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International Journal of Computer Integrated Manufacturing

Call for Papers | Data-Driven Modeling and Analytics for Optimization of Complex Manufacturing Systems | Deadline: 31 March, 2020

Introduction

 

As the core issue for improving manufacturing system performance (such as product quality, production efficiency and cost, etc.), modeling, analysis and optimization of manufacturing systems have always been studied towards smart manufacturing. However, due to the large-scale and dynamic characteristics of complex manufacturing systems, the causal relationship between system performance and manufacturing process parameters is difficult to determine. Therefore, traditional models and algorithms are now facing some challenges such as the “Curse of Dimensionality”, high computational complexity and so on.

With the rapid development of information technology, massive production data is captured, practitioners and academics are paying more and more attention to the huge value hidden behind the data. Data-driven technology can effectively help for revealing the inherent laws of complex manufacturing processes, transforming data into production and operation knowledge, optimizing production processes, improving product quality and production efficiency, as well as enhancing product lifecycle management level.

Data-driven modeling and analysis has become one of the most promising methods for optimization of complex systems, and has made important breakthroughs in many research areas (Runge et al., 2019; Severson et al., 2019). In biology science, data-driven modeling and analysis has been used to quantitatively identify direct dependencies between genes, reconstruct gene regulatory network and causal relations (Zhao et al., 2016), and identify cell behaviors affecting the observed aggregation dynamics without full knowledge of the underlying biological mechanisms (Cotter et al., 2017). In medical science, data-driven modeling and analysis can not only systematically explore the molecular complexity of specific diseases, but also identify disease modules and pathways, as well as the molecular relationships between distinct phenotypes (Cheng et al., 2019). These successful cases provide a new way of thinking for the modeling, analysis and optimization of complex manufacturing systems in the industrial field. How to use the big data to establish an effective model describing the complex manufacturing system? How to analyze the data-driven model to reveal the law of system operation? How to dynamically regulate the data-driven model to optimize the system performance? This special issue provides an opportunity for academia and practitioners to share state-of-the-art research and cases in the field of modeling, optimization and control in manufacturing systems. Original works are invited for consideration for publication.

Important dates 
Manuscript submission:31 March, 2020
Reviewer reports:30 June, 2020
Revised paper submission:31 July, 2020
Final manuscript submissions:30 September, 2020
Approximate publication date:March 2021

Scope of the Theme

 

Original, high quality theoretical and empirical research papers are invited for submissions in this special issue. Typical topics include, but not limited to, following topics:

 

  • State-of-the-art and future perspectives of data-driven technology, especially in the industrial and manufacturing fields
  • Data-driven modeling and simulation of manufacturing systems
  • Analysis of big data in Manufacturing system
  • Data-driven analysis of manufacturing system features and functional performance
  • Data-driven model based optimization of manufacturing system
  • Dynamic control of the manufacturing system model


All submissions will be judged for their appropriateness to the journal’s remit and the novelty of their theoretical and practical research contributions. While quantitative research is preferred, relevant qualitative research studies as well as case studies are also welcomed.

Submission Guidelines

 

To prepare the manuscripts, authors should follow the “Instructions for authors” presented at the journal website:

(http://www.tandfonline.com/action/authorSubmission?journalCode=tcim20&page=instructions#.Vbs_2_mqpBc)

Please check and follow the instructions on electronic submission system via Taylor & Francis: https://mc.manuscriptcentral.com/tcim. Full papers should follow the IJCIM guidelines and clearly indicate the “Special issue on Data-Driven Modeling and Analytics for Optimization of Complex Manufacturing Systems”. For further enquiries, please contact the managerial guest editor.

Guest Editor



Associate Prof. Wei Qin, (wqin@sjtu.edu.cn)
School of Mechanical Engineering, Shanghai Jiao Tong University, China

Prof. Yingfeng Zhang,
College of Mechanical and Electrical Engineering, Northwestern Polytechnical University, China

Prof. Ting Qu, 
School of Intelligent Systems Science and Engineering, Jinan University, China

Prof. Xinyu Li,
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, China

International Journal of Computer Integrated Manufacturing

International Journal of Computer Integrated Manufacturing (IJCIM) reports new research in theory and applications of computer integrated manufacturing. The scope spans mechanical and manufacturing engineering, software and computer engineering as well as automation and control engineering with a particular focus on today’s data driven manufacturing.

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