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Why is Quality Control needed?<\/b><\/p>\n
Whether companies are manufacturing cars, semiconductor chips, smartphones, or food and beverages, production quality and yield are two of the industry\u00e2€™s top performance metrics.\u00c2\u00a0<\/span><\/p>\n \u00e2€œPoor production quality control results in significant operational and financial costs in the form of reworked parts, scrap generated, reduced yield, increased work in process inventory, post-sale recalls, warranty claims, and repairs.\u00e2€<\/span><\/p>\n \u00e2€œIn fact, the American Society for Quality estimates that for many organizations this cost of quality is as high as <\/span>15-20% of annual sales revenue<\/span><\/a>, or billions of dollars annually for larger manufacturers.\u00e2€<\/span><\/p>\n How can manufacturers solve this problem? The answer is quite simple \u00e2€“ Computer Vision<\/strong>.<\/span><\/p>\n Computer Vision is a miraculous technology<\/span><\/a> that helps manufacturers perform production quality inspections in an accurate and cost-effective manner.<\/span><\/p>\n The problem with existing visual inspection approaches<\/b><\/p>\n Manufacturing processes typically include one or more steps where the product is visually inspected for defects. Typically, visual inspection is a highly manual process that can be time consuming and prone to errors.\u00c2\u00a0<\/span><\/p>\n However, traditional inspection has several drawbacks:\u00c2\u00a0<\/span><\/p>\n The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, to six sigma and, more recently, to enterprise resource planning.\u00c2\u00a0<\/span><\/p>\n Artificial intelligence (AI)<\/span><\/a> promises to bring even more innovation to the forefront. On paper, there are multiple benefits of using AI:<\/span><\/p>\n Before COVID-19, many manufacturers were stuck in \u00e2€œpilot purgatory\u00e2€ when it came to their AI- and ML-based projects, as they were\u00c2\u00a0<\/span>not seeing sufficient business value from their investments.<\/span><\/a>\u00c2\u00a0However,\u00c2\u00a0<\/span>new research<\/span><\/a> from Google Cloud\u00e2€”which polled more than 1,000 senior manufacturing executives across seven countries\u00e2€”indicates that 76% have now turned to digital enablers such as data and analytics, cloud-based services, and AI. Here is a quick overview of the impact of COVID-19 on manufacturing<\/span><\/p>\n Unravelling computer vision applications: How does it work and what are its use cases?<\/b><\/p>\n The advent of the fourth industrial revolution or Industry 4.0 has made it loud and clear that all industries must go digital to stay relevant and profitable. Full automation is no longer a choice, it\u00e2€™s an imminent need.<\/span><\/p>\n Machine Vision provides image-based automatic inspection. It is a system that uses visual computing technology to mechanically \u00e2€œsee\u00e2€ the activities that take place one by one along the production line.<\/span><\/p>\n The components of an automatic inspection system<\/strong> usually include lighting, a camera, a processor, software and other output devices. Machine vision outperforms human vision at the qualitative and quantitative measurement of a structured scene because of its speed, accuracy, and repeatability.<\/span><\/p>\n It can easily assess object details, which are too small to be perceived clearly by the naked eye and inspect them with greater reliability and lesser error. On a production line, machine vision systems can <\/span>inspect hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the inspection capabilities of humans.<\/span><\/p>\n <\/b><\/p>\n Here are some of the irrevocable benefits of Quality Control:<\/b><\/p>\n Computer vision-based approaches ensure a higher grade of accuracy within the accepted tolerance in every manufacturing process. For example, it\u00e2€™s possible to improve the level of bacteria detection for medical equipment by 95%\u00c2\u00a0through CV.\u00c2\u00a0<\/span><\/p>\n When it comes to repetitive work, CV-driven systems\u00c2\u00a0conduct monotonous tasks more effectively. Implementation of a fully automated system definitely speeds up the production time as the machine needs no time for thinking and, compared to an employee, its accuracy and repeatability is far greater.<\/span><\/p>\n An automated system is an effective tool to reduce quality control downtime. As the system is fully automated, it runs much faster, it is able to work 24\/7 and it does not need any breaks for rest.<\/span><\/p>\n An automatic machine vision system provides tangible economic benefits. Using such a system, manufacturing companies do not require working personnel to manually perform control of manufactured products, allowing them to concentrate on more important work.\u00c2\u00a0<\/span><\/p>\n Additionally, a CV-powered system does not make mistakes, which can appear during manual control. The cost of a small human mistake can sometimes be valued\u00c2\u00a0at millions if not billions of dollars and Machine Vision helps to avoid it.\u00c2\u00a0<\/span><\/p>\n Here are some industry use-cases, where Computer Vision has already revolutionized Quality Control:\u00c2\u00a0<\/b><\/p>\n<\/p>\n Long story, short:<\/b><\/p>\n Computer vision<\/strong> is a powerful technology that can help manufacturers improve their production Quality Control processes. Manufacturers across the globe are harnessing the power of CV to improve production, increase uptime and reduce expenses.<\/span><\/p>\n Get in touch with us to see how Computer Vision<\/strong> methods can scale your manufacturing business.\u00c2\u00a0<\/span><\/p>\n<\/p>\n \u00c2\u00a0<\/p>\n <\/body><\/html><\/p>","protected":false},"excerpt":{"rendered":" Why is Quality Control needed? Whether companies are manufacturing cars, semiconductor chips, smartphones, or food and beverages, production quality and yield are two of the industry\u00e2€™s top performance metrics.\u00c2\u00a0 \u00e2€œPoor production quality control results in significant operational and financial costs in the form of reworked parts, scrap generated, reduced yield, increased work in process inventory<\/p>","protected":false},"author":1,"featured_media":1812,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[17],"tags":[],"class_list":["post-1811","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-digital-transformation"],"_links":{"self":[{"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/posts\/1811","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/comments?post=1811"}],"version-history":[{"count":0,"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/posts\/1811\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/media\/1812"}],"wp:attachment":[{"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/media?parent=1811"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/categories?post=1811"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hudutech.com\/ar\/wp-json\/wp\/v2\/tags?post=1811"}],"curies":[{"name":"\u062f\u0628\u0644\u064a\u0648 \u0628\u064a","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}\n
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