Technical Library | 2017-04-28 07:53:37.0
A major drawback to Industry 4.0 that few write about is maintenance of an industry 4.0 plant. The maintenance aspect is a much greater and immediate drawback than even the commonly known major concern of security, and the lesser concern of system integration standards. Maintenance of 4.0 systems has, and will continue to result in related huge increases in process downtime. The barriers to overcoming the maintenance/downtime drawbacks of a 4.0 system are almost insurmountable. Has the Smart Manufacturing Leadership Coalition (SMLC) addressed the maintenance paradox? “... model also demands the ability to calculate and manage risk and uncertainty within very different operating structures. ..” Continue reading in pdf or for even more see and share http://bin95.com/Industry40inUSA.htm
Technical Library | 2021-03-24 01:34:35.0
In this article we propose a generic test script for real=time embedded software system testing, which has been applied to ATE (Automated Test Equipment). After a summary of the theory about embedded software automated test based on test script, the design philosophy and implementation details are described. We have chosen an ATE and integrated python interpreter into it.
Technical Library | 2020-08-27 01:15:10.0
Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers' first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.
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