Automatic visual inspection system for microelectronics

A system for automatically inspecting an integrated circuit was developed. A device for shining a scanning narrow light beam at an integrated circuit to be inspected and another light beam at an accepted integrated circuit was included. A pair of photodetectors that receive light reflected from these integrated circuits, and a comparing system compares the outputs of the photodetectors

A systematic algorithm development for image processing feature extraction in automatic visual inspection : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in the Department of Production Technology, Massey University

Image processing techniques applied to modern quality control are described together with the development of feature extraction algorithms for automatic visual inspection. A real-time image processing hardware system already available in the Department of Production Technology is described and has been tested systematically for establishing an optimal threshold function. This systematic testing has been concerned with edge strength and system noise information. With the a priori information of system signal and noise, non-linear threshold functions have been established for real time edge detection. The performance of adaptive thresholding is described and the usefulness of this nonlinear approach is demonstrated from results using machined test samples. Examination and comparisons of thresholding techniques applied to several edge detection operators are presented. It is concluded that, the Roberts' operator with a non-linear thresholding function has the advantages of being simple, fast, accurate and cost effective in automatic visual inspection

Development of outdoor luminescence imaging for drone-based PV array inspection

This work has the goal to perform outdoor defect detection imaging that will be used in a fast, accurate and automatic drone-based survey system for PV power plants. The imaging development focuses on techniques that do not require electrical contact, permitting automatic drone inspections to be perform quicker and with less manpower. The final inspection method will combine several techniques such as, infrared (IR), electroluminescence (EL), photoluminescence (PL), and visual imaging. Solar plant inspection in the future can be restricted only by imaging speed requirements, allowing an entire new perspective in large-scale PV inspection

Visual Inspection System To Detect Connector Tilts In PCBAs [TS156. V844 2005 f rb] [Microfiche 7845].

Sistem pemeriksaan visual automatic memainkan peranan penting dalam bahagian tapisan kualiti di industri eletronik. AVI’s are playing important roles in quality inspection in the electronic industry

Automatic welding of stainless steel tubing

The use of automatic welding for making girth welds in stainless steel tubing was investigated as well as the reduction in fabrication costs resulting from the elimination of radiographic inspection. Test methodology, materials, and techniques are discussed, and data sheets for individual tests are included. Process variables studied include welding amperes, revolutions per minute, and shielding gas flow. Strip chart recordings, as a definitive method of insuring weld quality, are studied. Test results, determined by both radiographic and visual inspection, are presented and indicate that once optimum welding procedures for specific sizes of tubing are established, and the welding machine operations are certified, then the automatic tube welding process produces good quality welds repeatedly, with a high degree of reliability. Revised specifications for welding tubing using the automatic process and weld visual inspection requirements at the Kennedy Space Center are enumerated

Image Analysis and Segmentation Based on the Circular Pipeline Video Processor

Visual inspection of printed circuit boards has generally depended on human inspectors. However, a system has been developed which allows for automated visual inspection using robotics and modern image processing techniques. This paper first introduces automatic visual inspection processes, overviews the Automatic Board Assembly, Inspection and Test (ABAIT) system, reviews image processing concepts and describes the Circular Pipeline Video Processor (CPVP). Image data from the CPVP is analyzed and an investigation into alternate segmentation algorithms to identify circuit board features is presented. The relative performance of these algorithms is compared conclusions drawn

A physics-based defects model and inspection algorithm for automatic visual inspection

AbstractThe representation of physical characteristics is the most essential feature of mathematical models used for the detection of defects in automatic inspection systems. However, the feature of defects and formation of the defect image are not considered enough in traditional algorithms. This paper presents a mathematical model for defect inspection, denoted as the localized defects image model (LDIM), is different because it modeling the features of manual inspection, using a local defect merit function to quantify the cost that a pixel is defective. This function comprises two components: color deviation and color fluctuation. Parameters related to statistical data of the background region of images are also taken into consideration. Test results demonstrate that the model matches the definition of defects, as defined by international industrial standards IPC-A-610D and IPC-A-600G. Furthermore, the proposed approach enhances small defects to improve detection rates. Evaluation using a defects images database returned a 100% defect inspection rate with 0% false detection. Proving that this method could be practically applied in manufacture to quantify inspection standards and minimize false alarms resulting from human error

Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for Automated Structural Condition Assessment in Visual Inspection

Efficiently monitoring the condition of civil infrastructure requires automating the structural condition assessment in visual inspection. This paper proposes an Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automatic structural condition assessment in visual bridge inspection. AECIF-Net can simultaneously parse structural elements and segment surface defects on the elements in inspection images. It integrates two task-specific relearning subnets to extract task-specific features from an overall feature embedding. A co-interactive feature fusion module further captures the spatial correlation and facilitates information sharing between tasks. Experimental results demonstrate that the proposed AECIF-Net outperforms the current state-of-the-art approaches, achieving promising performance with 92.11% mIoU for element segmentation and 87.16% mIoU for corrosion segmentation on the test set of the new benchmark dataset Steel Bridge Condition Inspection Visual (SBCIV). An ablation study verifies the merits of the designs for AECIF-Net, and a case study demonstrates its capability to automate structural condition assessment.Comment: Submitted to Automation in Constructio