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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (1 January 2020) | Viewed by 56921

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Prof. Dr. Konstantinos Salonitis

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Sustainable Manufacturing Systems Centre, School of Aerospace Transport and Manufacturing, Cranfield University, Cranfield MK43 0AL, UK
Interests: sustainable manufacturing systems; green manufacturing; environmental impact assessment; simulation; modelling
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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

Prof. Dr. Konstantinos Salonitis
Guest Editor

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Published Papers (11 papers)

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Editorial

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5 pages, 178 KiB

Open AccessEditorial

Energy Efficiency of Manufacturing Processes and Systems—An Introduction

by Konstantinos Salonitis

Energies 2020, 13(11), 2885; https://doi.org/10.3390/en13112885 - 5 Jun 2020

Cited by 2 | Viewed by 2241

Abstract

This Special Issue of Energies was devoted to the topic of “Energy Efficiency of Manufacturing Processes and Systems”. It attracted significant attention of scholars, practitioners, and policy-makers from all over the world. Eighteen papers on this topic were submitted between 2018 and 2020, and a total of 10 papers were published. Main topics included the energy efficiency improvement in both the manufacturing process and system levels. Furthermore, new methodologies and analysis approaches in developing energy efficiency were presented. Full article

(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems)

Research

Jump to: Editorial

19 pages, 5737 KiB

Open AccessArticle

Sustainability Assessment for Manufacturing Operations

by Prateek Saxena, Panagiotis Stavropoulos, John Kechagias and Konstantinos Salonitis

Energies 2020, 13(11), 2730; https://doi.org/10.3390/en13112730 - 29 May 2020

Cited by 76 | Viewed by 5873

Abstract

Sustainability is becoming more and more important as a decision attribute in the manufacturing environment. However, quantitative metrics for all the aspects of the triple bottom line are difficult to assess. Within the present paper, the sustainability metrics are considered in tandem with other traditional manufacturing metrics such as time, flexibility, and quality and a novel framework is presented that integrates information and requirements from Computer-Aided Technologies (CAx) systems. A novel tool is outlined for considering a number of key performance indicators related to the triple bottom line when deciding the most appropriate process route. The implemented system allows the assessment of alternative process plans considering the market demands and available resources. Full article

(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems)

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20 pages, 7648 KiB

Open AccessArticle

Parametric Modelling and Multi-Objective Optimization of Electro Discharge Machining Process Parameters for Sustainable Production

by Misbah Niamat, Shoaib Sarfraz, Wasim Ahmad, Essam Shehab and Konstantinos Salonitis

Energies 2020, 13(1), 38; https://doi.org/10.3390/en13010038 - 19 Dec 2019

Cited by 19 | Viewed by 4701

Abstract

Electro Discharge Machining (EDM) can be an element of a sustainable manufacturing system. In the present study, the sustainability implications of EDM of special-purpose steels are investigated. The machining quality (minimum surface roughness), productivity (material removal rate) improvement and cost (electrode wear rate) minimization are considered. The influence and correlation of the three most important machining parameters including pulse on time, current and pulse off time have been investigated on sustainable production. Empirical models have been established based on response surface methodology for material removal rate, electrode wear rate and surface roughness. The investigation, validation and deeper insights of developed models have been performed using ANOVA, validation experiments and microstructure analysis respectively. Pulse on time and current both appeared as the prominent process parameters having a significant influence on all three measured performance metrics. Multi-objective optimization has been performed in order to achieve sustainability by establishing a compromise between minimum quality, minimum cost and maximum productivity. Sustainability contour plots have been developed to select suitable desirability. The sustainability results indicated that a high level of 75.5% sustainable desirability can be achieved for AISI L3 tool steel. The developed models can be practiced on the shop floor practically to attain a certain desirability appropriate for particular machine limits. Full article

(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems)

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25 pages, 40846 KiB

Open AccessArticle

Experimental Investigation of Productivity, Specific Energy Consumption, and Hole Quality in Single-Pulse, Percussion, and Trepanning Drilling of IN 718 Superalloy

by Shoaib Sarfraz, Essam Shehab, Konstantinos Salonitis and Wojciech Suder

Energies 2019, 12(24), 4610; https://doi.org/10.3390/en12244610 - 4 Dec 2019

Cited by 8 | Viewed by 4997

Abstract

Laser drilling is a high-speed process that is used to produce high aspect ratio holes of various sizes for critical applications, such as cooling holes in aero-engine and gas turbine components. Hole quality is always a major concern during the laser drilling process. Apart from hole quality, cost and productivity are also the key considerations for high-value manufacturing industries. Taking into account the significance of improving material removal quantity, energy efficiency, and product quality, this study is performed in the form of an experimental investigation and multi-objective optimisation for three different laser drilling processes (single-pulse, percussion, and trepanning). A Quasi-CW fibre laser was used to produce holes in a 1 mm thick IN 718 superalloy. The impacts of significant process parameters on the material removal rate (MRR), specific energy consumption (SEC), and hole taper have been discussed based on the results collected through an experimental matrix that was designed using the Taguchi method. The novelty of this work focuses on evaluating and comparing the performance of laser drilling methods in relation to MRR, SEC, and hole quality altogether. Comparative analysis revealed single-pulse drilling as the best option for MRR and SEC as the MRR value reduces with percussion and trepanning by 99.70% and 99.87% respectively; similarly, percussion resulted in 14.20% higher SEC value while trepanning yielded a six-folds increase in SEC as compared to single-pulse drilling. Trepanning, on the other hand, outperformed the rest of the drilling processes with 71.96% better hole quality. Moreover, optimum values of parameters simultaneously minimising SEC and hole taper and maximising MRR are determined using multi-objective optimisation. Full article

(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems)

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28 pages, 6223 KiB

Open AccessArticle

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

by Miriam Benedetti, Francesca Bonfà, Vito Introna, Annalisa Santolamazza and Stefano Ubertini

Energies 2019, 12(20), 3935; https://doi.org/10.3390/en12203935 - 17 Oct 2019

Cited by 25 | Viewed by 4676

Abstract

Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability. Full article

(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems)

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12 pages, 1111 KiB

Open AccessArticle

Impact of Structural Changes on Energy Efficiency of Finnish Pulp and Paper Industry

by Satu Kähkönen, Esa Vakkilainen and Timo Laukkanen

Energies 2019, 12(19), 3689; https://doi.org/10.3390/en12193689 - 26 Sep 2019

Cited by 16 | Viewed by 3647

Abstract

A key challenge in prevention of global warming is how to increase energy efficiency, to be able to deal with increased fossil CO₂ emissions from rising energy usage. Increasing energy efficiency will decrease energy usage and is in a key role in emission mitigation. The focus is the pulp and paper industry, which is energy-intensive. Development of industrial energy efficiency has been studied before but the role of industrial transformation is still mostly unknown. The knowledge must be improved, to be able to predict future developments in the most effective way. In this research, impact of various production unit closures and start-ups on energy efficiency of the Finnish pulp and paper industry were studied utilizing statistical analysis. Results indicate that about 20% of the Finnish pulp and paper industry energy efficiency improvement between 2011 and 2017 is caused by the major structural changes. The rest, 80% of the progress, was mainly due to improved technology and more optimal operational modes. Additional findings suggest that modern mill start-ups have a significantly greater potential to reduce energy consumption than old mill closures. Full article

(This article belongs to the Special Issue Energy Efficiency of Manufacturing Processes and Systems)

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23 pages, 11078 KiB

Open AccessArticle

Life-Cycle and Energy Assessment of Automotive Component Manufacturing: The Dilemma Between Aluminum and Cast Iron

by Konstantinos Salonitis, Mark Jolly, Emanuele Pagone and Michail Papanikolaou

Energies 2019, 12(13), 2557; https://doi.org/10.3390/en12132557 - 3 Jul 2019

Cited by 49 | Viewed by 12873

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 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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17 pages, 2138 KiB

Open AccessArticle

Energy Saving Operation of Manufacturing System Based on Dynamic Adaptive Fuzzy Reasoning Petri Net

by Junfeng Wang, Zicheng Fei, Qing Chang and Shiqi Li

Energies 2019, 12(11), 2216; https://doi.org/10.3390/en12112216 - 11 Jun 2019

Cited by 26 | Viewed by 3436

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 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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19 pages, 4679 KiB

Open AccessArticle

Deep Learning Approach of Energy Estimation Model of Remote Laser Welding

by Jumyung Um, Ian Anthony Stroud and Yong-keun Park

Energies 2019, 12(9), 1799; https://doi.org/10.3390/en12091799 - 11 May 2019

Cited by 10 | Viewed by 4418

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 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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22 pages, 4155 KiB

Open AccessArticle

Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling

by Aqib Mashood Khan, Muhammad Jamil, Konstantinos Salonitis, Shoaib Sarfraz, Wei Zhao, Ning He, Mozammel Mia and GuoLong Zhao

Energies 2019, 12(4), 710; https://doi.org/10.3390/en12040710 - 21 Feb 2019

Cited by 75 | Viewed by 5625

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 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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15 pages, 3924 KiB

Open AccessArticle

Minimising Non-Processing Energy Consumption and Tardiness Fines in a Mixed-Flow Shop

by Chen Peng, Tao Peng, Yi Zhang, Renzhong Tang and Luoke Hu

Energies 2018, 11(12), 3382; https://doi.org/10.3390/en11123382 - 3 Dec 2018

Cited by 9 | Viewed by 3150

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) 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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