Automation of Visual Inspection Using Image Processing

International Journal of Engineering Research and, 2020

Product manufacturing industries produce machine parts in vast quantities. Quality assurance and control of these parts is necessary to maintain the standard defined by industry. Traditional methods of inspection using quality control inspector leads to inconsistency and imprecise decisions. Thus, Current manual inspection procedure is erroneous. The purpose of this research is to overcome the flaws in the manual inspection using automated visual inspection system. Collecting data for processing is done with the help of Image acquisition system, which captures image of the part on production line. This captured image is pre-processed and feature extraction is performed resulting in generation of feature vectors. Using machine learning model for defect classification, we train ML algorithm with these feature vectors to classify the defects found during the inspection. defect detection and other quality control actions are performed based on the reference part. The detection of defects at an early stage helps in increasing quality control factor and smoothing the process of production. automated visual inspection system saves time and is efficient. This survey definitively answers the question regarding the defect detection of machine parts.

Rev. Téc. Ing. Univ. Zulia., 2016

Assembly line automation gets more significance to cope up with increasing needs of latest technology machines which are used in industry and society. This paper presents a computationally efficient 2D computer vision based approach to recognize the machine parts and detect damaged parts on the assembly line. The image acquisition system which is part of the assembly line setup acquires data from the moving machine parts in line. Captured machine part image data undergoes image preprocessing techniques like background subtraction, binarization, scaling, and noise and holes removal to transform the data suitable for further processing. Then a contour of the machine parts are extracted and normalized by equal part area method to describe the shape. It gives important clues for machine part shape recognition and defect identification. For experimental purpose a model shape for each machine part is developed, the shape recognition and defect detection are performed with only reference to the model shape. The defects in the machine part such as damage, cracks are identified by the similarity measure between model shape and the data extracted from machine part of the assembly line. The detection and identification of defects at the early stage will helps smooth production process, saves production cost and time.

Computers in Industry, 2005

This paper presents a neural network-based vision inspection system interfaced with a robot to detect and report IC lead defects on-line. The vision system consists of custom software that contains a neural network database for each of the ICs to be inspected on a PCB. The vision system uses gray scale images and a single layer neural network with three outputs based on defect criteria. Each IC has a different inspection area, thus, the input vector varies for each of the ICs. The IC networks were trained with Matlab's Bayesian regularization module. Performance of each of the networks investigated was found to be 100% based on the defect criteria. This system has been implemented and tested on several electronic products using ProE, C++ and OpenGL software platforms [R. Balderas, S. Bose, Automated robotic inspection system for electronic manufacturing, MSE Thesis, Manufacturing Engineering Department, UT-Pan American, 2002; A.I. Edinbarough, J. Amieva, Experimental study on the robotics vision inspection of electronic components, BS Thesis, Engineering Technology Department, UT-Brownsville, 2002].

Computer vision systems are widely implemented in automatic inspection systems. The quality management of mechanical parts in industries is vital for proper functioning of machineries. Defect detection should be done in pre-production stage ensuring quality control. Real time inspection using manual labour is inadequate, time consuming and non-consistent. Hence there is a need for a system which is built for automatic defect detection, such that it avoids human errors and is comparatively accurate. The system builds a computer vision system which detects the defective objects and segregates it. This paper makes use of an overhead camera mounted at specified height over a conveyor belt, which sends recorded images to the Raspberry Pi. Pattern recognition is performed using Open CV to identify the defective objects moving over a conveyor belt. It identifies defective number of teeth in gears and surface abrasions in metal sheets and thereby helps in quality management.

Sensors

This paper addresses the problem of automatic quality inspection in assembly processes by discussing the design of a computer vision system realized by means of a heterogeneous multiprocessor system-on-chip. Such an approach was applied to a real catalytic converter assembly process, to detect planar, translational, and rotational shifts of the flanges welded on the central body. The manufacturing line imposed tight time and room constraints. The image processing method and the features extraction algorithm, based on a specific geometrical model, are described and validated. The algorithm was developed to be highly modular, thus suitable to be implemented by adopting a hardware–software co-design strategy. The most timing consuming computational steps were identified and then implemented by dedicated hardware accelerators. The entire system was implemented on a Xilinx Zynq heterogeneous system-on-chip by using a hardware–software (HW–SW) co-design approach. The system is able to det...

Advanced Engineering Forum, 2011

This paper briefly introduce the concept and characteristic and form of machine vision inspecting system, elaborate its application in industry, the present status and development prospects of the application in geometry measurement are described in detail, and deal with emphatically its critical technical points and the corresponding solutions. 1. People will be freed from repeated and tedious work. 2. Reduced labor costs. 3. Put up productivity and detection accuracy. 4. To achieve flexible manufacturing. Based on a lot of technical literature both abroad and domestic, this paper makes a brief description of machine vision systems and mainly discussed the detection of machine vision in industrial research and application.

The Mechanical members are subjected to static and dynamic loads during their service period. Sometimes these members fail before their expected service life. Some of the major causes are "crack development" and "misalignments of the members". The "improper assembly"

Automated inspection systems is the target for all automated organizations. The objective zero defect considers as a challenge for many industries since there are many factors effect on production line. Increase scrape items effect on productivity and environment; rework produced items as bottle neck for production lines and decrease products rates. The objectives for the research project focused on four main concerning, Evaluate automated inspection and control system in manufactures. Redesign online inspection system for some industrial case studies for the purpose of enhancing Quality control, tracking quality control in manufacturing systems and embedded improving computer vision systems in the production lines levels, and reduce defect items by correct parameters during the production lines. Research project focused on some case studies like (plastic, hot stamping, assembly, and textile) industries in Malaysia and Iraq. Computer vision systems was the common methods since it considers as non-destruction testing system. One of the machine vision systems techniques is image processing technique. Image processing algorithm implement by using MATLAB and Simulink. The developed points in this research focused on interpret defects and signal feedback for correcting deviations in the setting parameter for the fabrication machines. This system will help manufacturers to understand faults for their products online during fabrication route. Three main functions were using feature matching, color recognition and orientation and recognize the object functions. The results for this system showed that the ability for the system to know the weak points in the produced items and the production systems and accurate them with keeping on the stability for the automated system. Growth in information technology and cameras will improve system capabilities in different fields and adaptable for heavy environments.

Комп’ютерно-інтегровані технології автоматизації технологічних процесів на транспорті та у виробництві : матеріали всеукр. наук.-практ. конф. здобувачів вищ. освіти і молодих учених, 20 листоп. 2024 р., 2024

The work discusses the integration of computer vision technologies into modern production processes, focusing on their role in automating inspection and quality control. By leveraging high-resolution cameras, lighting systems, and advanced image processing algorithms, these systems ensure consistent product quality, reduce manual labor, and minimize the impact of human error. Key functionalities include surface defect detection (e.g., scratches, cracks), dimensional accuracy measurement, assembly verification, and shape analysis. Advanced methods like 3D scanning and machine learning are highlighted as pivotal for detecting defects with high precision and adapting to dynamic production environments. Additionally, the integration with Big Data and manufacturing solutions (e.g., ERP and MES) facilitates efficient quality data management. The work emphasizes that computer vision systems enhance production efficiency, cost-effectiveness, and product reliability, making them indispensable tools in modern manufacturing. It concludes by noting the technology's growing importance for professionals in automation and robotics, offering vast opportunities for innovation and research.

Soft Computing Research Society eBooks, 2023

The perfect Printed Circuit Board (PCB) plays a very important role in every electronic device as well as in automation systems. So, it is very important to find defects in the PCB before installing it to any system or any device. However, PCB Manufacturers use various inspection systems in the process of manufacturing PCBs for detecting various types of defects in the PCB. In this article, we present the Automated assembled PCB Inspection System. This system finds defects such as missing components and improper position of its components by using the Pattern matching Technique where a good known score of template image is matched with the score of the test image. This system gives results at each inspection within 10 Seconds and the result given by this system are passed or fail in the form of an array sheet. This automated inspection system is created by using NI Vision Builder AI and NI LabVIEW technology. Ni Vision Builder AI has been used to create the algorithm. And NI LabVIEW has been used to create the application.