Automatic surface defect quantification in 3D

Three-dimensional (3D) non-contact optical methods for surface inspection are of significant interest to many industrial sectors. Many aspects of manufacturing processes have become fully automated resulting in high production volumes. However, this is not necessarily the case for surface defect inspection. Existing human visual analysis of surface defects is qualitative and subject to varying interpretation. Automated 3D non-contact analysis should provide a robust and systematic quantitative approach. However, different 3D optical measurement technologies use different physical principles, interact with surfaces and defects in diverse ways, leading to variation in measurement data. Instrument s native software processing of the data may be non-traceable in nature, leading to significant uncertainty about data quantisation. Sub-millimetric level surface defect artefacts have been created using Rockwell and Vickers hardness testing equipment on various substrates. Four different non-contact surface measurement instruments (Alicona InfiniteFocus G4, Zygo NewView 5000, GFM MikroCAD Lite and Heliotis H3) have been utilized to measure different defect artefacts. The four different 3D optical instruments are evaluated by calibrated step-height created using slipgauges and reference defect artefacts. The experimental results are compared to select the most suitable instrument capable of measuring surface defects in robust manner. This research has identified a need for an automatic tool to quantify surface defect and thus a mathematical solution has been implemented for automatic defect detection and quantification (depth, area and volume) in 3D. A simulated defect softgauge with a known geometry has been developed in order to verify the implemented algorithm and provide mathematical traceability. The implemented algorithm has been identified as a traceable, highly repeatable, and high speed solution to quantify surface defect in 3D. Various industrial components with suspicious features and solder joints on PCB are measured and quantified in order to demonstrate applicability

An intelligent and confident system for automatic surface defect quantification in 3D

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

The suitability of lightfield camera depth maps for coordinate measurement applications

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

Developing confidence in automatic on-line quantification of surface defects

Developing confidence in automatic on-line quantification of surface defect

Investigation of relationship between interfacial electroadhesive force and surface texture

A novel investigation into the relationship between the obtainable interfacial electroadhesive forces and different surface textures is presented in this paper. Different surface textures were generated then characterized based on a recognized areal-based non-contact surface texture measurement platform and procedure. An advanced electroadhesive force measurement platform and procedure were then implemented to measure the obtainable electroadhesive forces on those different surface textures. The results show that the obtained interfacial electroadhesive forces increase with decreasing Sq (root mean square height) value of the substrate surface provided that the difference in Sq between the different substrates is over 5 μm. Also, the higher the applied voltage, the larger the relative increase in electroadhesive forces observed. However, when the difference of Sq value between different substrate surfaces is below 2 μm, the obtained interfacial electroadhesive forces do not necessarily increase with decreasing Sq. Furthermore, the obtainable electroadhesive forces are not necessarily the same when the Sq value of two substrate surfaces are the same due to the fact that the direction of the surface texture plays an important role in achieving electroadhesive forces

Real-time surface defect detection and traceable measurement of defect volume in 3D

In recent years, there has been an increased emphasis for quality control in the manufacturing sector. Many manufacturing processes have become fully automated resulting in high production volumes. However, this is not necessarily the case for inspection of aerospace surface defects. Volume measurement of defects is one of the key elements in quality assurance in order to determine the pass or failure of certain manufactured parts within this industrial sector. Existing human visual analysis of surface defects is qualitative and subject to varying interpretation. Noncontact 3D measurement should provide a robust and systematic quantitative approach for surface defect analysis. Instrument native software processing of 3D data is often subject to issues of repeatability and may be non-traceable in nature, leading to significant uncertainty about data quantisation and representation. This is compounded by a lack of traceable surface defect standards and softgauges with which to test the instruments and software respectively. This research is concerned with the development of novel traceable sub-millimetric surface defects produced using a Rockwell hardness test instrument on flat, single curvature (SC), and double curvature (DC) metal plates, and the development of a novel robust, repeatable, mathematical solution for automatic defect detection and characterization. This is then extended to a surface defect on an aerofoil that is measured in real-time and characterized using the novel algorithm. The results show that the new surface defect detection and quantification is more robust, efficient, and repeatable than existing solutions