Machine Vision Companies Enabling Improving Quality Metrics In PCB Industry

 Table Of Contents 1. Deployment of a machine vision system 2. What are some critical conditions that need to be maintained for high performance? 3. Machine vision maintenance needs Machine vision is a popular tool of the manufacturing industry. Coupled with AI, it has a wide range of use cases. For instance, it can perform an automated visual inspection on objects that are manufactured in thousands every day. Machine vision uses deep learning-based training algorithms to identify and understand the requirements of any particular task assigned to it. These systems are easy to train and implement. They are also reliable, robust, and stable. Machine vision systems are low-cost and high-accuracy devices that can withstand the mechanical and thermal stresses of the industry. Deployment and maintenance of machine vision systems, therefore, is simple. When done regularly, it is a short and quick process. Deployment of a machine vision system Installation of the different modules – Instal...

 6 Parameters that Influence Speed of Machine Vision Inspection Table Of Contents 1. Material Handling Time 2. Trigger delay 3. Image Capture Time 4. Image Transfer Time 5. Image Processing Time 6. Result Communication Time Machine Vision Speed Cycle time in terms of industrial processes refers to the time in which a unit of any task is completed so that the flow system can realize the target quantity output within a certain period. The cycle time is calculated by the daily net-working time divided by the number of units of work performed in the day. The cycle time of a machine vision system is also calculated similarly. The demands of the industry have greatly reduced cycle time with the help of automation and the integration of new technology leading to growing demand for high speed Machine Vision Inspection. The speed of machine vision inspection is largely dependent on — Material Handling Time — The material handling time is the time between which a material to be inspected is...

Table Of Contents 1.  AI in Maintenance 2. AI in Product Quality Inspection 3. AI vs ML vs DL 4.  Why choose Deep Learning models?  5.  Traditional Machine Learning vs Deep Learning  6.  Machine Learning  4.  Developing a solution with Qualitas EagleEye® Platform The fourth industrial revolution is essentially about connected devices. The Internet can connect devices as an interconnected and smart entities. The intelligent manufacturing market has grown up to the size of 2.9 trillion by 2021. 30% of the companies are planning to implement intelligent systems over the coming year. The penetration ability of AI is so much so that by 2030, all major industries will be using AI in some form for their operations. The applications of Deep Learning and Machine Vision in Manufacturing include process control, quality control, maintenance, and production among others. AI in Maintenance –  AI is used to make actionable decisions intelligently with data...

  Table Of Contents 1. The common ROI metrics to measure Machine Vision success 2. Elements people miss during ROI calculation Computer vision is a technology that is programmed to recognize and process visual data. Machine vision requires data inputs to teach a program about different objects and responses to such objects. Patterns detected by the machines to identify an image require many sample images. Storing and processing these samples results in the production of accurate inferences. Using AI with computer vision enhances revenue generation by reducing costs, providing real-time data insights, and helping businesses tailor their services according to consumer preference. The rise of industry 4.0 has led to better connectivity and efficiency. Machine vision contributes to predictive maintenance and quality control in manufacturing and can be leveraged to increase ROI on any project. What are some of the common ROI metrics to measure machine vision? Reduce costs – Costs are ...

Table Of Contents 1. Applications Of Machine Vision 2. How Machine Vision Deals With Small Defects 3. Challenges In Automating Inspection Of Small Parts 4. How Machine Vision See A Defects and Measure It Machine vision technology enables automatic analysis and inspection applications like process control, robotic guidance, or automated inspection using image processing. Machine vision encompasses a wide range of technologies that includes hardware and software products, integrated systems, methods, actions, and expertise. Machine vision is a new technical capability through which existing technology can solve many real-world problems. Machine vision has become increasingly popular within industrial automation spaces because of its capabilities. Using deep learning and machine learning models along with machine vision has enabled businesses to use technology to understand and optimize data better for higher business efficiency. What are the applications of machine vision?  Object d...

  Introduction Agriculture has been there ever since the beginning of civilization. Though humanity has progressed rapidly, agriculture still remains one of the major contributors to several nations’ economies. In fact, in 2019, agriculture, food, and related industries accounted for around $1.109 trillion of the US gross domestic product, with America’s farms contributing 0.6% of the GDP. Also, agriculture and related activities provide 10.9% of US employment. Every country depends on agriculture for something or the other. Here are some reasons why agriculture is so important: A major source of livelihood A significant contributor to the nation’s GDP Supply of food products and raw materials for other large and small industries Significance to international trade Considerable employment opportunities that would further result in economic development Crops demand care at every step until they are harvested. Ecological volatility can destroy a huge portion of the crop produce. It’s...

CASE‌ ‌STUDY‌ AUTOMATED SURFACE INSPECTION OF LPG CYLINDERS TO IDENTIFY DENTS USING 2D CAMERA AND LASER GRIDS (AI VISION SYSTEM) CLIENT’S PROBLEMS Defects like dents bend, cuts, dig, etc. were not being identified during the inspection process due to the limited visibility i.e. only one side of the cylinder could be seen by operators  False acceptance and false rejection cause the prolonged in production time and hence TTM (To the Market) time Operators manually take cylinders off the conveyor as soon as they see a defect as there is no automated rejection mechanism PROBLEM IMPLICATIONS Preventing defective cylinders from reaching the customer is the client's major concern. Improper inspection may result in sending defective cylinders to the customer Improper inspection may result in sending defective cylinders to gas filling stations as they may store less amount of gas or no gas at all because of the defects Supplying defective cylinders in the market would hamper the image and p...

Background of Industrial Image Acquisition Challenges : According to Fanuc, Image Acquisition contributes to 85% of Machine Vision solution success. The workflow of a typical AI based Machine Vision system includes four steps. First, the image acquisition is done through a Plug n Play camera setup that optimizes the acquisition process. Second, data preparation is done by configuring and collecting training images. Third, specialized deep learning architecture trains are used to optimize models using cloud infrastructure. Finally, the optimized model is deployed for inferencing in an edge device and monitored for accuracy. The first step of the workflow, image acquisition is a complex process and has a lot of challenges. However, a regular industrial setting comes with many challenges that make it increasingly hard to acquire perfect images for conducting visual process or quality inspections Let’s look at some real world scenarios: Imaging of complex geometries –  The manufacturin...

  What is Image Processing? Image processing  is a method of performing specific operations on an image in order to get an enhanced image or to extract some information that is required from it. It is a type of signal processing in which input is an image and output may be image or information defining the features associated with that image. Nowadays,  image processing  is amongst the most rapidly growing technologies and is an essential part of machine vision systems. It forms a core research area within the computer science discipline. Related Article:  IMAGE ACQUISITION COMPONENTS Image processing includes the following three steps: Importing the image via image acquisition tools; Analyzing and manipulating the image; Output in which result can be a modified image or a report that is based on the image analysis There are essentially two aspects in the image processing pipeline for it to be carried out successfully. Computational Hardware Image Processing Sof...