Special issue on smart energy efficient manufacturing

Manufacturing factories have been paying for higher energy costs due to the increasing energy demand and rising prices of limited resources. Since 2004, energy prices have risen significantly. It was reported that the average fuel price increased from £522 in January 2004 to £1309 in November 2012 (http://www.uswitch.com/gas-electricity/guides/gas-electricity-prices/). The energy concern, not only in manufacturing but also in total life cycle, is with great significance in the near future. This is especially true in some developing countries such as China, India and Brazil where manufacturing industry consumes great myriad of resources for lifting their economy.

In order to make more sustainable and environmental decisions, manufacturing industry has been using advanced technologies such as Internet of Things (IoT), Cyber-physical System and cloud computing as well as innovative models which consider energy consumption and low-carbon emission. Thus, smart energy efficient manufacturing (SEEM) was proposed to reduce the energy consumption required to design, manufacturing, deliver products and services, maintain and recycle the products. Currently, SEEM has received significant attention from the global communities of sustainable development. Manufacturing companies have been taking initiatives to achieve efficient and sustainable energy management energy.

Researchers have been developing new models, methods, solutions and tools for SEEM. This special issue provides an opportunity for academia and practitioners to share state-of-the-art research and cases to handle the issues related to SEEM. There are 11 papers accepted, covering new approaches for SEEM by making full use of emerging technologies such as IoT and cloud computing as well as new models for SEEM decision-making by integrating various energy-concerned variables.

The first paper ‘An internet-of-things and cloud based approach for energy consumption evaluation and analysis for a product’, by Ying Zuo, Fei Tao and AYC Nee, presents an IoT and cloud-based approach for product energy consumption evaluation and analysis within product entire life cycle. This paper uses real-time and dynamic collection of energy consumption-related data to support the evaluation and analysis.

The paper, ‘A physical internet-enabled building information modelling system for prefabricated construction’, by Ke Chen, Gangyan Xu, Fan Xue, Ray Y. Zhong, Diandian Liu and Weisheng Lu, introduces an investigation of a prefabricated construction project in Hong Kong which relates to energy consumptions. A solution integrated Auto-ID technology, building information modelling and cloud computing is used for higher energy efficient prefabricated construction.

The paper ‘IoT-enabled real-time energy efficiency optimization method for energy-intensive manufacturing enterprises’, by Wenbo Wang, Haidong Yang, Yingfeng Zhang and Jianxue Xu, provides a real-time energy efficiency optimisation method for energy-intensive manufacturing enterprises. This method applies IoT technology to capture the real-time primitive production data which is converted into production performance indicators to increase the energy efficient manufacturing.

The fourth paper, ‘A process parameters selection approach for trade-off between energy consumption and polishing quality’, by Hongcheng Li, Haidong Yang, Chengjiu Zhu, Hua Fang and Jun Li, proposes a new approach to select operational parameters by considering energy consumption. A porcelain tile polishing with the chip formation energy and surface quality is demonstrated for multi-objective optimisation.

The next paper ‘Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks’, by Peng Liang, Hai-Dong Yang, Wen-Si Chen, Si-Yuan Xiao and Zhao-Ze Lan, discusses a research question that is whether a forecasting model can be trained by transferring knowledge from a data-sufficient domain to a data-insufficient domain. A shared connected deep neural network is presented for electricity consumption time-series anomaly forecasting.

The sixth paper ‘Feature-based carbon emission quantitation strategy for the part machining process’, by Guanghui Zhou, Ce Zhou, Qi Lu, Changle Tian and Zhongdong Xiao, introduces a method to quantify the overall carbon emission in manufacturing stage. A carbon emissions-process bill of material based on machining features is presented to support the quantitation strategy by decomposing a part into an aggregation of machining features.

The paper ‘State space modelling carbon emission dynamics of machining workshop based on carbon efficiency’, by Hongcheng Li, Haidong Yang, Huajun Cao and Chengjiu Zhu, establishes a hierarchical analysis framework for carbon emission dynamics in a three-level structure machining workshop. In this paper, a state space-based conceptual model is presented to evaluate the balancing trends. An experimental study is conducted in this research to verify the feasibility and applicability of the proposed framework.

The next paper ‘A hybrid approach to energy-efficient machining for milled components via STEP-NC’, by Honghui Wang, Xun Xu, Chengrui Zhang and Tianliang Hu, develops an ontology STEP-NC to offer energy demand information so as to create preliminary machining schemes (PMSs). Then, ant colony optimisation is utilised to choose the best machining scheme from the PMSs. This paper also gives a test example to validate the proposed ontology STEP-NC.

The ninth paper ‘Evaluation of product recyclability at the product design phase: a time series forecasting methodology’, by Zhi Li, Jiadong He, Xinjun Lai, Yunbao Huang, Tao Zhou, Ali Vatankhah Barenji and W.M. Wang, proposes a time-series forecasting approach to examine the products’ recyclability and the product design stage. Economic and environmental factors at different stages in the life cycle are considered in this paper. To show the effectiveness of this methodology, this research presents a case study of a cylinder engine design by providing decision support to the designers.

The paper ‘Structured information description framework oriented to energy-saving design of machinery equipment’, by Qingchao Sun, Yingjie Jiang, Zhiyong Sun and Wei Sun, presents a structured energy consumption description model – Energy Breakthrough Structure (EBS) which is used to describe the influence of design parameters on energy consumption. Using the EBS, the energy relationships could be determined based on working conditions, design proposals or product structures. This research takes a horizontal machining centre feed system as an example to show the feasibility of the proposed EBS.

The final paper ‘Three-stage optimization method for concurrent manufacturing energy data collection’, by Jianhua Guo and Haidong Yang, presents a three-stage optimisation method for the scheduling of data collection jobs which are divided into concurrent and serial sub-jobs. A time Petri net model is used to evaluate the collection completion time and a test experiment demonstrates the improvement of concurrent efficiency by more than 50% comparing with traditional Data Collection Job method.

We would like to thank all the reviewers who gave their significant comments and suggestions for improving the published papers in this special issue. Thank you all the contributors to make the publication of this special issue. Special thanks should be given to Professor Stephen T Newman, editor-in-chief of the International Journal of Computer Integrated Manufacturing and Dr Aydin Nassehi, managing editor, who gave their great support. We hope that this special issue will bridge the academic and practitioners so as to enhance the SEEM in the future.