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.

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.