Efficient completeness inspection using real-time 3D color reconstruction with a dual-laser triangulation system

In this chapter, we present the final system resulting from the European Project \u201d3DComplete\u201d aimed at creating a low-cost and flexible quality inspection system capable of capturing 2.5D color data for completeness inspection. The system uses a single color camera to capture at the same time 3D data with laser triangulation and color texture with a special projector of a narrow line of white light, which are then combined into a color 2.5D model in real-time. Many examples of completeness inspection tasks are reported which are extremely difficult to analyze with state-of-the-art 2D-based methods. Our system has been integrated into a real production environment, showing that completeness inspection incorporating 3D technology can be readily achieved in a short time at low costs

Automated PCB identification and defect-detection system (APIDS)

Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs

Automatic Defect Detection System For Leadframe Inspection

Defect detection and classification are important for both product quality assurance and process improvement in the maimfacturing industry. Machine vision systems offer several beneficial. features such as consistency, accuracy and round the clock repeatability. This thesis presents the results of the development and implementation of such a machine vision system to automate the inspection of leadframes. Pengesanan dan mengklasifikasikan kecacatan adalah penting untuk memastikan kualiti produk dan meningkatkan kebolehan sesuatu proses dalam industri pembuatan. Sistem penglihatan mesin menawarkan beberapa kelebihan dalam perkara seperti konsisten, kejituan dan pemeriksaan berterusan. Disertasi ini mempersembahkan keputusan dalam membangun dan implementasi sistem penglihatan mesin untuk pemeriksaan secara bagi 'leadframe'

A Method for Pose and Type Verification of Resistor

AbstractThis paper proposes a method for verifying the pose and the type of different resistors mounted on a PCB. First, the pose of the resistor on the PCB is determined and missing resistors are detected by shape_based template matching. Then, the type of the resistor is extracted and compared to the known reference type by edge_based template matching. Finally, six types of resistors have been verified on 120 resistor images. Experiments have shown that the shape_based template can be used to determine the pose of the resistor even if it appears rotated and scaled. The proposed method can achieve the accuracy of 100% and average recognition time of 0.15s

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

Modelling and simulation of paradigms for printed circuit board assembly to support the UK's competency in high reliability electronics

The fundamental requirement of the research reported within this thesis is the provision of physical models to enable model based simulation of mainstream printed circuit assembly (PCA) process discrete events for use within to-be-developed (or under development) software tools which codify cause & effects knowledge for use in product and process design optimisation. To support a national competitive advantage in high reliability electronics UK based producers of aircraft electronic subsystems require advanced simulation tools which offer model based guidance. In turn, maximization of manufacturability and minimization of uncontrolled rework must therefore enhance inservice sustainability for ‘power-by-the-hour’ commercial aircraft operation business models. [Continues.