Automated optical inspection system for professional double face printed circuit boards (original) (raw)

Knowledge-based systems as an aid to computer-aided repair

Microprocessors and Microsystems, 1989

Prinetto* present an AI approach to improving test/repair productivity The paper introduces AI techniques in the field of computer-aided testing (CAT) and computer-aided repair (CAR). It is increasingly important to improve the diagnostic capabilities of automatic test equipment (ATE) and AI can provide suitable solutions. The overall goal is to set up test/ repair environments which guarantee not only high diagnostic capability, but also high productivity. The system presented is based on the loop tester--~ repair station -+ tester. In a conventional approach, data from a repair station are not used to improve the performances of the tester. In the solution described here, data are first validated by the tester and then successively processed to gather new knowledge about real cases. Such knowledge is learned by the tester to improve its symptom interpretation capabilities. Two methodological aspects are relevant to this approach: the knowledge the tester receives from the repair station and the knowledge the tester itself needs to learn from experience. It is thus necessary to organize knowledge in knowledge bases and to use it with a proper reasoning mechanism. AI techniques are used to achieve both goals: on the one hand they allow the problem to be formalized in a rigorous way, and on the other hand they facilitate the generation of an open system in which the introduction of new diagnostic rules is easy and where a feedback from the manufacturing test environment is possible. Thus, AI improves diagnostic capabilities in two ways: it helps the ATE to overcome symptom ambiguity and to learn from experience how to modify the fault location procedures.

An automated system for design-rule-based visual inspection of printed circuit boards

Proceedings. 1991 IEEE International Conference on Robotics and Automation, 1991

In this paper, the design and the implementation of an automated, design-rule-based, visual priiited circuit board (PCB) inspection system are presented. The developed system employs mathematical morphology based image processing a1gorith.m.s. This system detects PCB defects related to the conducting structures on PCBs b y checking a set of geometric design rules. For th,is purpose, a new image segmentation algorithm and a new defect detection algorithm are designed. The defect detection algorithm is capable of verifying the minimum conductor spacing, minimum conductor trace width, and the minimum land width requirements on digital binary PCB images. Also, an already existing defect detection algorithm is modijed for its implementation in our system. A prototype system is implemented in our image processing laboratory.

KIMS—A knowledge-based computer vision system for production line inspection

Computers & Industrial Engineering, 1989

the aim of this paper.is to discuss a generic expert system prototyped for image analysis and interpretation tasks. The system christened KIMS is a knowledge-based image management system which oversees the entire process of image processing, segmentation, feature extraction, knowledge representation along with an expandable capability for image understanding task. We discuss the architecture of KIMS, its modeling environment, and demonstrate its usefulness with photo-to-image inspection as applied to a manufacturing line.

ARDIS: Knowledge-Based Dynamic Architecture for Real-Time Surface Visual Inspection

Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira’s Scientific Legacy, 2009

This work presents an approach to surface dynamic inspection in laminated materials based on the configuration of a visual system to obtain a good quality control of the manufacturing surface. The configuration task for surface inspection is solved as a Configuration-Design task following the CommonKADS methodology, which supports the proposed knowledge-based dynamic architecture (ARDIS).

A PRINTED CIRCUIT BOARD INSPECTION SYSTEM WITH DEFECT CLASSIFICATION CAPABILITY

ABSTRACT. An automated visual printed circuit board (PCB) inspection is an approach used to counter difficulties occurred in human's manual inspection that can eliminates subjective aspects and then provides fast, quantitative, and dimensional assessments. In this study, referential approach has been implemented on template and defective PCB images to detectnumerous defects on bare PCBs before etching process, since etching usually contributes most destructive defects found on PCBs.

A knowledge-based inspection workstation

Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No.PR00446), 1999

We are building an inspection workstation development environment to use as a testbed for understanding what types of knowledge, e.g., data, algorithms, and processes, can increase the productivity of inspection operations. Inspection can be more efficient through reducing the need for fixturing, integrating the generation of process plans and their execution within the controller, and reducing the errors or data losses that occur by translating the models to different formats.

Automated inspection of printed circuit boards through machine vision

Computers in Industry, 1996

This paper introduces the development of an automated visual inspection system for printed circuit boards (PCBs). It utilizes an elimination-subtraction method which directly subtracts the template image from the inspected image, and then conducts an elimination procedure to locate defects in the PCB. Each detected defect is subsequently classified into one of the seven defect types hy three indices: the type of object detected, the difference in object numbers, and the difference in background numbers between the inspected image and the template. Finally, a 256 X 240 PCB image was tested to show the effectiveness of this system.

Neural network diagnosis for visual inspection in printed circuit boards

In this paper we present an Automatic Optical Inspection system to diagnose Printed Circuit Boards mounted in Surface Mounting Technology. The diagnosis task is handled as a classification problem with a neural network approach. The Printed Circuit Board tested images are preprocessed by means of several methods to reduce the amount of data to feed to the neural networks. We compare the results obtained in the diagnosis for all methods. The Automatic Optical Inspection system seems to be a good solution in an industrial application because of the low cost, very fast diagnosis and easiness to set-up and handle.

2-Automated inspection of printed circuit boards through machine vision

Development of a knowledge base for the planning of prismatic parts inspection on CMM

Acta IMEKO, 2015

Inspection on coordinate measuring machines (CMMs) is based on software support for various classes of metrological tasks, i.e. tolerances. Today, the design of a uniform inspection plan for a measuring part presents a rather complex issue due to the following: (i) metrological complexity of a measuring part; (ii) skills and knowledge of a designer / inspection planner; and (iii) software for CAI model, considered as a part of an integrated CAD-CAPP-CAM-CAI system. This issue could be addressed by the usage of expert systems that generate a conceptual inspection plan for a measuring part, based on which the inspection plan for a selected CMM could be automatically developed. This paper presents the development of a model of an automatic inspection planning system for CMMs, and, in particular, the developed knowledge base model.

Automated optical inspection system for professional double face printed circuit boards (original) (raw)

Related papers