Machine vision system for automatic inspection of surface defects in aluminum die casting

A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Machine vision system for automatic inspection of surface defects in aluminium die casting

A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.

Aluminium powder forging process using a rotating platen

A torsional upset forging process is analysed on the basis of plasticity theory for powder metal forging. Torsional upset forging is a process to be performed by rotating a lower die with a punch travelling along the longitudinal direction of a work-piece. In this study, an upper bound analysis considering bulging effect, finite element method simulation (DEFORM3D), and experimental research have been performed for the process. A simple kinematically admissible velocity field for a three dimensional deformation is presented for the torsional upset forging of a cylindrical billet. Distributions of stress, strain, and forging load in the process have been obtained, and compared with those in conventional upset forging. In the process, an increase in a friction factor and rotation speed results in a decrease in magnitude of upset force, dead metal zone, and non-homogeneous deformation. This process can reduce forming load, which leads to improvement of die life, and also reduce bulging effect. In addition, the initial sintered-structure and density distribution is improved by the process and surface defect due to high deformation is decreased.

Automatic inspection of metallic surface defects using genetic algorithms

This paper is concerned with the problem of automatic inspection of metallic surface using machine vision. An experimental system has been developed to take images of external metallic surfaces and an intelligent approach based on morphology and genetic algorithms is proposed to detect structural defects on bumpy metallic surfaces. The approach employs genetic algorithms to automatically learn morphology processing parameters such as structuring elements and defect segmentation threshold. This paper describes the detailed procedures which include encoding scheme, genetic operation and evaluation function.

The proposed method has been implemented and tested on a number of metallic surfaces. The results suggest that the method can provide an accurate identification to the defects and can be developed into a viable commercial visual inspection system.

Genetic learning based texture surface inspection

This paper presents a novel approach of visual inspection for texture surface defects. It is based on the measure of texture energy acquired by a kind if high performance 2D detection mask, which is learned by genetic algorithms. Experimental results of texture defect inspection on textile images are presented to illustrate the merit and feasibility of the proposed method.

Image processing and pattern recognition in textiles

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into / with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work forfiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yieldssatisfactory results by giving a high level of accuracy in classification.

Electrostatic complex of didodecyldimethylammonium and DNA: Langmuir monolayer, Langmuir–Blodgett film and dye recognition at air/water interface

DNA–didodecyldimethylammonium (DNA–DDDA) electrostatic complex was prepared and characterized through Fourier transformation infrared (FT-IR), 1H NMR and circular dichroism (CD) spectroscopy. When the dye molecule aqueous solutions were used as the subphase, the interaction between three dye molecules, acridine orange (AO), ethidium bromide (EB) and 5,10,15,20-tetrakis(4-N-methylpyridyl)porphine tetra(p-toluenesulfonate) (TMPyP) and the complex at air/solution interface were investigated through the surface pressure–area (π–A) isotherms, Brewster angle microscopy and UV-Vis spectroscopy, respectively. Our investigation indicates that the interaction capabilities of the three dyes to DNA–DDDA complex are different and present an order of TMPyP>AO>EB. For the interaction forms, we believe that TMPyP intercalates into the double helix of DNA, and AO adsorbs onto the surface of the DNA. As for EB, the measured signal is too weak to give a definite interaction form in the present experiment.

Parameter sensitivity analysis of surface plasmon resonance biosensor through numerical simulation

Surface based analytical tools have gained more importance for rapid, sensitive and label-free monitoring of molecular recognition events. Surface plasmon resonance (SPR) has played a prominent role in real time monitoring of surface binding events. SPR is increasing its significance especially for the study of ultrathin dielectric layer. This paper investigates the role of thin films of gold, silver and aluminium for protein detection in SPR biosensors. It is shown that the sensitivity, which is indicated by the shift of plasmon dip, is not linearly related to the thickness of protein but quadratic over a specific range. The approach involves a plot of a reflectivity curve as a function of the angle of incidence.

A surface characterisation and microstructural study by scanning electron microscopy of the N-methyl-n-alkylpyrrolidinium tetrafluoroborate organic salts

A microstructural characterisation of the family of N-methyl-N-alkylpyrrolidinium tetrafluoroborate organic salts was carried out by observation of powder surface morphologies with the aim of extending the microstructure-property correlation. Inherent difficulties limiting extensive studies of organic solids by SEM, including volatility under vacuum, charging due to electron beam irradiation, and air-sensitivity were overcome with the use of a Field Emission SEM and cryostage attachment. This technique, providing considerable improvements in image quality at low accelerating voltages, enabled direct observation of complex microstructural features in samples exhibiting high temperature plastic crystalline phases (N,N-dimethylpyrrolidinium tetrafluoroborate [P11BF4]; N-methyl-N-ethylpyrrolidinium tetrafluoroborate [P12BF4]; N-methyl-N-propylpyrrolidinium tetrafluoroborate [P13BF4]). Extensive lattice imperfections including grain boundaries, slip planes and dislocation pits were observed within particles of approximately 200 mgrm diameter. The N-methyl-N-butylpyrrolidinium tetrafluoroborate (P14BF4) sample in this series revealed columnar single crystals with high aspect ratios. The origin of plastic flow properties is discussed using single crystal and polycrystalline slip observations and a relationship proposed between defect characteristics and transport properties.

Electrochemical metal ion sensors. Exploiting amino acids and peptides as recognition elements

Amino acids and peptides are known to bind metal ions, in some cases very strongly. There are only a few examples of exploiting this binding in sensors. The review covers the current literature on the interaction of peptides and metals and the electrochemistry of bound metal ions. Peptides may be covalently attached to surfaces. Of particular interest is the attachment to gold via sulfur linkages. Sulfur-containing peptides (eg cysteine) may be adsorbed directly, while any amino group can be covalently attached to a carboxylic acid-terminated thiol. Once at a surface, the possibility for using the attached peptide as a sensor for metal ions becomes realised. Results from the authors’ laboratory and elsewhere have shown the potential for selective monitoring of metal ions at ppt levels. Examples of the use of poly-aspartic acid and the copper binding peptide Gly-Gly-His for detecting copper ions are given.

A novel defect detection and identification method in optical inspection

Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.

A surface-stress-based microcantilever aptasensor

Biosensors based on microcantilevers convert biological recognition events into measurable mechanical displacements. They offer advantages such as small size, low sample volume, label-free detection, ease of integration, high-throughput analysis, and low development cost. The design and development of a microcantilever-based aptasensor employing SU-8 polymer as the fabrication material is presented in this paper. Aptamers are employed as bioreceptor elements because they exhibit superior specificity compared to antibodies due to their small size and physicochemical stability. To immobilise thrombin DNA aptamer on the bare SU-8 surface of the aptasensor, a combined plasma mode treatment method is implemented which modifies the surface of the aptasensor. Label-free detection of thrombin molecules using the fabricated aptasensor is successfully demonstrated. The measured deflection is one order of magnitude higher than that of a silicon nitride microcantilever biosensor. The developed aptasensor also demonstrates high specificity.

A surface-stress-based microcantilever aptasensor

Biosensors based on microcantilevers convert biological recognition events into measurable mechanical displacements. They offer advantages such as small size, low sample volume, label-free detection, ease of integration, high-throughput analysis, and low development cost. The design and development of a microcantilever-based aptasensor employing SU-8 polymer as the fabrication material is presented in this paper. Aptamers are employed as bioreceptor elements because they exhibit superior specificity compared to antibodies due to their small size and physicochemical stability. To immobilise thrombin DNA aptamer on the bare SU-8 surface of the aptasensor, a combined plasma mode treatment method is implemented which modifies the surface of the aptasensor. Label-free detection of thrombin molecules using the fabricated aptasensor is successfully demonstrated. The measured deflection is one order of magnitude higher than that of a silicon nitride microcantilever biosensor. The developed aptasensor also demonstrates high specificity.

Theoretical modeling and experimental validation of surface stress in thrombin aptasensor

Adsorption of target molecules on the immobilized microcantilever surface produced beam displacement due to the differential surface stress generated between the immobilized and non-immobilized surface. Surface stress is caused by the intermolecular forces between the molecules. Van der Waals, electrostatic forces, hydrogen bonding, hydrophobic effect and steric hindrance are some of the intermolecular forces involved. A theoretical framework describing the adsorption-induced microcantilever displacement is derived in this paper. Experimental displacement of thrombin aptamer-thrombin interactions was carried out. The relation between the electrostatic interactions involved between adsorbates (thrombin) as well as adsorbates and substrates (thrombin aptamer) and the microcantilever beam displacement utilizing the proposed mathematical model was quantified and compared to the experimental value. This exercise is important to aid the designers in microcantilever sensing performance optimization.