The ultimate goal of this project is to develop a fundamental understanding of inclusion evolution during a particular refining process in secondary steelmaking unit. The particular focus is firstly on developing a detailed characterization of the inclusions formed during refining in the Stelco Ladle Metallurgy Facility, and secondly on adapting the existing McMaster ladle metallurgy/inclusion model for the Stelco facility. Ultimately this is expected to achieve better process and product control. Inclusions, depending on their size and type, may profoundly affect steel properties.
This research is an attempt to understand a heat transfer challenge encountered in the Glen Dimplex electric baseboard heaters in details and provide recommendations to resolve that. This baseboard heater uses electricity to heat up an internal element which is designed to transfer the heat to the room?s air via a series of thin aluminum plates called fins. The poor heat transfer from heater fins to air results in low efficiency of the heater as well as the unwanted physical deformation of fins by thermal expansion.
Firefighting water additives are a mixture of chemicals that are mixed with water to more effectively extinguish fires (i.e., residential, industrial, forest fires). The use of these additives is likely to increase in fighting forest fires due to the projected increase in forest fire occurrence and intensity due to climate change. Ingredients of firefighting water additives used in the past were found to be persistent and detrimental to the environment.
Metal-based direct energy deposition processes, such as robotic welding and laser powder fed additive manufacturing, ideally require feedback sensing of the deposition quality using vision detectors. Image processing algorithms are challenging to develop due to changing process operating conditions. Despite challenges, implementing in-process image processing algorithms is beneficial for traceability and quality assurance, for calibrating process models, and for developing closed loop control algorithms which are able to maintain deposition quality within acceptable quality margins.
Intern will significantly benefit in terms of knowledge generation and implementation from this research project by learning novel process to shape and modify biopolymer. After successful completion, the intern will learn process to scale up the product to real life application such as fishing line and nets. Plantee Bioplastics will be able to bring the modified product to market and capitalize on research done by the intern. This project has capacity to change the negative public perspective on plastics by bringing in market an improved fishing line and n
Coming into force in October 2019, amendments to the Canadian Cannabis Regulations will introduce guidelines governing the legal production and sale of cannabis-infused extracts, edibles, beverages and topicals. These new products are at the forefront of the natural health product (NHP) and consumer packaged goods (CPG) industries, but challenges associated with their formulation, production and stability are quickly mounting.
The Ladle Metallurgy Furnace is used for adjustment of chemical composition and temperature, and control of tiny particles called “inclusions”. Controlling inclusions is carried out by adding calcium to modify the solid alumina or magnesium aluminate inclusions to less harmful liquid inclusions.
During ladle process, reaction of top slag, steel and inclusions occur simultaneously. Therefore, establishing a model to describe ladle process is indeed a challenge.
This project will create novel goat milk-based products and test their health effects in healthy adults. Interest in goat milk is increasing, mainly due to two market trends: consumers’ new taste trends for niche/ethnic products, and the perception of goat milk having added health properties. Goat milk is perceived as having intestinal anti-inflammatory effects and improved digestive properties compared to bovine milk, due to the unique functionalities of its oligosaccharides and triglycerides.
Reinforcement learning (RL) is the problem of designing an agent that interacts with its environment and adaptively improves its long-term performance. Many complex real-world industrial decision-making problems can be formulated as an RL problem. RL is at the core of artificial intelligence and has the potential of having a huge impact on our economy and society, perhaps more so than any other area of machine learning.
The scope of this research is to provide a comprehensive recipe for powder-fed laser additive manufacturing (PF-LAM) of H13 tool steel. The H13 components manufactured by PF-LAM can potentially show superior mechanical properties as compared to H13 steel manufactured by conventional methods; however, the process optimization is requisite to achieve the additively manufactured H13 tool steel with satisfactory properties. The process is optimized to acquire the printed parts with minimum defects.