In manufacturing, monitoring machine health is an important step when implementing Industry 4.0 and ensures effective machining operations and minimal downtime. Monitoring the health of cutting tools during a machining process helps contain the faults associated with gradual tool wear, because they can be tracked and responded to as wear worsens. Left unchecked, tool failures can lead to more severe problems, such as dimensional and surface issues with machined workpieces and lower overall productivity during the machining process. This research explores the use of a machine vision setup used internally by the McMaster Manufacturing Research Institute (MMRI) in their three lathe machines. This machine vision setup provides a direct indication of the tool's maximum flank wear (VBmax), which, according to ISO 3685:1993(E), is set to be 300 µm. Also investigated was the use of image processing and analysis methods to determine the flank wear without removing the tool from the machine. This new, in-machine vision setup is intended to replace the use of an external optical microscope, which requires extended downtime between cutting passes. As a result of this replacement, the experimentation downtime was decreased by around 98.6%, leading to the experiment time to decrease from 5 weeks or more to just a couple of days. In addition, the difference in measurement between a commonly used optical microscope and in-machine vision setup was found to be ±3µm. / Thesis / Master of Science (MSc)

Current methods of determining spinal cord recovery in mice, post-directed injury, are qualitative measures. This is due to the small size and quickness of mice. This thesis presents a design for a gait acquisition and analysis system able to capture the footfalls of a mouse, extract position and timing data, and report quantitative gait metrics to the operator. These metrics can then be used to evaluate the recovery of the mouse. This work presents the design evolution of the system, from initial sensor design concepts through prototyping and testing to the final implementation. The system utilizes a machine vision camera, a well-designed walkway enclosure, and image processing techniques to capture and analyze paw strikes. Quantitative results gained from live animal experiments are presented, and it is shown how the measurements can be used to determine healthy, injured, and recovered gait.