Machine Vision Companies Enabling Improving Quality Metrics In PCB Industry

AI image processing in a week Table Of Contents 1.  Image Data Collection 2. Data Preparation 3. Training and Deployment 4. Fine Tuning Machine vision technology has been dealing with automated task inspection processes like defect detection, flaw analysis, sorting, counting, and assembly verification in industrial settings. The recent discoveries in computer vision software have enhanced the capabilities of imaging systems in new and innovative ways. With a well-designed and properly installed vision system, the software can reliably detect defects. They can also improve the efficiency, throughput, and revenue of an organization. Machine vision systems work using an artificial intelligence tool called deep learning, which trains objects using examples. This example-based training is the fastest and the most efficient way of building a machine vision model. Steps to build a deep learning model in a week –  Image Data Collection – The data collection process is a crucial step ...

Introduction to Manufacturing Ops In simple words, a manufacturing operation is a process in which materials are changed, converted, or transformed onto a new state or form. Manufacturing Ops deals with the smooth functioning of such processes. In today’s fiercely competitive space, where supply outstrips demand for a lot of products, an enterprise’s manufacturing segment can prove to be a significant differentiating factor. Ensuring that you have effective processes in place across the manufacturing operations can enhance your organization’s ability to satisfy consumer demand, release products into the market, and your ability to improve your manufacturing operations. But how can we do that? Let’s find out. Stages of Manufacturing Ops Manufacturing operations can be segmented into the following: Production planning Production planning works closely with the procurement and material management functions. It ensures that the raw materials and other perquisites are prepared for the produ...

Introduction to Toyota Production System Toyota production system (TPS) is a lean manufacturing system, created by Taiichi Ohno. It focuses on the absolute elimination of waste, cost reduction, and producing high-quality products. TPS is implemented in industries for the following reasons: It helps in monitoring quantity control to reduce costs by eliminating waste.  It enhances process and product quality. Elements of Toyota production system Just in time is a technique of supplying exactly the right quantity, at exactly the right time, and at the exact location.  Jidoka is about building quality into the process. It uses tactics like poka-yoke, 5 whys, kaizens, and continuous improvement processes to improve quality. Machine vision and artificial intelligence solutions add value to jidoka tactics. Machine Vision And Artificial Intelligence Machine vision is the technology and methods used to provide image-based automatic inspection for industries. It uses a camera or multipl...

Counting Small Pieces With AI Based Vision System Qualitas with the use of EagleEye®, has developed an application for counting small parts. This solution takes advantage of Deep Learning and Machine Learning to train the software to look for specific parts. The advantage can be observed by how it’s able to identify the parts in spite of various backgrounds as well as varying lighting conditions. This solution can be used for counting small metallic, machined parts such as jewelry or pharmaceutical products, natural products such as coffee and many more. With this solution, human errors can be eliminated and the counting process can be automated and thus made much faster and error free. 

Table Of Contents 1.  Imaging Complex Geometries 2. Challenging Surfaces 3. A Large Number of Variants 4.  Complex Material Handeling 5.  Working Distance Constrains 6.  High Resolution Necessities 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...

Table Of Contents 1. Problem Scenario 2. Challenges and concern 3. Solution 4. Results Quality control is a critical element in the automotive manufacturing industry supply chain with visual part inspection being a heavily relied upon activity in the process. This task has been traditionally carried out by highly experienced and qualified staff. However, the inherent nature of visual inspection and difference in perception between individuals, make defect detection subjective. These inconsistencies have an adverse impact on quality and lower the overall productivity of the plant. One of our clients, a leading car manufacturer, approached us to find a solution to a similar problem. The application uses Machine Vision in the Paint Shop for Inspecting primer on the coat. Problem Scenario In the given scenario, one car passed through the painting station every 30 seconds during the primer coat application process at the paint shop. As the robot applicator deployed for the task performe...