Uneven illumination surface defects inspection based on convolutional neural network

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination

A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe

Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation

Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Magnetic NDT Technology for Characterizing Materials – A State of the Art Survey

Per definition, magnetic NDT can be applied at ferromagnetic material only. However, most of the materials we use today still are iron-based steels with bcc lattice and therefore magnetic. The magnetic properties of these materials can be utilized in NDT for defect detection and sizing as well as for materials characterization in terms of mechanical properties determination, also in on-line process-controlled systems. MT is old and one of the most applied NDT techniques in the world for detecting surface-breaking cracks by using magnetic particles. Nowadays the technique can be mechanized and the interpretation of powder indications as findings is performed by intelligent pattern recognition software, i.e. the drawback to be working with a high human factor influence can be eliminated. However, in complex shaped geometries, for instance pusher beams of steering gears in car industry, the existence of pseudoindications prevent the application of MPI. Based on new magnetic sensors, i.e. GMR, an automatic detection with high sensitivity became possible

Development of a guided wave EMAT online inspection system for Al/Al-Sn/Al/steel and CuSn/steel bimetal strip bond quality control used in the automotive industry

Cold roll bonded (CRB) Al/Al-Sn/Al/steel and sintered CuSnNi/steel bimetal strips are used in the automotive industry for the manufacture of engine bearings, bushes and thrust washers. Any defects such as delamination or porosity that occur in bimetal strips during manufacturing can cause problems at downstream production steps and if they remain undetected, could result in components failing in the field, which is a significant business risk.;One way to reduce this business risk is to install a final inspection system on a continuous production line as the strip passes a fixed inspection point. In process control this could alert the operators to reject defective material and correct process parameters when the defect occurs. As this system requires 100% volumetric inspection, installing it has its challenges due to the harsh manufacturing environment in which the strip moves at up to 20 m/min in the processing lines at room temperature.;A literature review and feasibility study on different non-destructive testing (NDT) techniques to inspect bond quality of CRBed Al/Al-Sn/Al/steel bimetal strips was conducted to assess technologies that could be developed for serial inspection. Guided waves generated using Electromagnetic Acoustic Transducers (EMATs) was identified as best suited for this application. Since this technology was not available off-the-shelf, significant research and experimental work was carried out to develop an automated prototype system.;The system was successfully installed at a strip processing line and demonstrated the online bond inspection capability for Al/Al-Sn/Al/steel and CuSnNi/steel bimetal strips, which is the main achievement of this EngD project. For CuSnNi/steel strips, causes of defects and preventative control measures were studied and examined. Industrialisation of the inspection system will significantly reduce the company business risk and improve bond quality of bimetal strips.Cold roll bonded (CRB) Al/Al-Sn/Al/steel and sintered CuSnNi/steel bimetal strips are used in the automotive industry for the manufacture of engine bearings, bushes and thrust washers. Any defects such as delamination or porosity that occur in bimetal strips during manufacturing can cause problems at downstream production steps and if they remain undetected, could result in components failing in the field, which is a significant business risk.;One way to reduce this business risk is to install a final inspection system on a continuous production line as the strip passes a fixed inspection point. In process control this could alert the operators to reject defective material and correct process parameters when the defect occurs. As this system requires 100% volumetric inspection, installing it has its challenges due to the harsh manufacturing environment in which the strip moves at up to 20 m/min in the processing lines at room temperature.;A literature review and feasibility study on different non-destructive testing (NDT) techniques to inspect bond quality of CRBed Al/Al-Sn/Al/steel bimetal strips was conducted to assess technologies that could be developed for serial inspection. Guided waves generated using Electromagnetic Acoustic Transducers (EMATs) was identified as best suited for this application. Since this technology was not available off-the-shelf, significant research and experimental work was carried out to develop an automated prototype system.;The system was successfully installed at a strip processing line and demonstrated the online bond inspection capability for Al/Al-Sn/Al/steel and CuSnNi/steel bimetal strips, which is the main achievement of this EngD project. For CuSnNi/steel strips, causes of defects and preventative control measures were studied and examined. Industrialisation of the inspection system will significantly reduce the company business risk and improve bond quality of bimetal strips