Detection of surface anomalies on electric motors based on visual deep learning methods
No Thumbnail Available
Date
2022
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Open Access Color
OpenAIRE Downloads
OpenAIRE Views
Abstract
Otomotiv endüstrisi, mühendislik zorlukları, süreç sayısı ve diğer zorluklar nedeniyle diğerleri arasında en gelişmiş endüstrilerden biridir. Üretilen her bileşen, elektrik ve mekanik parça, monte edilebilmesi için kalite ve performans testlerinden geçmelidir. Bu denetim ve test amacıyla, küresel markalar (tier-one) ve ilgili şirketler (tier-two) üretim kalitesini standartlaştırmak ve marka değerine, ekonomik duruma ve insan güvenliğine zarar verebilecek riskleri en aza indirmek için çok sayıda endüstriyel otomasyon ekipmanına yatırım yapmaktadır. Buna ek olarak, dijitalleşme ve artan otomasyon sistemleri, küresel bir trend olan ve otomotivin lider sektör olduğu Endüstri 4.0 konseptinin temel özellikleridir. Bahsedilen tüm denetim görevleri, yan sanayiler de dahil olmak üzere tüm otomotiv endüstrisi için gereklidir, özellikle elektrikli otomobiller artan bir trenddir ve önümüzdeki 10 yıl içinde yakıtlı araçlara karşı baskın hale gelecektir. Yapay görme sistemleri, elektrikli araç üretimi sırasında elektrik motorlarının ve alt montajlarının denetlenmesi için giderek daha önemli hale gelmektedir. Bu sistemler, bileşenleri kusurlara karşı otomatik olarak incelemek ve analiz etmek için gelişmiş görüntüleme ve bilgisayarla görme tekniklerini kullanarak nihai üründe yalnızca yüksek kaliteli parçaların kullanılmasını sağlar. Elektrikli motor denetimi için yapay görme teknolojisini kullanmanın en önemli avantajlarından biri hızlı ve doğru bir şekilde çalışabilmesidir. Manuel görsel denetim gibi geleneksel denetim yöntemleri insan hatasına açıktır ve yavaş ve emek yoğun v olabilir. Öte yandan, yapay görme sistemleri yüzlerce veya binlerce bileşeni kısa bir süre içinde denetleyebilir ve üretim sürecinin verimli ve uygun maliyetli olmasını sağlar. Yapay görme sistemlerinin bir diğer avantajı da insan gözünün görmesinin zor olabileceği kusurları tespit edebilmesidir. Bu sistemler, bileşenleri mikroskobik düzeyde analiz ederek en küçük kusurları veya kusurları bile belirleyebilir. Küçük kusurlar bile önemli performans sorunlarına yol açabileceğinden, bu özellikle elektrik motorlarının üretiminde önemlidir. Sonuç olarak, elektrikli araç üretimi sırasında elektrik motorlarını ve alt montajlarını incelemek için yapay görme sistemlerinin kullanımı giderek daha önemli hale gelmektedir. Bu sistemler hızlı, doğru ve güvenilir denetim sağlayarak nihai üründe yalnızca yüksek kaliteli parçaların kullanılmasını sağlar.
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 to be assembled. For this inspection and test purpose, global brands (tier-one) and its related companies (tier-two) invest lots of industrial automation equipments 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 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. Machine vision systems are becoming increasingly important for inspecting electric motors and their sub-assemblies during electric vehicle manufacturing. These systems use advanced imaging and computer vision techniques to automatically inspect and analyze components for defects, ensuring that only high-quality parts are used in the final product. One key benefit of using machine vision for electric motor inspection is its ability to operate quickly and accurately. Traditional inspection methods, such as manual visual inspection, are prone to human error and can be slow and labor-intensive. Machine vision systems, on the other hand, can inspect hundreds or thousands of components in a short period of time, ensuring that the manufacturing process is efficient and cost-effective. Another advantage of machine vision systems is their ability to detect defects that may be difficult for the human eye to see. These systems can analyze components at a microscopic level, identifying even the smallest flaws or imperfections. This is particularly important in the production of electric motors, as even small defects can lead to significant performance issues. In conclusion, the use of machine vision systems for inspecting electric motors and their sub-assemblies during electric vehicle manufacturing is becoming increasingly important. These systems provide fast, accurate, and reliable inspection, ensuring that only high-quality parts are used in the final product.
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 to be assembled. For this inspection and test purpose, global brands (tier-one) and its related companies (tier-two) invest lots of industrial automation equipments 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 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. Machine vision systems are becoming increasingly important for inspecting electric motors and their sub-assemblies during electric vehicle manufacturing. These systems use advanced imaging and computer vision techniques to automatically inspect and analyze components for defects, ensuring that only high-quality parts are used in the final product. One key benefit of using machine vision for electric motor inspection is its ability to operate quickly and accurately. Traditional inspection methods, such as manual visual inspection, are prone to human error and can be slow and labor-intensive. Machine vision systems, on the other hand, can inspect hundreds or thousands of components in a short period of time, ensuring that the manufacturing process is efficient and cost-effective. Another advantage of machine vision systems is their ability to detect defects that may be difficult for the human eye to see. These systems can analyze components at a microscopic level, identifying even the smallest flaws or imperfections. This is particularly important in the production of electric motors, as even small defects can lead to significant performance issues. In conclusion, the use of machine vision systems for inspecting electric motors and their sub-assemblies during electric vehicle manufacturing is becoming increasingly important. These systems provide fast, accurate, and reliable inspection, ensuring that only high-quality parts are used in the final product.
Description
Keywords
Mekatronik Mühendisliği, Mühendislik Bilimleri, Mechatronics Engineering, Engineering Sciences, Otomotiv Mühendisliği, Automotive Engineering