报告题目：AI-Based Industrial Data Analytics: A Case Study in Metal Additive Manufacturing

报告简介：Metal Additive manufacturing (MAM) is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. To ensure the stability of the MAM process, it is of critical importance to carry out data analytics on MAM process by monitoring the sensor data collected and detecting potential defects/outliers. This goal of the data analytic leads to the development of a knowledge-based system which is to readapt Product engineering stages: 1) Building AI model to detect future deviations caused by complex geometries to propose alternative geometry changes; and 2) Modifying the manufacturing strategy based on trained AI algorithm to avoid deposition paths that cause final distortions or heat accumulation. In this talk, we focus on the defect detection of thermal image data and outlier detection of welding sensor data based on artificial intelligence techniques. In the first part, a novel image processing method, an image-enhancement generative adversarial network, with aim to improve the contrast ratio of the thermal images for image segmentation will be discussed. In the second part, a novel clustering-based outlier detection method for anomaly detection will be introduced. The proposed methods are exploited in analyzing the real-world industrial data collected from a wire arc MAM pilot line in Sweden.

报告人：刘伟伯博士，现为Brunel University计算机系讲师，IEEE会员。主要研究方向包括智能数据分析、演化计算、迁移学习、机器学习、深度学习、医疗数据分析和工业大数据。目前担任国际学术期刊Journal of Ambient Intelligence and Humanized Computing和Journal of Cognitive Computation的副主编。

报告时间：2023年7月14日（星期五）下午 3:30-5:30

报告地点：扬州大学瘦西湖校区数学科学学院208报告厅

主办单位：扬州大学数学科学学院

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