Defect Identification System
Inspect Machined Parts for Defects Quickly and Accurately Using Pixuate's Technology
Introduction:One of the aims of industrial machine vision is to develop computer and electronic systems to replace human vision in quality control of industrial production. Web inspection systems are currently used for defect detection and quality control in numerous applications, such as the manufacture of high-tension-cable insulation, paper, plastic bags, strip steel, fuel pellets, chip packaging, wood, cloth, and weaving machines. Automatic inspection systems have numerous advantages over manual inspection. The manual inspection of surface defects is a tedious, if not impossible, task—often because of the small size of many defects and the very large areas to be inspected.
Pixuate® inspection systems consist of a line-scan camera, host computer, frame grabber, and one or more dedicated processing circuit boards. In this article, we discuss the development of a new integrated design environment—intended for real-time defect detection—that eliminates the need for an external frame grabber and eliminates or reduces the need for other associated host computer peripheral systems. Usually defects are found either by preplanned activities specifically intended to uncover defects (e.g., quality control activities such as inspections, testing, etc.) or by accident (e.g., users in production).
Techniques to find defects can be divided into three categories:
- Static techniques: Testing that is done without physically executing a program or system. A code review is an example of a static testing technique.
- Dynamic techniques: Testing in which system components are physically executed to identify defects. Execution of test cases is an example of a dynamic testing technique.
- Operational techniques: An operational system produces a deliverable containing a defect found by users, customers, or control personnel — i.e., the defect is found as a result of a failure.
Features:Pixuate® Defect Detection Systems overcomes all the failures due to defects.
- Provides a centralized repository for tracking defects across projects.
- Provides automated notifications of resource assignments.
- Ability to define defect resolution status in order to map back to your defect management process.
- Ability to provide management reporting, like the number of open defects grouped by various criteria such as open defects by project, severity, and priority.
- Ability to capture other items in addition to defects, such as customer suggestions and project-related issues. Items such as customer complaints or enhancement suggestions are often lost if not logged in a centralized system. If other tools are not already available, a defect management tool can be used to track these types of items as long as they can easily be filtered out or logically separated from defects.
- Ability to support internal and external teams. This feature provides the opportunity to involve external teams and in some situations, customers if appropriate.
How It Works?
The Pixuate® image processing is especially used to improve the image quality, making the analysis process easier, which consists of detecting and classifying defects on the film. In the conventional method, the analysis is done exclusively by the X-ray inspector. The progresses in computer science and the artificial intelligence techniques have allowed the defect classification to be carried out by using pattern recognition tools, especially the methods that use artificial neural networks, which make the process automatic and more reliable, as it is not a subjective analysis. Pattern recognition are tasks routinely performed by humans. When someone who is driving a car looks at a traffic light, he/she is able to distinguish the information easily and start the proper procedure afterwards. When we observe a photograph, we can identify precisely the people in the photo even if we have seen them before for just a few times, although the people in the photo may wear different clothes, have different hair styles and be in different positions. These tasks, as many others, are simple for humans, but implementing them in a computer system is extremely complex.
The techniques for pattern recognition implemented on a digital image involve, mainly, the location and isolation of the objects (defects) on the image, and later, the defect identification (classification). The pattern recognition assumes that the image may have one or more defects, and that each defect belongs to a preset type, category or pattern class of defects. If a digitized image has several defects, the process for pattern recognition consists of three main steps.
The first step is the image quality improvement or the defect isolation, in which each defect is located and isolated from the rest of the image. Several types of degradations and distortions, inherent in the processes of image acquisition, transmission and display contribute to limit the capacity to extract information. The main purpose of this image quality improvement step is to suppress or reduce the consequences of these barriers, making it easy to extract the relevant information. This improvement is usually done with the application of image digital filters.
The second step is the extraction of defect features, that is, where the defects are measured. One measurement is the value of any sizeable property of defect. A feature is a function of one or more measurements, which are registered in the computer to dimension any significant characteristic of the defect. This drastic reduction in the amount of information (if compared to the original image) represents all the knowledge on which the subsequent process of classification shall be based. Therefore, the fewer features existing to represent a certain class, the fewer information will be processed. For example, if an intelligent system intends to classify banana and orange, only one feature of form is necessary to classify these two classes. However, the classes are not always so easily separated. The use of features of defects is one of the mostly used techniques to classify the welding defects after their detection. In this case, the correct selection of the most relevant features in the identification of each class has a great importance in the recognition of such classes done by the intelligent system8. This selection is similar to the interpretation given by an inspector who, in most cases, recognizes first one type of welding defect in the radiography by visual characteristics, such as: location, shape, length, density (gray level), aspect ratio, etc., besides the welding conditions. So, an important study of the defect morphology at the image level is required to optimize the system performance.
The third step of the pattern recognition process is the classification. This output is merely a decision considering the class that each defect belongs to. Each defect is considered to be an individual class, and the recognition is implemented as a process of classification. Each defect is directed to one of the several preset classes, which represent all possible types of defects expected to exist in the image.
Advantages of Pixuate® Defect Identification System
- Reduces Fix and Remediation Cost
- Reduces Overall Spend
- Increased Manufacturing and QA Staff Productivity
- Reduces Business Risk Due to Outages
- Improves Security and Overall Manufacturing Quality
- Detect Defects Earlier
- Fulfill Architecture Standards
- Improve Manufacturing Quality
- Minimize Complexity
- Verifying Manufacturing Practices
- Lower Technical Debt
The Ideal SolutionThe Pixuate® Manufacturing Defect Identification System is ideal for all businesses who are into any form of manufacturing:
- Semiconductor Fabrication
- PCB manufacturing units
- Water pipe line systems
- Dam monitoring systems