Assembly line balancing (ALB) is used in many industries to minimize the number of stations, improve the efficiency and work load balance among stations. Enhanced Genetic Algorithm (EGA) is proposed using precedence preservative crossover and scramble mutation techniques, aiming to minimize two objectives; number of stations as a primary objective and smoothing index as a secondary objective. Benchmark problems were selected from the literature, used to test the efficacy of the algorithm and to compare the results with the well-known algorithm SALOME. The results showed high efficacy while handling two objectives. It outperformed SALOME in work load balance and efficiency. On the other, EGA outperformed the Genetic Algorithm (GA) developed by [1] in minimizing the number of stations.
Surface defects represent a major threat for product quality and its function that require proper inspection. Variety of surface defects makes their inspection more complicated, costly and requires longer time. Reliance on human inspection can lead also to less consistent results due to the variance in expertise and human error. For those reasons, traditional inspection methods less fit to fast automated manufacturing systems. Employing computer vision techniques in vision-based Inspection systems (VBI) can lead to developing better systems that match modern manufacturing systems in terms of speed, automation, higher productivity, less dependency human experience and cost optimization. In this research, an automated vison-based inspection system (CAI-2) is developed for detection and classification of surface defects encountered in metal parts using Digital Image Processing (DIP) techniques. CAI-2 receives the image of the part under inspection as an input, detects and generates automatically the type, number and location of existing surface defects. Six types of surface can be detected using the proposed method including Cracks, dents, fretting, flaking, rust, and smearing. The accuracy and effectiveness of the developed model were evaluated against skilled inspectors by measuring the values of inspection time, recall, precision and f-measure parameters values. Experimental results proved competitive accuracy and efficiency of the proposed inspection model compared to traditional inspection methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.