Studying the Influence of Illumination Design in Quality Inspection on Vaccine Vials Production Line Using Computer Vision

In the industrial context, there are key factors that directly affect the system’s efficiency. Higher demands for both quantity and quality in today’s market call for constant research and development of technologies for automating production and quality control. Machine vision is a solution to increase speed and accuracy in defect detection. However, applications from machine vision are only effective if there is good data input. This is the reason why a machine vision system, needs high-quality input images from a well-designed illumination system. These illumination systems are designed to highlight faults in products. Therefore, the images obtained will provide optimized data for easier image processing thus directly increase the processing speed, accuracy, and overall system performance. To achieve this goal, this paper presents a few approaches to enhance and optimize images by implements illumination techniques into a miniature model of pharmaceutical bottle assembly line using machine vision as the inspector block. In this paper, we will evaluate the critical needs of using customize illumination system for quality inspection on an assembly line.

An Audit Quality Inspection Scandal: Ethical Lapses in a Misguided Attempt to Alleviate Regulatory Scrutiny

In 2017, KPMG discovered that several high-ranking partners in its Department of Professional Practice (DPP) had surreptitiously obtained highly confidential information on upcoming PCAOB inspections. In obtaining this information, these KPMG partners were able to anticipate and prepare for PCAOB inspections, causing the firm’s inspection deficiency rate to plummet and its executives to tout the success of their efforts to improve audit quality. Once the firm discovered the scandal, the individuals involved were terminated, and six of them were ultimately convicted of felonies. This case study introduces students to relevant auditing standards, audit quality concepts, and facilitates discussion of a number of ethical issues. Learning objectives for this case include obtaining an understanding of the PCAOB and its inspection program, understanding audit documentation standards, demonstrating the ability to evaluate ethical issues, applying the fraud triangle in a unique setting, and assessing responsibility for the various parties involved.

Development of an automated system for adaptive post-weld treatment and quality inspection of linear welds

High-frequency mechanical impact (HFMI) treatment is a well-documented post-weld treatment to improve the fatigue life of welds. Treatment of the weld toe must be performed by a skilled operator due to the curved and inconsistent nature of the weld toe to ensure an acceptable quality. However, the process is characterised by noise and vibrations; hence, manual treatment should be avoided for extended periods of time. This work proposes an automated system for applying robotised 3D scanning to perform post-weld treatment and quality inspection of linear welds. A 3D scan of the weld is applied to locally determine the gradient and curvature across the weld surface to locate the weld toe. Based on the weld toe position, an adaptive robotic treatment trajectory is generated that accurately follows the curvature of the weld toe and adapts tool orientation to the weld profile. The 3D scan is reiterated after the treatment, and the surface gradient and curvature are further applied to extract the quantitative measures of the treatment, such as weld toe radius, indentation depth, and groove deviation and width. The adaptive robotic treatment is compared experimentally to manual and linear robotic treatment. This is done by treating 600-mm weld toe of each treatment type and evaluating the quantitative measures using the developed system. The results showed that the developed system reduced the overall treatment variance by respectively 26.6% and 31.9%. Additionally, a mean weld toe deviation of 0.09 mm was achieved; thus, improving process stability yet minimising human involvement.

An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

Assembly Quality Inspection of Combine Harvester Based on Whale Algorithm Optimization LSSVM

In order to detect the assembly quality of the combine harvester accurately and effectively, a method for the assembly quality inspection of the combine harvester based on the improved whale algorithm (IWOA) to optimize the least square support vector machine is proposed. Aiming at the characteristics of whale optimization algorithm’s weak search ability and easy maturity, this paper introduces the cosine control factor and the sine time-varying adaptive weight to improve it and uses the benchmark function to verify the general adaptability of the algorithm. Combined with the local mean decomposition (LMD), the assembly quality inspection model of the combine harvester was established and applied to the Dongfanghong 4LZ-9A2 combine harvester for experimental verification. The experimental results show that the IWOA proposed in this paper has better optimization ability and adaptability. The average accuracy of the IWOA model proposed in this paper reaches 90.5%, which is 4% higher than that of the WOA model, and the standard deviation of the average accuracy is reduced by 0.15%, which indicates that the IWOA model has better stability.

Research on Project-based Teaching of Building Decoration Construction Technology

Building decoration construction technology is the professional core course of construction engineering technology major in higher vocational colleges. The focus of this course is to cultivate students' ability to prepare construction scheme, organize on-site construction, project quality inspection and on-site operation management and control. Starting from the characteristics of architectural decoration construction technology course, this paper carries out Project-based teaching design, establishes the Project-based teaching mode of "combination of work and study", and determines the specific teaching contents and assessment methods, to effectively promote the teaching reform of the course.