Sandblasting Defect Inspection of Investment Castings Based on Deep Convolutional Neural Network

Investment castings often have surface impurities and pieces of shell molds can remain on the surface after sandblasting. Identification of defects involves time-consuming manual inspections in working environments of high noise and poor air quality. To reduce labor costs and increase the health and safety of employees, we applied automated optical inspection (AOI) combined with a deep learning framework based on convolutional neural networks (CNNs) to the detection of sandblasting defects. We applied the following four classic CNN models for training and predictive classification: AlexNet, VGG-16, GoogLeNet, and ResNet-34. In terms of predictive classification, AlexNet, VGG-16, and GoogLeNet v1 could accurately determine whether there were defects. Among the four models, AlexNet was the most accurate, with prediction accuracy of 99.53% for qualifying products and 100% for defective products. We demonstrate a direct detection technique based on the AOI and CNN structure with a fast and flexible computational interface.

Optical Material Characterisation of Prepreg CFRP for Improved Composite Inspection

Automated fibre layup techniques are often applied for the production of complex structural components. In order to ensure a sufficient component quality, a subsequent visual inspection is necessary, especially in the aerospace industry. The use of automated optical inspection systems can reduce the inspection effort by up to 50 %. Laser line scan sensors, which capture the topology of the surface, are particularly advantageous for this purpose. These sensors project a laser beam at an angle onto the surface and detect its position via a camera. The optical properties of the observed surface potentially have a great influence on the quality of the recorded data. This is especially relevant for dark or highly scattering materials such as Carbon Fiber Reinforced Plastics (CFRP). For this reason, in this study we investigate the optical reflection and transmission properties of the commonly used Hexel HexPly 8552 IM7 prepreg CFRP in detail. Therefore, we utilise a Gonioreflectometer to investigate such optical characteristics of the material with respect to different fibre orientations, illumination directions and detection angles. In this way, specific scattering information of the material in the hemispherical space are recorded. The major novelty of this research are the findings about the scattering behaviour of the fibre composite material which can be used as a more precise input for the methods of image data quality assessment from our previous research and thus is particularly valuable for developers and users of camera based inspection systems for CFRP components.

Algorithm Research and Implementation Based on Component Optical Detection System

With the development of the times, gradually there is a new thing called electronic components born. Electronic components are collectively called components of electronic components and small machine instruments, such as diodes used in light bulbs. Components can be used in optical inspection systems, but they require some help from computer technology. Therefore, the purpose of this paper is to use algorithms to study component-based optical detection systems. After consulting the literature on components and optical detection systems, we analyzed the suitability of components and optical inspection systems by constructing different systems using a variety of algorithms. The experimental results show that the traditional ant colony algorithm is better than the AdaBoost algorithm and the genetic algorithm, so we finally choose to use the ant colony algorithm to construct the optical detection system.

Optical Inspection of CPVC Fitting Elbows and Fault Diagnosis of the Production Process

The topic of this study is the optical inspection of CPVC fitting elbows concerning the geometric parameters that can be detected in 2 dimensions. Based on the evaluation of the results, fault diagnosis has been set up for the production line by statistical calculations. The optical inspection was carried out in the Vision Development Module software environment produced by National Instruments, and the data were evaluated using Microsoft Excel.