Papers by Keyword: Surface Inspection

Abstract: Cost-optimized materials and processes are the key to high-performance components at attractive production costs. This study shows that non crimp fabrics (NCF) used as inner layers of high performance Class-A cfrp parts can lead to unwanted print-through effects on Class-A composite surfaces, even though they are not the surface layer. This surface distortion that is expressed in scattered lines in the direction of inner NCF layers can lead to high reject rates and is normally first noticed in the painted state. The presented methodology is able to quantify this secondary print-through effect for cured composites as well as for the dry textile intermediates. The surface can be measured with conventional measuring techniques, such as laser triangulation or interferometers, and characterized with the standardized values Sz and Sz25. The results show that the visible and measurable more uneven surface of a 50k biaxial NCF leads to significantly higher Sz and Sz25 values in the dry textile and the cured component. Also the regularity of the measured textiles can be detected by determining the variation of different areas of a 800 by 800mm sized sample. The presented methodology has the potential to optimize the incoming goods inspection of high volume Class-A composite manufacturers, as well as the requirement-orientated and cost-efficient development of textile intermediates by suppliers.

Abstract: Using Conti-M® which is a globally unique technology for producing cast-rolled copper strips, MKM has been able to link and optimise the continuous casting and rolling process together with the surface quality of milled strips. The last 15 years have seen many targeted projects for process, product and quality development. In this article, the process related challenges to optimise the surface quality of casting strip will be highlighted. We will present a process control system and the impact it had in the optimisation of the surface quality.

Abstract: Surface texture assessment is very useful in predicting the functional behaviour of engineering components. Surface texture is composed of three elements-roughness, waviness and form Error. The proposed method analyzes surface texture in two ways-Subjective analysis and Objective analysis. Subjective analysis makes use of histogram and texture spectrum whereas objective analysis uses Grey Level Co-occurrence Matrix (GLCM) based standard texture descriptors. Different milled surfaces having different textures are prepared by varying the machining parameters. The proposed method is non-contact in nature and high measuring speeds are possible. The method provides a complete texture description for a given surface.

Abstract: The paper presents the possibilities of a hybrid vision method based on simultaneous analysis of infrared and vision images for surface inspection of hot aluminium in a manufacturing process. The system consists of a NIR/SWIR camera and a high resolution visual camera, and a computer based image analysis system. The simultaneous analysis of infrared and vision images will enable surface inspection for detecting defects in temperature range from 200°C to 600°C. Thermal images present temperature distribution on the surface, and contain information about the manufacturing process. The analysis of thermograms enables to find areas of temperature irregularity caused by increased friction loads, as well as areas of inhomogeneous emissivity caused by surface defects. Furthermore, information captured by the vision camera is used to detect surface defects. The software developed enables the overlaying of images. The proposed simultaneous thermovision and vision imaging can be applied in industry for in-line monitoring of aluminium extrusion processes.

Abstract: Because steel strips are covered with scales and water during hot-rolling, it is difficult to recognize the defects from images of hot-rolled strips through conventional methods. Principles and characteristics of fractal dimensions were studied, and computation of the fractal dimensions of the defect images with Peleg Covered Carpet is presented. Fractal dimensions of piecewise linearly transformed and smoothed images were used as features for classification of defects. These features were inputted to train the AdaBoost classifier. Experiments with samples of pimples, shells and scales from a real surface inspection system of hot-rolled strips showed that it is effective to recognized scales from other defects, and the total classification rate of this method is higher than 90%.

Abstract: In this paper, it is proposed a new method of free-from surface’s reverse engineering, making data acquisition and surface reconstruction form closed loop system, solving no feedback problems in the measuring and modeling process, shortening the time of the whole reverse engineering, improving the quality of reconstructed models. The core of this paper is used the CMM adaptive measuring method and non-uniform b-spline surface reconstruction method, integrating the free-from surface measuring and modeling in a closed loop system, realizing the CMM real-time online measurement and reconstructed surface real-time update.

Abstract: This paper is concerned with the problem of automatic inspection of hot-rolled plate surface using computer vision. An automated visual inspection system has been developed to take images of external hot-rolled plate surfaces and an intelligent surface defect detection paradigm based on gradient spectrum technique is presented. Gradient spectrum characterizes the spatial configuration of local image texture and is robust against any monotonic transformation of the gray scale. Texture features based on gradient spectrum are extracted from ROI in hot-rolled plate surface images and integrated into a feature vector which uniquely differentiates the abnormal regions from normal surface. Classification accuracies using the gradient spectrum and gradient-based method are compared. The results indicate that gradient spectrum performs well in classifying the samples with the lowest classification error.

Abstract: This paper is concerned with the problem of automatic inspection of hot-rolled plate surface using machine vision. An intelligent surface defect detection paradigm based on texture analysis and neural network is presented. Texture features based on GLCM, Laws energy, and LBP are extracted from ROI in hot-rolled plate surface images. These features are integrated into a feature vector which uniquely differentiates the abnormal regions from normal surface. A radial basis function network is used for classification of ROI as normal or abnormal. Classification accuracies using the individual feature sets and the integrated features are compared. The results indicate that the integrated features improve the accuracy of detection. Empirical results show the integrated features from GLCM and LBP perform well in classifying the samples with the lowest classification error.

Abstract: In order to reduce and control yield loss in the fabrication process of next generation ULSI devices, nano-defects inspection technique for polished Silicon (Si) wafer surface becomes more essential. This paper discusses a new optical nano-defects detection method, which is applicable to silicon wafer surface inspection for next-generation semiconductors. In our proposed method, the evanescent light is emerged on the wafer surface with total internal reflection (TIR) of infrared (IR) laser at the Si-air interface. By scanning the surface where the evanescent light is emerging with a very shaped fiber probe, it enables to detect nanometer scale defects in the vicinity of Si wafer surface without diffraction limit to resolution. To experimentally verify the feasibility of this method, an evanescent light measurement system was developed and several fundamental experiments were performed. The results show that the proposed Si wafer microdefects detection method can detect the microdefect with 10nm scale on and beneath the surface based on evanescent light distribution.