Detection of surface anomalites on electric motors based on visual deep learning methods

Ali Sami Gözükirmizi, Ömer Cihan Kivanç
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Abstract

Automotive industry is one of the most advanced industry among others due to the fact that engineering challenges, number of processes and other difficulties. Every component, electrical and mechanical parts produced must pass quality and performance tests in order to be assembled. For this inspection and test purpose, global brands (tier-one) and its related companies(tier-two) invest lots of industrial automation equipment to standardize production quality and minimize risks which can damage brand value, economical states and human safety. In addition to that digitalization and growing number of automation systems are core features of Industry 4.0 concept which is a global trend and automotive is leading industry. All of mentioned inspection tasks are essential for all automotive industry including sub-industries, especially in electric cars are increasing trend and will become dominant against fuel vehicles in next 10 years.
基于视觉深度学习方法的电机表面异常检测
汽车工业是其中最先进的行业之一,由于工程挑战,工艺数量和其他困难。生产的每个部件、电气和机械部件必须通过质量和性能测试才能组装。为了这种检验和测试的目的,全球品牌(一级)及其相关公司(二级)投入了大量的工业自动化设备,以规范生产质量,最大限度地降低损害品牌价值、经济状态和人身安全的风险。此外,数字化和越来越多的自动化系统是工业4.0概念的核心特征,这是一个全球趋势,汽车是主导行业。上述所有检测任务对于所有汽车行业包括子行业都是必不可少的,特别是在电动汽车呈增长趋势,并将在未来10年内成为燃油汽车的主导。
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