Parallel machine vision for the inspection of surface mount electronic assemblies

The aim of this thesis is to analyse and evaluate some of the problems associated with developing a parallel machine vision system applied to the problem of inspection of surface mount electronic assemblies. In particular it analyses the problems associated with 2-D feature and shape extraction. Surface Mount Technology is increasingly being used for manufacturing electronic circuit boards because of its light weight and compactness allowing the use of high pin count packages and greater component density. However with this comes significant problems with regards inspection, especially the inspection of solder joints. Existing inspection systems are either prohibitively expensive for most manufacturers and/or have limited functionality. Consequently a low cost architecture for automated inspection is proposed that would consist of sophisticated machine vision software, running on a fast computing platform, that captures images from a simple optical system. This thesis addresses a specific part of this overall architecture, namely the machine vision software required for 2-D feature and shape extraction. Six stages are identified in 2-D feature and shape extraction: Canny Edge Detection, Hysteresis Thresholding, Linking, Dropout Correction, Shape Description and Shape Abstraction. To evaluate the performance of each stage, each is fully implemented and tested on examples of synthetic data and real data from the inspection problem. After Canny Edge Detection, significant edge points are isolated using Hysteresis Thresholding which determines which edge points are important based on thresholds and connectivity. Edge points on their own do not describe a boundary of an object. A linking algorithm is developed in this thesis which groups edge points to describe the outline of a shape. A process of dropout correction is developed to overcome the problem of missing edge points after Canny and Hysteresis. Connected edges are converted to a more abstract form which facilitates recognition. Shape abstraction: is required to remove minor details on a boundary without removing significant points of interest to extract the underlying shape. Finally these stages are integrated into a demonstrator system. 2-D feature and shape extraction is computationally expensive so a parallel processing system based on a network of transputers is used. Transputers can provide the necessary computational power at a relatively low cost. The 2-D feature and shape extraction software is then required to run in parallel so a distributed form of shape extraction is proposed. This minimises communication overheads and maximises processor usage which increases execution speed. For this, a generic method for routing data around a transputer network, called Spatial Routing, is proposed