In order to improve the production capacity of traditional wood manufacturing industry, efficient wood quality and thickness detection is a challenging issue. This paper firstly carries out digital twin modeling for a drawer side panel processing line of a wood company and explores the efficiency problems existing in the links of quality inspection and thickness inspection of wood by means of value stream mapping. Therefore, we adopted a lightweight convolution neural network MobileNetV2 for wood quality detection, which realized efficient wood quality identification. In contrast, traditional convolution neural network has many weighting parameters and large scale of generating detection model, which makes it difficult to apply in situations with limited computing power and memory. Secondly, due to the stronger robustness and generalization ability of the residual network, we used ResNet to detect the wood thickness and obtain reliable performance. Finally, we reasonably embedded them in the whole wood production process and established the simulation model of production line before and after improvement in FlexSim simulation software. The experimental results show that the improved plan can simplify the workshop production process, increase the production balance rate by 29.07%, increase the product value-added rate from 0.08% to 0.11%, and shorten the production cycle by 2 hours. Performance indicators such as product inventory, number of people, and equipment utilization also improve significantly. Based on the above results, the validity of the production process improvement model proposed by US based on lightweight convolution and deep residual network is demonstrated.
Large-volume waste products, such as refrigerators and automobiles, not only consume resources but also pollute the environment easily. A two-sided disassembly line is the most effective method to deal with large-volume waste products. How to reduce disassembly costs while increasing profit has emerged as an important and challenging research topic. Existing studies ignore the diversity of waste products as well as uncertain factors such as corrosion and deformation of parts, which is inconsistent with the actual disassembly scenario. In this paper, a partial destructive mode is introduced into the mixed-model two-sided disassembly line balancing problem, and the mathematical model of the problem is established. The model seeks to comprehensively optimize the number of workstations, the smoothness index, and the profit. In order to obtain a high-quality disassembly scheme, an improved non-dominated sorting genetic algorithm-II (NSGA-II) is proposed. The proposed model and algorithm are then applied to an automobile disassembly line as an engineering illustration. The disassembly scheme analysis demonstrates that the partial destructive mode can raise the profit of a mixed-model two-sided disassembly line. This research has significant application potential in the recycling of large-volume products.
Purpose This paper aims to provide feasible countermeasures for the application of lean manufacturing in automatic sand casting workshops to optimize production line balance. Design/methodology/approach A production line balance optimization model for sand casting workshops is proposed. The value stream mapping (VSM) approach is applied to diagnose the problems in the production process. An optimization scheme is established based on eliminate, combine, rearrange, simplify and increase theory and Kanban management method. Further, simulation is done to compare current VSM and future VSM. Findings After implementing the proposed model, findings indicated that the idle time of equipment was effectively reduced, the line of balance of the production line was increased by 44.7%, the production lead time was shortened by 60.3% and the production capacity was increased by 50.0%. Research limitations/implications Application of the optimization model in this study is limited to sand casting workshops that have realized automatic or semi-automatic production. Originality/value This paper provides an optimization model for the implementation of lean manufacturing in sand casting workshops and provides a reference case that reflects the actual application of lean manufacturing tools in a real situation.
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