniques. His current research focuses on enhancing the mechanical, corrosion, impact, and fatigue properties of additively manufactured metals (e.g., aluminum, titanium, steels). It also involves 3D printed long fibre composites, metal matrix coatings, and ultra-light high strength metamaterials.
Prior to beginning his academic career at UNB in 2015, Mohammadi was a research associate and postdoctoral fellow at the University of Waterloo in Ontario, NSERC Visiting Research Fellow at Canmet MATERIALS (Natural Resources Canada) and PhD candidate at the University of Western Ontario.
Mohammadi is a leader of several significant projects on metal additive manufacturing in marine, defence, and aerospace sectors.
Corrax stainless steel powder is mainly developed to target the tool and die industry as well as the marine sector.
This project investigates the effects of powder size, morphology and powder compositions on mechanical properties of final additively manufactured parts. We also develop an optimum sieving technique for remaining powders after the building is completed, for recycling as well as to lower the cost of procedures.
This builds the foundation for developing new variations of stainless-steel powders for better mechanical and corrosion properties.
This project attempts to address the missing links to generate the knowledge and technology to develop 3D-printed Corrax stainless steel parts for the marine industry such as impellers.
This project investigates the effects of building parameters to produce as-built parts with optimum corrosion properties, including:
This project also looks at the effects of different levels of surface roughness, like mirror-like, on the corrosion properties of additively manufactures parts.
We study the effects of different corrosive environment on the corrosion behaviour of 3-printed parts.
For the nuclear energy application of corrosion resistant materials, this project will study the effects of ionized water on the corrosion behaviour of 3D-printed parts for radiation-assisted corrosion.
This project investigates the effects of building parameters to produce as-built parts with optimum fatigue endurance limit, such as:
We also study the effects of different corrosive environments on the corrosion fatigue behaviour of 3D-printed parts.
This research project investigates the safety and security of additively manufactured parts used under impact, blast and shock loadings. It also leads to designing new materials with better impact properties than conventional metals.
We study the strain rate sensitivity of additively manufactured parts under quasi-static to medium strain rates and the strain rate sensitivity of additively manufactured parts under medium to high strain rate regimes. We will investigate the texture and microstructure evolution of the additively manufactured parts under low to high strain regimes.
The main goal of this project is to study the mechanical behaviour and microstructural properties of additively manufactured steels at warm and hot temperatures by:
The goal of this project is to investigate the effects of different pre-heating scenarios for optimum interface properties in a final and hybrid additively manufactured part.
We also investigate the effects of different heat treatment procedures for highest hardness properties in a final hybrid, additively manufactured part.
Finally, we study the interface in a final hybrid, additively manufactured part under different loading conditions such as impact or corrosion fatigue.
Radiography is used to look at the behaviour of powder and melt-pool during the printing process. This research will provide insight into the high heating and cooling rates during powder beds printing methods as well as the interaction between laser and powder and its impact on the material's microstructure and properties after printing.
We work to develop a phenomenological simulation framework using commercial finite element software to model the mechanical behaviour of additively manufactured parts during different loading conditions.
We are also developing a micromechanics model to predict the microstructure of additively manufactured parts under different loading conditions such as impact, compression and tension for a variety of materials.
We also work on a multiscale bridge to moel microstructure properties of additively manufactured parts with their micro response under different loading conditions such as impact and cyclic loadings.
This project develops machine learning algorithms using cognitive-based models to design micro-lattice structures for lightweight and optimum energy absorption. We develop a computer science/mechanical engineering platform for topology and tomography optimization for different marine and aerospace parts to provide optimum performance during service.
This will lead to a new paradigm for mechanical design based on machine learning techniques. This project improves lightweight structures with optimum performance for the aerospace industry as well as reducing build times and material used to manufacture greener products.
In additive manufacturing, parts are manufactured through layer-by-layer deposition of materials using different 3D-printing processes, which allows translating design concepts into products. This allows the exploration of printing sensors with communication abilities.
3D printing is one of the key basic enabling technologies for Industry 4.0 the fourth industrial revolution. This project explores the integration of 3D printers, 3D scanners and CNC machines in production logistics.
It aims at redesigning the supply chain based on end-to-end digital integration of 3D-printing technology, achieving full automation in the production line.
