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Special Issue "Energy Efficiency of Manufacturing Processes and Systems "

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editor

Guest Editor
Dr. Konstantinos Salonitis

Manufacturing Department, Cranfield University, UK
Website | E-Mail
Interests: Industrial Sustainability, Simulation and modelling of manufacturing processes, Energy efficiency of manufacturing systems, Environmental impact assessment of manufacturing processes, Rapid Manufacturing

Special Issue Information

Dear Colleagues,

The availability and affordability of energy affect the whole life cycle of a product and subsequently the production phase as well. Manufacturing activities are responsible for one third of the global total energy consumption and CO2 emissions. Thus producing with higher energy efficiency has been the focus of research in recent years and is nowadays considered one of the key decision-making attributes for manufacturing. This Special Issue considers the energy efficiency of both manufacturing processes and systems. Papers are particularly invited in the following areas:

  • Methods for the measurement of energy efficiency, including obtaining performance data from older production technologies
  • Tools and techniques for the analysis and development of improvements with regards to energy consumption
  • Tools and techniques for the modelling and simulation of energy efficiency for both manufacturing processes and systems
  • Continuous improvement methodologies and cases
  • Case studies on the management of such systems and what practices are necessary to maintain
  • Green and lean manufacturing

Dr. Konstantinos Salonitis
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

Open AccessArticle
Life-Cycle and Energy Assessment of Automotive Component Manufacturing: The Dilemma Between Aluminum and Cast Iron
Energies 2019, 12(13), 2557; https://doi.org/10.3390/en12132557
Received: 6 June 2019 / Accepted: 30 June 2019 / Published: 3 July 2019
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Abstract
Considering the manufacturing of automotive components, there exists a dilemma around the substitution of traditional cast iron (CI) with lighter metals. Currently, aluminum alloys, being lighter compared to traditional materials, are considered as a more environmentally friendly solution. However, the energy required for [...] Read more.
Considering the manufacturing of automotive components, there exists a dilemma around the substitution of traditional cast iron (CI) with lighter metals. Currently, aluminum alloys, being lighter compared to traditional materials, are considered as a more environmentally friendly solution. However, the energy required for the extraction of the primary materials and manufacturing of components is usually not taken into account in this debate. In this study, an extensive literature review was performed to estimate the overall energy required for the manufacturing of an engine cylinder block using (a) cast iron and (b) aluminum alloys. Moreover, data from over 100 automotive companies, ranging from mining companies to consultancy firms, were collected in order to support the soundness of this investigation. The environmental impact of the manufacturing of engine blocks made of these materials is presented with respect to the energy burden; the “cradle-to-grave approach” was implemented to take into account the energy input of each stage of the component life cycle starting from the resource extraction and reaching to the end-of-life processing stage. Our results indicate that, although aluminum components contribute toward reduced fuel consumption during their use phase, the vehicle distance needed to be covered in order to compensate for the up-front energy consumption related to the primary material production and manufacturing phases is very high. Thus, the substitution of traditional materials with lightweight ones in the automotive industry should be very thoughtfully evaluated. Full article
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
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Open AccessArticle
Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net
Energies 2019, 12(11), 2216; https://doi.org/10.3390/en12112216
Received: 6 May 2019 / Revised: 5 June 2019 / Accepted: 10 June 2019 / Published: 11 June 2019
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Abstract
The energy efficient operation of a manufacturing system is important for sustainable development of industry. Apart from the device and process level, energy saving methods at the system level has attracted increasing attention with the rapid growth of the industrial Internet of things [...] Read more.
The energy efficient operation of a manufacturing system is important for sustainable development of industry. Apart from the device and process level, energy saving methods at the system level has attracted increasing attention with the rapid growth of the industrial Internet of things technology, which makes it possible to sense and collect real-time data from the production line and provide more opportunities for online control for energy saving purposes. In this paper, a dynamic adaptive fuzzy reasoning Petri net is proposed to decide the machine energy saving state considering the production information of a discrete stochastic manufacturing system. Fuzzy knowledge for energy saving operations of a machine is represented in weighted fuzzy production rules with certain values. The rules describe uncertain, imprecise, and ambiguous knowledge of machine state decisions. This makes an energy saving sleep decision in advance when a machine has the inclination of starvation or blockage, which is based on the real-time production rates and level of connected buffers. A dynamic adaptive fuzzy reasoning Petri net is formally defined to implement the reasoning process of the machine state decision. A manufacturing system case is used to demonstrate the application of our method and the results indicate its effectiveness for energy saving operation purposes. Full article
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
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Open AccessArticle
Deep Learning Approach of Energy Estimation Model of Remote Laser Welding
Energies 2019, 12(9), 1799; https://doi.org/10.3390/en12091799
Received: 22 March 2019 / Revised: 28 April 2019 / Accepted: 1 May 2019 / Published: 11 May 2019
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Abstract
Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average [...] Read more.
Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods’ comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods. Full article
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
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Open AccessArticle
Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling
Energies 2019, 12(4), 710; https://doi.org/10.3390/en12040710
Received: 16 December 2018 / Revised: 4 February 2019 / Accepted: 17 February 2019 / Published: 21 February 2019
Cited by 5 | PDF Full-text (4155 KB) | HTML Full-text | XML Full-text
Abstract
Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel [...] Read more.
Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality. Full article
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
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Open AccessArticle
Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop
Energies 2018, 11(12), 3382; https://doi.org/10.3390/en11123382
Received: 2 November 2018 / Revised: 18 November 2018 / Accepted: 28 November 2018 / Published: 3 December 2018
Cited by 1 | PDF Full-text (3924 KB) | HTML Full-text | XML Full-text
Abstract
To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) [...] Read more.
To meet the increasingly diversified demand of customers, more mixed-flow shops are employed. The flexibility of mixed-flow shops increases the difficulty of scheduling. In this paper, a mixed-flow shop scheduling approach (MFSS) is proposed to minimise the energy consumption and tardiness fine (TF) of production with a special focus on non-processing energy (NPE) reduction. The proposed approach consists of two parts: firstly, a mathematic model is developed to describe how NPE and TF can be determined with a specific schedule; then, a multi-objective evolutionary algorithm with multi-chromosomes (MCEAs) is developed to obtain the optimal solutions considering the NPE-TF trade-offs. A deterministic search method with boundary (DSB) and a non-dominated sorting genetic algorithm (NSGA) are employed to validate the developed MCEA. Finally, a case study on an extrusion die mixed-flow shop is performed to demonstrate the proposed approach in industrial practice. Compared with three traditional scheduling approaches, the better performance of the MFSS in terms of computational time and solution quality could be demonstrated. Full article
(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems )
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