Development of the digital model of the jewellery production process for resource optimisation and prediction

被引:8
作者
Lin F. [1 ]
Wong M.C. [1 ]
Ge M. [1 ]
机构
[1] Intelligent Robotic & Automation, Hong Kong Productivity Council
来源
HKIE Transactions Hong Kong Institution of Engineers | 2018年 / 25卷 / 04期
关键词
Industry; 4.0; Production process modelling; Resource optimisation and prediction; Smart manufacturing;
D O I
10.1080/1023697X.2018.1535284
中图分类号
学科分类号
摘要
Smart manufacturing is becoming one of the core tendencies in manufacturing nowadays. In the conventional production process mode, the varieties in the production process, manpower processing time, and production scheduling lead to a big challenge in production process management. In order to keep pace with the rapid technology development and market demand diversification, digital and intelligent transformation became extremely essential for manufacturing industries. It is able to evaluate and manage the production process performance in a timely and scientific manner. With the digital model, the production efficiency can be improved and the resources management can be optimised based on the prediction. In this study, the traditional labour-intensive jewellery manufacturing is used and analysed to evaluate its digital model for the resource optimisation and prediction. By evaluating the production process by functional groups separately with the manpower level classification, the digital model could provide an automatic and efficient solution to its production process management system and logistic flow. It eliminates the unnecessary time-consuming working process and enhances the working process efficiency, which is capable of optimising the entire production efficiency as well as performing the resource prediction. © 2019 The Hong Kong Institution of Engineers.
引用
收藏
页码:229 / 236
页数:7
相关论文
共 4 条
[1]  
Wollschlaeger M., Sauter T., Jasperneite J., The future of industrial communication: Automation networks in the Era of the internet of things and industry 4.0, IEEE Ind Electron Mag., 11, 1, pp. 17-27, (2017)
[2]  
Chen B., Wan J., Shu L., Et al., Smart factory of industry 4.0:Key technologies, application case, and challenges, IEEE Access., 6, pp. 6505-6519, (2018)
[3]  
Song Z., Sun Y., Yan H., Et al., Robustness of smartmanufacturing information systems under conditions of resource failure: A complex network perspective, IEEE Access., 6, pp. 3731-3738, (2018)
[4]  
Qi Q., Tao F., Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison, IEEE Access., 6, pp. 3585-3593, (2018)