Laser machining is a highly flexible non-contact manufacturing technique that has been employed widely across academia and industry. Due to nonlinear interactions between light and matter, simulation methods are extremely crucial, as they help enhance the machining quality by offering comprehension of the inter-relationships between the laser processing parameters. On the other hand, experimental processing parameter optimization recommends a systematic, and consequently time-consuming, investigation over the available processing parameter space. An intelligent strategy is to employ machine learning (ML) techniques to capture the relationship between picosecond laser machining parameters for finding proper parameter combinations to create the desired cuts on industrial-grade alumina ceramic with deep, smooth and defect-free patterns. Laser parameters such as beam amplitude and frequency, scanner passing speed and the number of passes over the surface, as well as the vertical distance of the scanner from the sample surface, are used for predicting the depth, top width, and bottom width of the engraved channels using ML models. Owing to the complex correlation between laser parameters, it is shown that Neural Networks (NN) are the most efficient in predicting the outputs. Equipped with an ML model that captures the interconnection between laser parameters and the engraved channel dimensions, one can predict the required input parameters to achieve a target channel geometry. This strategy significantly reduces the cost and effort of experimental laser machining during the development phase, without compromising accuracy or performance. The developed techniques can be applied to a wide range of ceramic laser machining processes.

Brent is a PhD Candidate in the Department of Computer Science and a member of the Φ Lab and Insight Lab. He was previously the instructor for MMASc 9251A: Professional Computing for Applied Scientists and presently the Teaching Assistant for Unstructured Data. As of March 1st 2019, Brent will also be a Mitacs Accelerate Intern. This work is with the Parkwood Institute and IBM with the target of improving mental health resources for Canadian Veterans. His research interests are two-fold.

AI'2004 was held at the University of Western Ontario in London, Ontario from May 17 to May 19, 2004. The conference was held jointly with the Computer and Robot Vision and Graphics Interface conferences. The three conferences attracted more than 200 attendees. Three workshops and a graduate symposium were held in conjunction with the technical sessions. Three preconference workshops were affiliated with AI'2004: The Third Business Agents and the Semantic Web workshop, organized by Harold Boley, Scott Buffett, Bruce Spencer (National Research Council), Ali Ghorbani (University of New Brunswick), and Said Tabet (Macgregor Inc.); the First Agent Meets Robot workshop, organized by Hamada Ghenniwa, (University of Western Ontario), Weiming Shen (National Research Council) and Mohamed Kamel (University of Waterloo); and the First Causality and Causal Discovery workshop, organized by Kamran Karimi (University of Regina).

AI'2004 was held at the University of AI'2004 had a large program committee were affiliated with AI'2004: The Third Canada (NRC) (who also hosted the hosted one of the longest-running and the First Causality and preconference workshops) cosponsored conference series in the AI field. The Causal Discovery workshop, organized the AI'2004 Graduate Symposium. CSCSI conference started in 1976 as a by Kamran Karimi (University of Regina). The Eighteenth Canadian Conference the conference became an annual forum, In addition, the AI'2004 graduate on Artificial Intelligence thus making AI'2004 the seventeenth symposium attracted many graduate (AI'2005) will be held in Victoria, conference in this series. These events turned the city British Columbia, from May 9 to 11, the years, the conference has attracted of London into a Mecca for AI and 2005.