报告题目：Deep Learning in Defect Assessment of Optoelectronic Devices

报告简介：Photonic assembly is a popular manufacturing technique which is broadly exploited in rapid prototyping and fabricating components with complex geometries. A novel event-based defect assessment framework is going to be introduced in this lecture to minimize the occurrence of machine breakdowns and component failures during the assembly process. The formulation of the defect assessment model is based on certain inline quality and condition monitoring equipment that helps to realize the zero-defect manufacturing strategy. Two industrial case studies are presented about the online quality prediction and assessment of laser devices. The assessment model is tested in both cases to build the sequence of events or the genomics of production, where a combination of contiguous normally recurring events leads to defective or non-defective products, as the main inputs to the production cost model (output).

报告人：耿航博士，电子科技大学。主要研究方向包括智能测试、信息融合、工业大数据分析等。曾获电子科技大学学术新人奖、中国仪器仪表学会金国藩青年学子奖学金，入选中国科协青年人才托举工程项目。现为中国仪器仪表学会高级会员、中国自动化学会高级会员、中国自动化学会控制理论专业委员会随机系统控制学组委员、四川省科技青年联合会理事、成渝地区双城经济圈科技创新联盟项目评估咨询专家、四川省科技协同创新促进会智能制造委员会委员。担任SCI国际期刊Computer Modeling in Engineering & Sciences副主编、International Journal of Network Dynamics and Intelligence编委。

报告时间：2023年7月28日（星期五）下午 4:00-6:00

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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