The integration of AI and 3D printing in manufacturing can help increase unit production rate, detect defects, and provide real-time control over the manufacturing process. As the name suggests, additive manufacturing is a method of building products by adding layers of components on one another. AI, on the other hand, as everyone knows, can automate monotonous tasks and bring accuracy in those tasks. The manufacturing sector has many repetitive labor tasks that make AI a perfect match for the manufacturing and 3D printing process.. AI can increase the production rate and accuracy of 3D production. Using computer vision, manufacturers can reverse engineer the existing models and create a new and improved product design.
In today's world, artificial intelligence (AI) is transforming several industries, and many of us interact with AI on regular basis in some form or another. From banking and manufacturing to e-commerce and personalized advertisements, AI is becoming imperative in almost every business. Business experts believe that it is an essential tool for data analytics, predictive suggestions, chatbots, and so on. AI can be defined as any software algorithm which possesses human-like features, such as an ability to learn, plan, and solve problems. These attributes can be groomed, and the system made more intelligent depending upon the type of industry where it is going to be used.
In the race to enable manufacturing plants to increase production in the face of an intermittent human workforce, manufacturers are looking at how to supplement their cameras with AI to give human inspectors the ability to spot defective products immediately and correct the problem. While machine vision has been around for more than 60 years, the recent surge in the popularity of deep learning has elevated this sometimes misunderstood technology to the attention of major manufacturers globally. As CEO of a deep learning software company, I've seen how deep learning is a natural next step from machine vision, and has the potential to drive innovation for manufacturers. How does deep learning differ from machine vision, and how can manufacturers leverage this natural evolution of camera technology to cope with real-world demands? In the 1960s, several groups of scientists, many of them in the Boston area, set forth to solve "the machine vision problem."
Computer vision is an important Artificial Intelligence application that will transform many industries and many business processes. Also known as machine vision technology, this data-driven innovation allows machines to interpret the world visually. This visual data can be in the form of photos, videos, or feed from infrared and thermal cameras too. As a way of imitating the human visual system, the researchers in the field of computer vision intend to develop machines that can automate tasks that require visual cognition. One of the most commonly known examples of this technology is facial recognition.
Machine vision quality assurance systems have excelled at automating the location, identification, and inspection of manufactured components through computational image analysis. But when the component is part of a larger assembly, a complex package, or a kit--such as an automotive assembly or surgical intubation kit--defects, random product placement, variations in lighting, and other factors can quickly overwhelm a traditional machine vision system. For this reason, final inspection of assemblies, packages, and kits is usually conducted manually, to the detriment of overall quality and productivity. While manual operators typically excel at inspecting complex assemblies, by comparing multiple attached or connected components to automated quality inspection solutions, it's hard for operators to stay sharp. Studies show most operators can only focus on a single task for 15 to 20 minutes at a time.