Grant: Collaborative Research and Development Grants

All Weather Windows is Canada's largest window and door manufacturer, operating a state-of-the-art manufacturing facility in Edmonton. ACQBUILT, Inc., also based in Edmonton, operates North America's leading residential construction manufacturing facility, carrying out homebuilding operations from design and prefabrication to delivery and onsite assembly. Both are seeking to boost their competitive edge through more intelligent use of automation, machine vision, automated material handling systems, and more efficient supply management and logistics. Accordingly, the research team proposes to (1) investigate, research, and develop new unplasticized polyvinyl chloride welding technologies, materials, and machinery which can be used to improve the quality of window frames, with the aim of reducing the amount of human effort required and, accordingly, the overall production cost and cycle time; (2) design and develop an automated material handling system to reduce total manufacturing time and enable safer operations with regard to heavy materials and equipment through well-designed material movement, ergonomic analysis, and human-robotic collaboration; (3) design and develop automated visual inspection systems, which will assist with meeting quality control and assurance standards and specifications, thereby lowering rework rates and manufacturing process waste while ensuring customer satisfaction; and (4) achieve-through a combination of distribution practices, truck fleet management, and technological innovations-marked improvements in supply chain efficiency by improving supply chain management. The industrial partners stand to benefit from an increased production capacity and better product quality based on the implementation of research breakthroughs. This research will benefit Canada as a whole, as the broad adoption of these new technologies and increasingly efficient methods will allow Canadian companies to remain competitive in the global market and deliver cost-effective products. The algorithms to be developed will establish a foundation for further research in optimization, planning, control, and automation of manufacturing processes and systems.