Plenoptic cameras can capture 3D information in one exposure without the need for structured illumination, allowing grey scale depth maps of the captured image to be created. The Lytro, a consumer grade plenoptic camera, provides a cost effective method of measuring depth of multiple objects under controlled lightning conditions. In this research, camera control variables, environmental sensitivity, image distortion characteristics, and the effective working range of two Lytro first generation cameras were evaluated. In addition, a calibration process has been created, for the Lytro cameras, to deliver three dimensional output depth maps represented in SI units (metre). The novel results show depth accuracy and repeatability of +10.0 mm to-20.0 mm, and 0.5 mm respectively. For the lateral X and Y coordinates, the accuracy was +1.56 m to −2.59 m and the repeatability was 0.25 µm.
Abstract:Automatic surface defect inspection within mass production of high-precision components is growing in demand and requires better measurement and automated analysis systems. Many manufacturing industries may reject manufactured parts that exhibit even minor defects, because a defect might result in an operational failure at a later stage. Defect quantification (depth, area and volume) is a key element in quality assurance in order to determine the pass or failure criterion of manufactured parts. Existing human visual analysis of surface defects is qualitative and subjective to varying interpretation. Non-contact and three dimensional (3D) analyses should provide a robust and systematic quantitative approach for defect analysis. Various 3D measuring instruments generate point cloud data as an output, although they work on different physical principles. Instrument's native software processing of point cloud data is often subject to issues of repeatability and may be non-traceable causing significant concern with data confidence.This work reports the development of novel traceable surface defect artefacts produced using the Rockwell hardness test equipment on flat metal plate, and the development of a novel, traceable, repeatable, mathematical solution for automatic defect detection and quantification in 3D. Moreover, in order to build-up the confidence in automatic defect analysis system and generated data, mathematical simulated defect artefacts (soft-artefact) have been created. This is then extended to a surface defect on a piston crown that is measured and quantified using a parallel optical coherence tomography instrument integrated with 6 axis robot. The results show that surface defect quantification using implemented solution is efficient, robust and more repeatable than current alternative approaches.
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