Selected Papers on Energy-efficient Machining Systems


  1. Condition monitoring towards energy-efficient manufacturing: a review (published online: 23 January 2017)
  2. An interoperable energy consumption analysis system for CNC machining (published online: 1 Jan 2017)
  3. A new high-performance open CNC system and its energy-aware scheduling algorithm (published online: 16 June 2017)
  4. Facing energy-aware scheduling: a multi-objective extension of a scheduling support system for improving energy efficiency in a moulding industry (published online: July 2017)
  5. Ubiquitous manufacturing system based on Cloud: A robotics application (published online: June 2017)
  6. Context-aware collect data with energy efficient in Cyber–physical cloud systems (published online: 7 June 2017)
  7. Cyber-Physical Control for Energy-Saving Vehicle Following with Connectivity (published online: 11 May 2017)
  8. Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing (published online: 15 March 2017)
  9. Energy consumption awareness in manufacturing and production systems (published online: 30 May 2016)
  10. Real-Time Demand Bidding for Energy Management in Discrete Manufacturing Facilities (published online: Jan. 2017)

Detailed Information

1. Zhou Z, Yao B, Xu W, Wang L. Condition monitoring towards energy-efficient manufacturing: a review. The International Journal of Advanced Manufacturing Technology. 2017:1-21.

ABSTRACT: Recently, sustainable development has obtained increasing attentions from governments, industry, and academia owing to the limited natural resources. In the area of energy consumption, manufacturing accounts for a major portion of the total energy usage in industry. There is a clear necessity for energy-efficient manufacturing by optimizing manufacturing activities. Condition monitoring is the technology that provides runtime information for optimization. This paper aims to provide a better understanding of past achievements and future trends of condition monitoring towards energy-efficient manufacturing. Since there are a variety of sensors and technologies that can be used for condition monitoring towards energy-efficient manufacturing, this paper divides manufacturing activities into three levels, namely unit process level, shop-floor level, and supply chain level, and summarizes and discusses the sensors and technologies required to enable energy-efficient manufacturing on each level. With the advancement of technology, condition monitoring shows the characteristic of intelligence. Intelligent sensors that can be applied to condition monitoring in energy-efficient manufacturing are also reviewed. This paper can be helpful to manufacturers who are willing to improve energy efficiency in own manufacturing practice.

2. Peng T, Xu X. An interoperable energy consumption analysis system for CNC machining. Journal of Cleaner Production. 2017 Jan 1;140:1828-41.

ABSTRACT: Due to the ever-increasing energy prices and the growing public awareness of environmental well-being, improving energy efficiency has become a major concern for manufacturing industry. Effective energy analysis is the basis for a company’s energy saving strategy. A systematic perspective is taken in this research and an interoperable energy analysis system is developed based on two techniques. One is the hybrid energy consumption modelling that combines high-level and low-level models. It improves the description of energy consumption in terms of accuracy, scalability, effectiveness and practicability. The other is integrated, standardised and STEP-NC compliant energy data models, which support energy analysis for various activities and purposes. A prototype system was developed, which enables systematic energy analysis and evaluation, and assists decision-making based on these techniques. Two case studies were conducted to demonstrate that energy estimation for different machining systems based on STEP-NC data, and energy analysis with adapted parameters is feasible and straightforward. With the developed energy analysis system, various activities that collectively contribute to overall energy efficiency are tightly integrated, which, in turn, keeps machining industry sustainable.

3. Deng CY, Guo RF, Xu X, Zhong RY, Yin Z. A new high-performance open CNC system and its energy-aware scheduling algorithm. The International Journal of Advanced Manufacturing Technology. 2017:1-3.

ABSTRACT: Computer numerical control (CNC) systems are shifting to a direction of open architecture which has better flexibility, adaptability, versatility, and expansibility. Existing CNC systems tend to have a high level energy consumption. This paper introduces a new open CNC system based on the low-power embedded platform, named open and high-performance CNC (OHP-CNC). OHP-CNC is able to achieve high precision, high efficiency, and low power consumption by making use of international standards, open components such as hardware and software, and an energy-aware real-time scheduling algorithm. The proposed algorithm for mixed tasks, including periodic and aperiodic tasks, is divided into two phases. Firstly, the slack time and utilization are calculated on each processor and tasks are assigned to the processor according to the load. Secondly, because there is a trade-off between the energy-saving and the response times of the aperiodic task, the scheduling server is used to schedule aperiodic tasks in order to meet the response time constraints of aperiodic tasks. Meanwhile, periodic tasks recycle the slack time with dynamic voltage scaling technology to achieve low power consumption. Experiment results show that the energy-aware real-time algorithm yields high-performance and effective machining processes.

4. Paolucci M, Anghinolfi D, Tonelli F. Facing energy-aware scheduling: a multi-objective extension of a scheduling support system for improving energy efficiency in a moulding industry. Soft Computing. 2017 Jul 1;21(13):3687-98.

ABSTRACT: Nowadays most industries do not integrate product, process and energy data. Costs due to energy consumption are often considered externalities and energy efficiency is not deemed a relevant performance criterion. In energy-intensive processes, as injection moulding, the specific energy consumption, embedded inside the same products, depends on the machine–product combinations. Multi-objective scheduling, including the energy data acquired from shop floor and allocation criteria, is a valuable approach to improve energy efficiency. This paper presents the extension of a commercial detailed scheduling support system developed within a regional Italian project aiming at providing tools to manufacturing industry for improving energy efficiency. The project designed a monitoring system developed by instrumenting injection moulding presses to acquire the energy consumption for each product–machine combination. The commercial scheduling system was extended by implementing a multi-objective metaheuristic scheduling approach. The experimental assessment of the proposed approach involved a major producer of plastic dispensers. The extended algorithm simultaneously optimizes the total weighted tardiness, the total setup and the energy consumption costs. The obtained results, produced for a real test case and a set of random generated instances, show the effectiveness of the proposed approach.

5. Wang XV, Wang L, Mohammed A, Givehchi M. Ubiquitous manufacturing system based on Cloud: A robotics application. Robotics and Computer-Integrated Manufacturing. 2017 Jun 30;45:116-25.

ABSTRACT: Modern manufacturing industry calls for a new generation of production system with better interoperability and new business models. As a novel information technology, Cloud provides new service models and business opportunities for manufacturing industry. In this research, recent Cloud manufacturing and Cloud robotics approaches are reviewed. Function block-based integration mechanisms are developed to integrate various types of manufacturing facilities. A Cloud-based manufacturing system is developed to support ubiquitous manufacturing, which provides a service pool maintaining physical facilities in terms of manufacturing services. The proposed framework and mechanisms are evaluated by both machining and robotics applications. In practice, it is possible to establish an integrated manufacturing environment across multiple levels with the support of manufacturing Cloud and function blocks. It provides a flexible architecture as well as ubiquitous and integrated methodologies for the Cloud manufacturing system.

6. Liu Y, Liu A, Guo S, Li Z, Choi YJ, Sekiya H. Context-aware collect data with energy efficient in Cyber-physical cloud systems. Future Generation Computer Systems. 2017 Jun 7.

ABSTRACT: Cyber–Physical Cloud System is emerging as a promising system that enables a wide range of applications. In many applications, smart grids, sensing operations generate large amount of data. In order to effectively and efficiently collect large amount of data, a Global view of Context-aware Sensing and Collection (GCSC) scheme is proposed for exploiting both local and global of spatial–temporal correlations to perform data collection in cyber–physical cloud system (CPC system). In a GCSC scheme, the size of the representative region varies according to the residual energy of its smart sensor nodes. For areas far from the sink decrease the size of the representative region to keep high accuracy in the collected data, while areas near the Sink increase the size of the representative region to lower energy consumption to ensure efficient energy management. Thus, the accuracy of sensing data and lifetime can be enhanced at same time. Both theoretical analysis and experimental results indicate that the performance of GCSC scheme is better than the performance in previous studies. Compared with previous schemes, GCSC scheme can improve the data accuracy by 7.56% ∼ 23.16% and increase the network lifetime by more than 11%, also increase energy efficiency as much as 12.39%.

7. Hu X, Wang H, Tang X. Cyber-Physical Control for Energy-Saving Vehicle Following with Connectivity. IEEE Transactions on Industrial Electronics. 2017 May 11.

ABSTRACT: This article aims to develop an optimal look-ahead control framework to maximize car-following fuel economy, while fulfilling requirements of inter-vehicle safety. Three original contributions make this work distinctive from the existing relevant literature. First, a model predictive fuel-optimal controller is constructed to optimize the vehicle speed and continuously variable transmission (CVT) gear ratio. The controller leverages state trajectories of the leading vehicle transmitted via Vehicle-to-Vehicle/Vehicle-to-Infrastructure (V2V/V2I) communication. How operating conditions affect the engine efficiency and CVT efficiency is explicitly taken into account. Second, the controller is sufficiently evaluated in a variety of traffic flows, such as cruising, urban, and highway-like driving, and is compared with a short-sighted alternative without V2V/V2I connectivity. Finally, we further demonstrate the advantages of the proposed scheme by a comparison with two existing benchmark controllers.

8. Cassettari L, Bendato I, Mosca M, Mosca R. Energy Resources Intelligent Management using on line real-time simulation: A decision support tool for sustainable manufacturing. Applied Energy. 2017 Mar 15;190:841-51.

ABSTRACT: At a historic time when the eco-sustainability of industrial manufacturing is considered one of the cornerstones of relations between people and the environment, the use of energy from Renewable Energy Sources (RES) has become a fundamental element of this new vision. After years of vain attempts to hammer out an agreement to significantly reduce CO2emissions produced by the burning of fossil fuels, a binding global accord was finally reached (Paris December 2015 – New York April 2016). As we know, however, some of the most commonly-used RES, such as solar or wind, present the problem of discontinuity in energy production due to the variability of weather and climatic conditions. For this reason, the authors thought it appropriate to study a new methodology capable of marrying industrial users’ instantaneous need for energy with the production capacity of Renewable Energy Sources, supplemented, when necessary, by energy created through self-production and possibly acquired from third-party suppliers. All of this in order to minimize CO2 emissions and company energy costs. Given the massive presence of stochastic and sometimes aleatory elements, for the proposed energy management model we have used both Monte Carlo simulation and on-line real-time Discrete Event Simulation (DES), as well as appropriate predictive algorithms. A test conducted on a tannery located in southern Italy, equipped with a 700 KWp photovoltaic installation, showed extremely interesting results, both economically and environmentally. In particular the application of the model permitted an annual savings of several hundreds of thousands of euros in energy costs and a comparable parallel reduction of CO2 emissions. The systematic use of the proposed approach, gradually expanded to other manufacturing sectors, could result in very consistent benefits for the entire industrial system.

9. Delgado-Gomes V, Oliveira-Lima JA, Martins JF. Energy consumption awareness in manufacturing and production systems. International Journal of Computer Integrated Manufacturing. 2017 Jan 2;30(1):84-95.

ABSTRACT: Industry is a major energy consumer and therefore, manufacturers are continuously trying to reduce manufacturing costs. The improvement of energy usage in manufacturing has been receiving large attention during the last years due to the increasing energy cost and environmental awareness. However, it is still difficult to identify the most energy demanding manufacturing phases in the whole manufacturing process, and which ones can and should be energetically improved. This paper proposes a standard-based infrastructure to collect and monitor energy data in real time for manufacturing and production systems, along with a manufacturing energy management system (MEMS). The collected data improves energy consumption awareness and allows the MEMS to make further analysis and to identify where to take actions in the manufacturing process in order to reduce the energy consumption. The developed MEMS enables energy consumption monitoring with different granularities, depending on where the monitoring devices are placed, that is, per machine, per product process, or for the factory as a whole. The developed MEMS also includes the control of energy related devices to support energy-based decisions and enables the interoperability with a generic manufacturing management system simulation software that works with real monitored energy data in order to test and validate modifications made to the manufacturing process.

10. Li YC, Hong SH. Real-Time Demand Bidding for Energy Management in Discrete Manufacturing Facilities. IEEE Transactions on Industrial Electronics. 2017 Jan;64(1):739-49.

ABSTRACT: During periods of power system stress, demand bidding (DB) programs encourage large electricity consumers to submit curtailment capacity bids and carry out load reduction, in return for financial rewards. In this paper, a real-time DB (RT-DB) program with applications in discrete manufacturing facilities is considered. A discrete manufacturing production model is constructed and an automated RT-DB algorithm is designed. An optimization problem, where the objective is to maximize the profits for manufacturers, is formulated. Solving this problem enables the RT-DB algorithm to automatically generate optimal load-reduction bids with adjusted production and energy plans. A case study is described, which shows that the proposed algorithm reduced the load demand during an RT-DB event, increasing the manufacturer’s profits. Furthermore, the relationship between the incentive rate and the demand elasticity of the consumer, as well as the production volume and profits is described.



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