Special issue on smart automation and manufacturing
With the development of recent information and computer technologies, a variety of opportunities can be identified to promote the transformation of today's manufacturing paradigm to smart automation and manufacturing. The challenges facing this transformation should be how to maintain the high quality performance while adapting system flexibility and intelligence. However, such challenges should be treated in different ways, depending on their problem domains. In this special issue, we would like to cover a few emerging fields that require more attention to realise future smart automation and manufacturing notions, which include robot precision, energy consumption, inventory cost, system scalability, safety, process efficiency and product quality. Even though each paper deals with a specific problem of its own field, the foundation of the methods can be easily driven from three fundamental research approaches; i.e. (1) data driven analytics, (2) computeraided design and (3) systems optimisation. This special issue includes the selected papers from the 26th International Conference on Flexible Automation and Intelligent Manufacturing in 2016. Most of the conference papers were of high quality, particularly, the selected papers presented in this issue. A summary of each paper is presented as follows. The paper titled 'An analytical approach for positioning error and mode shape analysis of n legged parallel manipulator' by Sohail et al. presents an analytical model for parallel manipulator positioning. Their approaches show a great potential to perform precise positioning for n legged parallel manipulator. In terms of energy efficiency, the paper titled 'Energy efficient robot applications towards sustainable manufacturing' by Wang et al. discusses energy consumption of robots during assembly in a cloud environment. They propose the cloudbased energy efficient approach and show that practical application processes can be largely enhanced towards sustainable manufacturing. As an example of data driven quality control and costsensitive decision, Kim et al. study an imbalanced classification problem for die casting quality analysis in the paper titled 'Imbalanced classification of manufacturing quality conditions using cost sensitive decision tree ensembles' Their results highlight the influence of data analytics for quality control in advanced manufacturing environments. The paper titled 'A novel coarse to fine registration approach for aligning partially overlapped 3D scanned data' by Tuladhar et al. presents a coarse to fine alignment technique for 3D objects registration. Based on the computer vision and data driven approach, reliable corresponding points can be found to achieve higher accuracy during objects registration. To improve the quality evolution for end milling, the paper titled 'Vision based surface roughness evaluation system for end milling' by Cuka et al. proposes a vision based surface roughness evaluation technology. Their results show how to reconstruct, calibrate and evaluate the three dimensional surface for a better inspection process. To maximise total profit of the production system that consists of remanufactured and hybrid products, the paper titled 'A simulated annealing algorithm with neighbourhood list for capacitated dynamic lot sizing problem with returns and hybrid products' by Koken et al. presents a mixed integer nonlinear programming model. They applied meta heuristic approaches, which show a great potential in inventory and production control in remanufacturing process. The paper titled 'Design of scalable agent based reconfigurable manufacturing systems with Petri nets' by Hsieh proposes an optimisation model for the scalability problem in flexible manufacturing. The author combined multi agent approach with Petri net models and Lagrange relaxation optimisation and show how to effectively reconfigure manufacturing systems that have frequently changing demands and technologies. In the paper titled 'Evaluation of interval regression analysis for uncertain resistance spot welding quality data' Park and Kim study the application of data analysis model for uncertain resistance spot welding quality data. Their interval regression analysis method shows the capability to identify valuable information from the uncertainty to support decision making. The paper titled 'Path planning for SCARA robot based on marker detection using feature extraction and labelling' by Ji Yang Lee and Cheol soo Lee presents a computer vision based approach for selective compliance assembly robot arm path planning. Based on Speeded Up Robust Features and labelling, 3D positions of the markers can be found. To improve the safety operations in mass production, the paper titled 'Formulation of novel DFMA rules for the advancement of ergonomic factors in non linear iterative prototype assembly' by Matthews et al. presents planning of assembly ergonomics for non linear iterative prototype assembly. Based on prototype construction studies, their approach can significantly reduce reassembly times and the number of injuries. In paper titled 'Realization of a multi sensor framework for process monitoring of the wire arc additive manufacturing in producing Ti6Al4V parts' Xu et al. provide a multi sensor framework for process monitoring to improve the quality of wire arc additive manufacturing. Their approach provides a INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2018, VOL. 31, NO. 8, 675 676 https:/doi.org/10.1080/0951192X.2018.1492069
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