This project focuses on advancing the development of the Wyrm phase-field module, a versatile computational framework designed to model complex material behaviors at the nano/micro-scale. Phase-field models are powerful tools for capturing microstructural evolution, including interfacial energetics and kinetically limited transport phenomena, but they require robust mathematical frameworks and efficient numerical computation.
The goal of this internship is to enhance the capabilities of the Wyrm code to improve its scalability, usability, and accuracy, and to assist in its deployment as an open-source tool online. The intern will work on key objectives including: (1) integrating phase-field modules for microstructural evolution; (2) incorporating self-consistent thermodynamic models using surrogate data for accurate predictions of phase behavior; and (3) optimizing code for computational efficiency through physics-informed solvers.
Finally, the project will culminate in application testing, where the enhanced Wyrm code will be applied to real-world material engineering problems such as polygranular microstructure evolution, dendritic growth, and solute trapping. This project immerses the student in widely used computational techniques with broad applications across the nuclear, manufacturing, and clean energy sectors.
Research area, student roles & skills
Research area: My research focuses on computational materials science and multiscale modelling to support clean energy technologies, including nuclear power, batteries, and hydrogen storage. Specifically, I develop holistic, multiphysics computer models to predict complex material behaviors under extreme conditions, such as corrosive or radiation environments. By integrating equilibrium thermodynamics with phase-field techniques, we bridge the gap between atomistic phenomena and macroscopic material performance. This work provides critical predictive open-source tools to accelerate the design and qualification cycle of advanced materials for a carbon-neutral economy.
Student roles: The student will play a central role in both the development and application testing of the Wyrm phase-field module. Initially, the student will receive comprehensive training in Python programming, phase-field modelling principles, and the Firedrake Finite Element method. Under the guidance of the supervisor and senior graduate students, the intern will progressively tackle structured milestones.
Their primary responsibility will be to develop demonstrative models focusing on specific Wyrm applications, such as polygranular microstructure evolution, dendritic growth, or solute trapping. To achieve this, the student will write and optimize Python code, integrate thermodynamic data, and utilize physics-informed solvers to ensure computational efficiency.
Beyond coding, the student will be responsible for testing the developed modules against theoretical benchmarks and real-world material problems. They will meticulously document their code and publish their finalized models as open-source samples online to support the broader research community. The student will also actively participate in weekly group meetings, presenting their progress and engaging in peer discussion to refine their communication and presentation skills tailored to scientific and technical audiences.
Skills required: The ideal candidate should have an academic background in Engineering Physics, Materials Science, Mechanical Engineering, or a related computational field. Proficiency in Python programming is required, as the project heavily relies on Python-based scientific libraries. Foundational knowledge of materials science, thermodynamics, and partial differential equations is highly desirable. Experience with or interest in Finite Element Methods (specifically Firedrake) and high-performance computing will be considered a strong asset. The student should possess strong creative problem-solving skills and the ability to work collaboratively in a team-oriented research environment.
2. Analytical Methods for 3D Surface Deformation Detection
In automotive manufacturing, quality assurance is critical to ensure that vehicles meet high-quality standards. Globalization and the increasing speed of production lines have driven the need for automated, accurate, robust, and fast inspection systems to detect any surface shape deformation that may appear over the exterior body of a vehicle. Even small deformations can lead to costly rework, paint defects, or downstream manufacturing issues, and must be identified as early as possible on assembly lines to reduce costs. Therefore, we are working at developing real-time 3D vision-based inspection systems capable of automatically detecting deformations on metallic body panels, which may significantly improve the reliability of quality control. For that matter, various algorithms have been designed to process accurate shape measurements generated by various types of 3D point, line, or full area scanners.
The general scope of the project is to implement a class of analytical methods that examine the relative deviation of surface normals orientation to detect any abnormal changes in the local curvature. Given that automotive body panels are designed with an emphasis on aesthetic appearance, the surfaces to inspect are naturally curved and exhibit several functional features to support their assembly on a vehicle, making defects detection challenging. The objective is to develop software that implements one or more conceptual point-cloud analysis methods and to study their performance for automatically detecting local and shallow surface deformation without comparing to a reference CAD model. A quantitative analysis will be conducted from the examination of 3D scan datasets representing various types of surface defects and at different scales. Recommendations regarding sensors and algorithms selection will be formulated as the conclusion of the experimental investigation.
Research area, student roles & skills
Research area: The Sensing and Machine Vision for Automation and Robotic Intelligence (SMART) research laboratory at the University of Ottawa is pursuing research to develop innovative solutions for quality assurance in the automotive manufacturing sector. With the objective to increase automation for surface shape inspection over automotive body parts, our group has developed methods to analyze the distribution of 3D measurements over smoothly curved metallic panels. This project will consist of programming different approaches for the detection of small deformations from 3D measurements and comparing their performance to formulate recommendations for the selection of adequate technologies.
Student roles: Over the entire internship, the student will work in close collaboration with Masters and Doctoral students involved in related projects in robotics and machine vision. The specific activities of the student will include: • Arrival in Ottawa, Canada, and integration into the research environment (1 week); • Familiarization with conceptual analytical methods for surface shape analysis, with surface scanning technologies, and with 3D point cloud datasets (2 weeks); • Programming of analytical methods to convert 3D point cloud measurements into normal-based surface shape representations, and extracting relative local variations to map with categories of surface deformation or undamaged areas (5 weeks); • Test, validation and refinement of the software implementation with actual 3D scans over different classes of deformation to quantify performance and robustness in detection capability (3 weeks); • Preparation of recommendations for technology selection, documentation and presentation of results at the end of the internship (1 week); • Regular exchanges and discussions about design strategies and system’s performance with graduate students and supervisor (continuous over 12 weeks).
Through these activities the research intern will develop strong technical design and programming skills for automated vision-based quality assurance processes in an industrial manufacturing context. The intern will have the opportunity to become familiar with high-end sensors while acquiring new knowledge, developing autonomy, professional experience, and being immersed in a bilingual (English, French) academic environment. As an active member of a research team, the intern will be exposed to day-to-day research activities, providing an opportunity to evaluate their own interest in pursuing graduate studies and potentially a career in research. Through exchanges with colleagues, preparation of documentation about the solution developed, and presentation to a broad group of researchers in computer vision, robotics, automation, and machine intelligence, the participant will improve their communication skills and teamwork abilities.
Skills required: The project will be performed by one research intern possessing a solid background in mechatronics, manufacturing, mechanical, or computer engineering, with a demonstrated interest in sensing, data acquisition and automation. Prior experience with robotics or machine vision is an asset. Strong programming skills are necessary as software development will be performed using the C/C++/Python/Matlab programming languages and specialized image processing resources (e.g., OpenCV), and will involve the manipulation of 3D data. Professional-level communication skills in English are essential.
3. Data-Driven Defect Detection and Process Monitoring during Laser-Beam Powder Bed Fusion (PBF-LB) of Metals for Nuclear Energy Applications
Supervisor: Mostafa Yakout
University: University of Alberta (Edmonton campus)
Additive manufacturing (AM) is increasingly being considered for the production of critical components used in nuclear energy systems. However, widespread industrial adoption requires reliable methods for detecting and mitigating manufacturing defects that can compromise structural integrity and long-term performance. Recent advances in machine learning, computer vision, and in-situ sensing technologies provide new opportunities to monitor the additive manufacturing process in real time and improve component quality.
The objectives of this project are: (1) developing data-driven approaches for identifying and classifying defects during laser-beam powder bed fusion (PBF-LB) of metallic materials (e.g., IN617, 316L, 316H, etc.) used in nuclear applications; (2) correlating in-situ sensor signals with defect formation mechanisms and resulting material quality; and (3) establishing predictive models capable of supporting real-time process monitoring and quality assurance.
The research project will utilize data collected from high-speed cameras, thermal imaging systems, and process monitoring sensors during PBF-LB of nuclear-grade metallic materials such as stainless steels and nickel-based alloys. Image processing techniques and machine learning algorithms will be applied to detect and classify common manufacturing defects, including porosity, lack-of-fusion regions, spatter-related defects, and keyhole instability. Experimental observations will be correlated with post-build characterization results to establish robust relationships between sensor signatures and final part quality. The project will contribute to the development of intelligent manufacturing systems that improve reliability, reduce inspection costs, and accelerate qualification of additively manufactured components for nuclear energy applications.
Research area, student roles & skills
Research area: Dr. Yakout is the Director and Founder of the Alberta Next-Generation Additive Manufacturing (ANGAM) Lab, a multidisciplinary research program that attracted over 28 undergraduate and graduate students last year at the University of Alberta. His research program focuses on the development of process-driven solutions for additive manufacturing of next-generation materials, such as high-performance alloys, high-temperature materials, advanced ceramics, and materials containing rare-earth elements and critical minerals, for applications that require advanced functionality and performance in extreme environments in the nuclear, defence, energy, and aerospace sectors. He is an expert in materials processing, laser-material interactions, and manufacturing engineering.
Student roles: The student is expected to: (1) Receive training and gain hands-on experience in the following areas: AM of metals, advanced methods in PBF-LB AM, advanced monitoring techniques (e.g., high-speed thermal imaging), materials characterization techniques, and mechanical testing. (2) Perform AM experiments (e.g., run the AM machine in the ANGAM lab), analyze experimental data, compare data with the literature, and characterize AM samples. (2) Contribute to writing manuscripts for publication in collaboration with graduate students and postdoctoral fellows working on the project. (3) Present research work (e.g., analyzed data, experimental results) to the research team and industry partners involved in the project. (4) Participate in meetings and workshops related to the project and the ANGAM lab.
Skills required: The student should have expertise and experience in AM of metals, design of experiments (DOE), sensors, in-situ monitoring, high-temperature mechanical testing, data processing, and materials characterization.
4. Design of Additively Manufactured Lattice Cooling Structures for EV Battery Thermal Management Using CFD
This project will explore how lattice-based cooling structures can be designed for electric vehicle battery thermal management using additive manufacturing and CFD simulation. The student will help create and compare different lattice geometries, evaluate coolant flow and heat transfer performance, and study temperature distribution and pressure drop within the designs. The work is well suited for a fourth-year engineering student interested in thermal systems, simulation, additive manufacturing, and EV technologies. Experience with CAD and CFD would be helpful, but strong problem-solving skills and willingness to learn are equally important.
Research area, student roles & skills
Research area: Machining, fixture dynamics, machinability, Sustainable machining, environmentally Benign Machining, Environmentally friendly design and manufacturing (EFD/EFM) and Computer Aided Design, Manufacturing and Engineering (CAD/CAM/CAE).
Student roles: the Student will be working within the research team to complete tasks assigned to him under my supervision and will be guided and trained by PHD students in the lab. http://www.uoguelph.ca/aml/
Skills required: Training will be provided, but the student must be enrolled in a mechanical engineering program. knowledge of , Matlab and ANSYS and related software packages.
The proposed project aims to develop high-performance aluminum alloys for electrical conductor applications, combining high electrical conductivity with improved mechanical strength. The work focuses on alloy design and process optimization in systems such as Al–Mg–Si and 1xxx series alloys, which are widely used in conductive applications.
The project will investigate how alloying elements, impurity levels, and microalloying additions influence precipitation behavior, solute distribution, and defect structures, and how these factors affect the balance between conductivity and strength. Particular attention will be given to thermomechanical processing routes, including deformation and heat treatments, to tailor microstructure through controlled recrystallization, precipitation, and recovery mechanisms.
Experimental work includes alloy processing, controlled thermomechanical treatments, and subsequent characterization of microstructure, mechanical properties, and electrical conductivity using advanced analytical techniques. The objective is to establish processing–microstructure–property relationships that enable the design of alloys with optimized performance.
Conducted at CURAL (UQAC), this project provides a strong foundation for graduate research and can be extended to more advanced alloy design strategies and processing optimization approaches for next-generation conductive aluminum materials.
Research area, student roles & skills
Research area: My research focuses on the physical metallurgy and processing of metallic materials, particularly aluminum alloys and advanced structural materials. It integrates alloy design, processing, and microstructure–property relationships to enhance performance.
Key areas include Direct Chill casting of aluminum alloys, additive manufacturing, and phase transformations in Al alloys and steels. The work is supported by computational thermodynamics and process modeling to optimize materials and processing conditions.
Advanced characterization techniques are used to link microstructure with mechanical behavior, including deformation and fatigue. The research group has strong international collaboration and works closely with industry to develop high-performance, reliable materials for demanding applications.
Student roles: The student will play an active role in the execution of the research project, contributing to both experimental work and data analysis. The responsibilities include conducting a comprehensive literature review to gather and synthesize relevant information from scientific publications and technical sources. The student will be trained and involved in microstructure analysis and characterization, including sample preparation, mounting, polishing, and the use of optical microscopy and scanning electron microscopy (SEM). They will also be trained and participate in mechanical properties evaluation, such as hardness testing and related measurements. In addition, the student will analyze experimental results, interpret findings, and establish links between processing, microstructure, and properties. The role also includes contributing to the writing of technical reports and scientific documents, ensuring clear and structured presentation of results.
Skills required: The ideal candidate should have a strong academic background in materials science, metallurgy, or mechanical engineering, with a solid understanding of the relationships between processing, microstructure, and material properties. Familiarity with mechanical properties evaluation and microstructural characterization techniques is required, along with an ability to interpret experimental data. The student should be comfortable working in a research environment that involves experimental work, data analysis, and problem-solving. Experience with metallic materials, particularly aluminum alloys, is an asset. In addition to technical skills, the candidate must demonstrate the ability to work effectively within a team, communicate clearly, and collaborate with researchers.
6. Energy Efficient Novel Material Actuators for Insect-Scale Robots
In disaster recovery operations and high-risk environments, the deployment of insect-scale robotic platforms presents a promising approach to data acquisition where physical manipulation is not required. These microrobots, lightweight, mobile, and minimally invasive, excel in tasks requiring wide-area coverage and real-time sensing. However, their limited size, weight, and power capacity significantly constrain autonomous operation, with energy management being the most critical challenge.
This project addresses the developing mechanical, piezoelectric or other material based actuators for insect-scale robotic systems. Key research areas include examining novel and traditional material properties to exploit to act as actuators to support the deployment of insect-scale robots in real-world scenarios. Results will be documented and shared through peer-reviewed publications and conference presentations to promote further innovation in related fields.
Research area, student roles & skills
Research area: My research specializes in the development of metals and materials for the transportation, biomedical and other sectors. For automotive vehicles, complex geometry components with high strength and low weight are required to reduce greenhouse gas emissions. My research involves developing new materials based on aluminum alloys, advanced polymers and magnesium alloys that are light, strong and easy to manufacture. I also examine the microstructure and mechanical properties of these materials for a better understanding of their behaviour to improve manufacturability, sustainability and performance.
Student roles: The student will work independently or with a team of other undergraduate and graduate students in carrying out experiments using 3D printers and other tools. Additional duties will include manufacturing complex assemblies of components to produce insect-scale robots and actuators. The student will also conduct further analysis on how manufacturing parameters influence the performance of the prepared insect-scale robots. The developed insect-scale robots will be examined using microscopy and mechanical testing. All training related to safety and the operation of equipment will be provided. The results from this analysis will be compared to previous research in the field. Finally, the student will summarize their findings, analyze their results and prepare tables, graphs or other documentation to present their research. The research progress is expected to be presented during group meetings.
Skills required: The ideal student would be studying engineering with a focus on materials, manufacturing, metallurgy, mechanical, or a similar type field. The student will be trained in all aspects of safety and operation of equipment. The proposed research is hands-on as well as theoretical. The student must be hardworking, motivated and able to work in teams to conduct the experiments safely and proficiently. Students must also be comfortable explaining their findings to peers in reports and presentations.
7. From Survey to Insight: Using Predictive Analytics to Identify Drivers and Barriers to Circular Construction in Canada
This research project investigates the drivers and barriers that influence early-stage material selection decisions in the construction industry, with a focus on circular economy practices for non-structural building materials such as drywall and gypsum board, as high contribution (12%) as part of non-structural materials. The project applies artificial intelligence tools (e.g., Bayesian Belief Network, Regression Analysis, etc.) and other predictive analytics techniques to data collected through a structured survey and focus groups with construction industry stakeholders in Alberta, including architects, engineers, contractors, and material suppliers. The goal is to identify and quantify the statistical relationships between key decision criteria, such as cost, regulatory requirements, supply chain reliability, and stakeholder risk perception, and the likelihood of selecting circular or recycled-content materials during early design and procurement.
Working from this analysis, the intern will help develop a preliminary framework for monitoring and ranking circular economy adoption barriers across the Alberta construction sector, which will form the foundation for a more comprehensive risk-based decision-support model. This internship serves as a feasibility assessment in advance of an upcoming NSERC Alliance grant application (Theme 2: Risk-Based Modelling of Circular Material Decision Pathways), and the results will directly inform that submission. Should the NSERC Alliance application be successful, the intern will have the opportunity to continue with the research team as a research assistant on the larger, multi-year project.
The position offers hands-on experience in applied machine learning, survey design and analysis, and construction industry data and engagement with stakeholders, and is well suited to a student with an interest in sustainability, data science, or construction management. A background in marketing or consumer and stakeholder behaviour research is considered an asset, given the project's emphasis on understanding stakeholder decision-making and perception.
Research area, student roles & skills
Research area: My research develops optimization and risk-aware decision-making frameworks that integrate techno-economic analysis, life cycle assessment, and multi-criteria decision-making to address complex supply chain and operations problems, including circular economy adoption in construction. I translate these models into interactive dashboards and decision-support tools that make advanced analytics transparent and actionable for industry. I have published over 25 peer-reviewed articles in journals such as the Journal of Cleaner Production and Computers & Chemical Engineering, and previously led industry-collaborative sustainability projects as a postdoctoral fellow at UBC, with funding from Mitacs Accelerate, NSERC Alliance, and the UBC Circular Economy
Student roles: The student will work closely with the supervisory team (at MacEwan University and University of British Columbia (Faculty of Management)) to support all stages of this research project. Initial responsibilities include conducting a focused literature review on circular economy adoption barriers in construction and assisting in the design and refinement of a survey instrument targeted at Alberta-based architects, engineers, contractors, and material suppliers. The student will help coordinate and, where appropriate, facilitate focus group sessions with industry stakeholders, and will be responsible for organizing the resulting survey and qualitative data.
The core technical component of the role involves building and training an interrelationship-based network model to identify statistical relationships between decision criteria, such as cost, regulatory requirements, and risk perception, and stakeholder material selection behaviour. The student will work with the supervisory team to interpret model outputs and translate them into a preliminary framework for monitoring and ranking circular economy adoption barriers across the sector.
The student will also contribute to a feasibility report summarizing the project's findings, which will directly support an upcoming NSERC Alliance grant application. This includes assisting with figures, tables, and plain-language summaries suitable for both academic and industry audiences. The student will participate in regular research team meetings, present interim findings, and may contribute to a conference presentation or manuscript. Strong organizational skills and the ability to manage multiple concurrent tasks independently are essential, as the student will be expected to take ownership of specific deliverables within the project timeline.
Skills required: Applicants should have a background in engineering, data science, computer science, statistics, or a related quantitative field, with experience or coursework in machine learning, artificial neural networks, or statistical modelling. Proficiency in Python, R, or a comparable analytics platform is an asset. Experience with survey design, qualitative analysis, or focus group facilitation is an asset. Strong written and verbal communication skills are essential for engaging Alberta-based industry stakeholders. A background in marketing, consumer behaviour, or business is considered a bonus given the project's stakeholder-focused design. Interest in sustainability or construction is preferred.
8. Hybrid 3D Printing and Casting of Light Metals for Next Generation Vehicles and Biomedical Applications
To reduce greenhouse gas emissions and reduce the impact the transportation industry has on the environment, automotive vehicle manufacturers are adapting lightweight materials such as aluminum, advanced polymers and magnesium alloys to produce strong, easy to manufacture and recyclable components. As well, for biomedical implant manufacturers, magnesium and its alloys is seen as the next frontier in developing complex geometry fixation devices for bone regeneration. As well, polymer coatings on magnesium alloys offers another dimension to control corrosion and improve biocompatibility.
The fastest route from raw material to finished product is through casting where the metal is heated until liquid and poured into a mold composed of sand or steel. The metal is then allowed to solidify in the mold before it is removed and further processed using machining or heat treatment. Casting combined with additive manufacturing or 3D printing has allowed for the production of complex components with design features not previously possible.
The research will involve using 3D printers to produce mold templates for metallic components. The research will also involve conducting analysis of the produced components with tensile testing, hardness testing and advanced microstructure analysis using optical and electron microscopes. The proposed research will examine how to optimize 3D printing parameters and solidification parameters to produce strong, ductile and defect free components. Similar testing and analysis will be conducted with novel polymers as well as their joining to metallic materials.
The research will help support the development of 3D printed materials and alloys for multiple industries.
Research area, student roles & skills
Research area: My research specializes in the development of metals and materials for the transportation, biomedical and other sectors. For automotive vehicles, complex geometry components with high strength and low weight are required to reduce greenhouse gas emissions. My research involves developing new materials based on aluminum alloys, advanced polymers and magnesium alloys that are light, strong and easy to manufacture. I also examine the microstructure and mechanical properties of these materials for a better understanding of their behaviour to improve manufacturability, sustainability and performance.
Student roles: The student will work independently or with a team of other undergraduate and graduate students in carrying out casting and solidification experiments with different alloys. The student will be required to carry out all the required tasks in producing a cast component including melting, preparing the mold, pouring and removing the part from the mold. Additional duties will include developing 3D printed molds to produce complex shaped components to assess liquid metal flowability, tendency to shrink as well as ability to fill thin sections. The student will also conduct further analysis on how 3D printing parameters influence the surface roughness and performance of prepared materials. The materials will be examined using microscopy and mechanical testing. All training related to safety and the operation of equipment will be provided. The results from this analysis will be compared to previous research in the field. Finally, the student will summarize their findings, analyze their results and prepare tables, graphs or other documentation to present their research. The research progress is expected to be presented during group meetings.
Skills required: The ideal student would be studying engineering with a focus on materials, manufacturing, metallurgy, mechanical, biomedical or a similar type field. The student will be trained in all aspects of safety and operation of equipment. The proposed research is hands-on as well as theoretical. The student must be hardworking, motivated and able to work in teams to conduct the experiments safely and proficiently. Students must also be comfortable explaining their findings to peers in reports and presentations.
The continually increasing demand for energy has driven the need for reliable infrastructure in order to extract, transport, and convert the energy resources in Canada. However, fabrication of welded pipelines represents is one of the most costly pieces of energy infrastructure. This is mainly due to the need for high-productivity welding processes which also provide good reliability in the final joints. There is also a need for a deeper understanding of the materials selected for energy equipment and their performance following welding, since the degradation of the material is inevitable in the heat affected zone of the weld. An analysis of the microstructure, chemistry and welding parameters in various grades of steel joints made using existing mechanized welding techniques will be used to develop optimized processing parameters, which yield robust joints and improve integrity. The productivity of advanced laser welding and hybrid laser welding techniques will also be assessed in order to promote their widespread implementation in future designs.
Research area, student roles & skills
Research area: Current research focuses on automotive and energy infrastructure, and this will involve several new research projects involving the use of laser hybrid arc welding for advanced alloys. This new direction will focus on the development of higher strength and toughness joints for these applications.
Student roles: The student will be trained on robotic welding and will be mentored by graduate students working on closely related research topics. The student will learn to prepare and interpret the microstructural features of the various weld zones, and become experienced in the development of weld procedures. The student will perform quantitative microscopy to determine the volume fractions of various steel micro-constituents in the steels (ie: acicular ferrite, martensite, bainite), and these will be correlated to the thermal profile based on the continuous cooling diagram. The student will assess how closely the actual thermal profile matches with that given by the microstructure based on the continuous cooling diagram. Near the end of the project the student will provide a report summarizing their findings, and an effort will be made to submit the report as a journal or conference publication to showcase their work. This will become a team effort in which writing duties and analysis will be combined with efforts of existing graduate students.
Skills required: The student must have a strong background in physical metallurgy, and mechanical deformation. Ideally the student should be familiar with metallographic preparation and characterization techniques, particularly with steels.
10. Laser-Beam Powder Bed Fusion (PBF-LB) of Nuclear-Grade 316L and 316H Stainless Steels for Advanced Reactor Applications
Supervisor: Mostafa Yakout
University: University of Alberta (Edmonton campus)
Small modular reactors (SMRs) and next-generation nuclear energy systems require structural materials capable of operating safely under elevated temperatures, corrosive environments, and prolonged service conditions. Austenitic stainless steels, particularly 316L and 316H, are widely used in nuclear systems because of their excellent corrosion resistance, weldability, and high-temperature mechanical performance. However, conventional manufacturing methods limit design flexibility and increase material waste for complex reactor components. Laser-beam powder bed fusion (PBF-LB) offers a promising alternative by enabling the fabrication of near-net-shape components with complex geometries and enhanced material control (e.g., control of material microstructure over the build direction).
The objectives of this project are: (1) investigating the relationships between PBF-LB process parameters, microstructure evolution, and mechanical properties of 316L and 316H stainless steels for nuclear applications; (2) identifying and quantifying defect formation mechanisms, including porosity, lack-of-fusion defects, and keyhole-induced defects; and (3) evaluating the suitability of additively manufactured 316L and 316H components for advanced nuclear energy systems.
The research project will focus on studying melt pool behavior, solidification mechanisms, grain structure evolution, and defect formation under various laser processing conditions. Samples will be fabricated using a PBF-LB system, followed by microstructural characterization using optical microscopy, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD). Mechanical performance will be assessed through hardness testing and tensile testing. The resulting process-structure-property relationships will contribute to the qualification and deployment of additively manufactured stainless steel components for future nuclear reactor systems.
Research area, student roles & skills
Research area: Dr. Yakout is the Director and Founder of the Alberta Next-Generation Additive Manufacturing (ANGAM) Lab, a multidisciplinary research program that attracted over 28 undergraduate and graduate students last year at the University of Alberta. His research program focuses on the development of process-driven solutions for additive manufacturing of next-generation materials, such as high-performance alloys, high-temperature materials, advanced ceramics, and materials containing rare-earth elements and critical minerals, for applications that require advanced functionality and performance in extreme environments in the nuclear, defence, energy, and aerospace sectors. He is an expert in materials processing, laser-material interactions, and manufacturing engineering.
Student roles: The student is expected to: (1) Receive training and gain hands-on experience in the following areas: AM of metals, advanced methods in PBF-LB AM, advanced monitoring techniques (e.g., high-speed thermal imaging), materials characterization techniques, and mechanical testing. (2) Perform AM experiments (e.g., run the AM machine in the ANGAM lab), analyze experimental data, compare data with the literature, and characterize AM samples. (2) Contribute to writing manuscripts for publication in collaboration with graduate students and postdoctoral fellows working on the project. (3) Present research work (e.g., analyzed data, experimental results) to the research team and industry partners involved in the project. (4) Participate in meetings and workshops related to the project and the ANGAM lab.
Skills required: The student should have expertise and experience in AM of metals, design of experiments (DOE), fatigue of metals, high-temperature mechanical testing, powder metallurgy, heat treatments, and materials characterization.
11. Multiscale Analysis of Turbulent Filling and Rapid Solidification in High-Pressure Vacuum Die Casting
The proposed research project focuses on the processing–structure–property relationships in high-pressure vacuum die casting of aluminum alloys. The work aims to understand how turbulent mold filling, heat transfer, and rapid solidification influence defect formation and microstructure evolution.
The project combines experimental characterization at multiple length scales with computational modeling to analyze flow behavior, solidification dynamics, and resulting microstructures. Emphasis is placed on linking processing parameters to porosity, grain structure, and mechanical performance.
The overall objective is to develop predictive understanding and optimized processing strategies for producing high-quality aluminum components with improved reliability and performance in industrial applications.
Research area, student roles & skills
Research area: My research focuses on physical metallurgy and processing of metallic materials, emphasizing aluminum alloys and their microstructure–property relationships. The work integrates alloy design, casting and solidification processes, and thermomechanical processing to develop materials with optimized mechanical performance and reliability.
A key aspect involves understanding turbulent flow, filling behavior, and rapid solidification in high-pressure vacuum die casting, and how these phenomena influence defect formation and microstructure development. Multiscale characterization techniques are combined with computational modeling to analyze process–structure interactions and guide optimization.
The objective is to establish processing–microstructure–property relationships for high-performance aluminum alloys in demanding industrial applications.
Student roles: The student will play an active role in the execution of the research project, contributing to both experimental work and data analysis. The responsibilities include conducting a comprehensive literature review to gather and synthesize relevant information from scientific publications and technical sources. The student will be trained and involved in microstructure analysis and characterization, including sample preparation, mounting, polishing, and the use of optical microscopy and scanning electron microscopy (SEM). They will also be trained and participate in mechanical properties evaluation, such as hardness testing and related measurements. In addition, the student will analyze experimental results, interpret findings, and establish links between processing, microstructure, and properties. The role also includes contributing to the writing of technical reports and scientific documents, ensuring clear and structured presentation of results.
Skills required: The ideal candidate should have a strong academic background in materials science, metallurgy, or mechanical engineering, with a solid understanding of the relationships between processing, microstructure, and material properties. Familiarity with mechanical properties evaluation and microstructural characterization techniques is required, along with an ability to interpret experimental data. The student should be comfortable working in a research environment that involves experimental work, data analysis, and problem-solving. Experience with metallic materials, particularly aluminum alloys, is an asset. In addition to technical skills, the candidate must demonstrate the ability to work effectively within a team, communicate clearly, and collaborate with researchers.
12. Regenerated cellulose fibres from hemp and recycled textiles
Supervisor: Patricia Dolez
University: University of Alberta (Edmonton campus)
The project is nested into a large R&D program that looks at establishing a made-in-Canada supply of regenerated cellulose fibres using Canadian sources of cellulose and the environmentally-friendly Lyocell process. It aims at determining the optimal characteristics of the sources of cellulose and parameters of the production process as well as the appropriate performance improvement technologies for regenerated cellulose fiber products through the Lyocell process for different applications. The applications targeted by the project include personal protective equipment (PPE), dental floss and healthcare products, nonwoven products, filtration media, and consumer apparel textiles.
The project will determine the parameters leading to the production of high-performance regenerated cellulose filaments using local sources of cellulose and the environmentally-friendly Lyocell process. In the first phase of the project, the pulp dissolution and extrusion processes will be optimized to get the best performance for the pure cellulose filaments. In the second phase, the performance of the filament will be improved using different technologies to meet the requirements associated with the different applications of interest.
This project will pave the way to produce made-in-Canada environmentally friendly cellulose-based fibres for a wide range of products.
Research area, student roles & skills
Research area: Worldwide demand for man-made cellulosic fibres is increasing as availability of cotton fibre declines due to climate change. The regenerated cellulose Lyocell process offer large advantages over other processes in terms of both environmental and social impacts: the solvent for cellulosic dissolution can be recycled, and the process utilizes non-toxic chemicals and low amounts of water. Hemp is an environmentally conscious cellulosic feedstock for Lyocell man-made cellulosic fibres as hemp cultivation results in carbon dioxide sequestration and requires less water, fertilizers, pesticides, and herbicides than other feedstock crops. Another interesting feedstock for these fibers is post-consumer cellulosic textiles.
Student roles: The mission of the student in this project includes the following tasks: • Prepare an experimental design • Produce samples • Characterize the sample performance • Analyze the results • Produce technical reports • Prepare progress presentations
The student will work in close collaboration with the rest of the research team, which includes Master and PhD students, postdoctoral fellows, and other interns. They will be trained on different manufacturing and characterization techniques in the state-of-the-art laboratories at the University of Alberta. Throughout the internship, they will also have the opportunity to interact with the industrial partners involved in the project.
Skills required: In addition to solid skills in materials science and engineering, it is critical that the student is curious and rigorous. They should also be able to work independently while displaying a good ability for teamwork. Oral and written communication skills are important as well. Expertise in textiles and lab work experience, for instance at preparing samples and characterizing materials performance, are an asset.
This research project focuses on developing a high-fidelity digital twin of a CNC machine tool feed drive system. Digital twins are virtual representations of physical assets that replicate their dynamic behavior and enable performance prediction, optimization, condition monitoring, and advanced control. The accuracy and usefulness of a digital twin depend on the fidelity of its underlying mathematical models.
The project will focus on the ball-screw feed drive system of a CNC machine tool, a complex mechatronic system consisting of structural, mechanical, electromechanical, and control components. The research will begin with a review of the state of the art in feed drive modeling, covering key components such as servo motors, ball screws, bearings, guideways, controllers, and friction interfaces. This review will identify suitable modeling approaches and parameter identification techniques for developing an integrated digital twin.
Building on this foundation, the student will develop a comprehensive multi-physics model of the feed drive system available in the laboratory. The model will capture the dominant physical phenomena affecting feed drive performance, including structural dynamics, transmission compliance, friction, backlash, bearing behavior, and servo control dynamics. Experimental testing will be conducted to characterize the system and identify model parameters using machine signals and external sensors.
The developed model will be calibrated and validated against experimental measurements collected under a range of operating conditions. The resulting high-fidelity model will provide an accurate representation of the physical feed drive system and serve as a platform for future research in machine tool dynamics, performance optimization, fault diagnosis, predictive maintenance, and intelligent manufacturing.
Expected outcomes include a comprehensive review of feed drive modeling methodologies, a validated multi-physics model of a CNC feed drive system, and scholarly publications contributing to the advancement of digital twin technologies for advanced manufacturing.
Research area, student roles & skills
Research area: The Dynamics and Digital Manufacturing (DDM) lab of UVic specializes in machine tool dynamics, intelligent manufacturing, and mechanical vibrations. Our lab is equipped with various research test setups for advanced manufacturing, including a 6-axis KUKA robot with a machining end-effector, a 3-axis Computer Numerical Control (CNC) machine tool, a micro-milling center, dSpace real-time computer, a full suit of force and vibration measurement sensors and actuators.
Student roles: The student is responsible for conducting a systematic review of the current mathematical models of various components of the CNC feed drive. The results of this research should be documented and presented as a comprehensive report on feed drive modeling or a review paper. Subsequently, the student will develop multi-physics model of the entire system, combining capabilities from various modeling software such as Matlab Simulink, SimScape, and Comsol. Ultimately, the student will perform system identification and other experiments to calibrate the model and validate its fidelity under varying conditions. In addition, you will have access to our research equipment to experiment and learn about your topics of interest in dynamics, vibrations, and manufacturing. You will attend our regular group meetings to learn about other team members' research projects, present your work, and receive feedback from your lab mates. We are deeply committed to fostering a collegial, inclusive, and equitable lab environment, ensuring everyone feels valued and supported.
Skills required: -Interest in digital twin technology and its applications in improving the efficiency and performance of CNC machine tools. -Excellent written communication skills, as the project results will contribute to a detailed survey report or review paper. -Familiarity with systematic literature review. -Strong understanding of mathematical modeling and its application to mechanical systems -Familiarity with CNC machine tools, its components like ball-screw, and the prevalent defects in mechanical systems like backlash and clearance is an asset. -Previous co-op or work experience in industrial and manufacturing sectors is an asset
14. Sustainability and Recycling of Aluminum Products
The proposed research project focuses on the recycling of aluminum to develop sustainable, high-performance materials for industrial applications. The work aims to optimize the use of recycled aluminum while maintaining or enhancing mechanical properties and functional performance.
The project will investigate how impurities and compositional variations introduced during recycling influence microstructure evolution, phase transformations, and final material properties. Thermomechanical processing strategies, combined with advanced characterization and modeling tools, will be employed to control microstructure and optimize alloy performance.
The overall objective is to establish processing–microstructure–property relationships that enable the production of cost-effective, environmentally sustainable aluminum materials suitable for high-value applications.
Research area, student roles & skills
Research area: My research focuses on sustainable aluminum materials, emphasizing recycling and their use in industrial applications. The work integrates alloy design, processing, and microstructure engineering to enable recycled aluminum while maintaining high mechanical performance.
A key aspect is understanding how impurity elements and compositional variations introduced through recycling affect microstructure evolution and resulting properties. The approach combines thermomechanical processing with advanced characterization and modeling tools to control microstructure and optimize performance.
The objective is to develop high-performance, cost-effective, and environmentally sustainable aluminum conductors by improving the utilization of recycled aluminum in industrial applications.
Student roles: The student will play an active role in the execution of the research project, contributing to both experimental work and data analysis. The responsibilities include conducting a comprehensive literature review to gather and synthesize relevant information from scientific publications and technical sources. The student will be trained and involved in microstructure analysis and characterization, including sample preparation, mounting, polishing, and the use of optical microscopy and scanning electron microscopy (SEM). They will also be trained and participate in mechanical properties evaluation, such as hardness testing and related measurements. In addition, the student will analyze experimental results, interpret findings, and establish links between processing, microstructure, and properties. The role also includes contributing to the writing of technical reports and scientific documents, ensuring clear and structured presentation of results.
Skills required: The ideal candidate should have a strong academic background in materials science, metallurgy, or mechanical engineering, with a solid understanding of the relationships between processing, microstructure, and material properties. Familiarity with mechanical properties evaluation and microstructural characterization techniques is required, along with an ability to interpret experimental data. The student should be comfortable working in a research environment that involves experimental work, data analysis, and problem-solving. Experience with metallic materials, particularly aluminum alloys, is an asset. In addition to technical skills, the candidate must demonstrate the ability to work effectively within a team, communicate clearly, and collaborate with researchers.