Manufacturing Energy Consumption Research Articles

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Modelling and Prediction of Process Parameters with Low Energy Consumption in Wire Arc Additive Manufacturing Based on Machine Learning

Energy-aware cloud manufacturing service selection and scheduling optimization

ABSTRACT Cloud Manufacturing Service Selection and Scheduling (CMSSS) is vital for optimizing resource allocation and meeting task requirements. However, inattention to the preheating process of manufacturing equipment has resulted in wasted energy. To reduce manufacturing energy consumption and ensure the Quality of Service (QoS), this paper establishes a bi-level programming model for CMSSS, quantifies the preheating energy consumption of manufacturing equipment by task cohesion, and proposes an energy-aware cloud manufacturing service selection and scheduling approach. The approach selects the service composition for a task from candidate service sets, schedules subtasks to avoid service occupancy conflicts and maximises task cohesion to reduce the preheating energy consumption of manufacturing equipment by Energy-aware Scheduling Generation Scheme (ESGS). Finally, it integrates ESGS into Non-dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal task execution solution. Experimental results show that ESGS has a better Pareto front than the previous Feasible Scheduling Generation Scheme (FSGS) under seven types of QoS weights. With almost the same QoS satisfaction level, ESGS consumes, on average, 2% to 8% less energy than FSGS for preheating manufacturing equipment. In cloud manufacturing scenarios with preheating processes, ESGS can meet the QoS requirements of demanders as FSGS but with a better energy economy.

Hierarchical energy signatures using machine learning for operational visibility and diagnostics in automotive manufacturing

Manufacturing energy consumption data contains important process signatures required for operational visibility and diagnostics. These signatures may be of different temporal scales, ranging from monthly to sub-second resolutions. We introduce a hierarchical machine learning approach to identify automotive process signatures from paint shop electricity consumption data at varying temporal scales (weekly and daily). A Multi-Layer Perceptron (MLP), a Convolutional Neural Network (CNN), and Principal Component Analysis (PCA) combined with Logistic Regression (LR) are used for the analysis. We validate the utility of the developed algorithms with subject matter experts for (i) better operational visibility, and (ii) identifying energy saving opportunities.

Data-driven hybrid algorithm with multi-evolutionary sampling strategy for energy-saving buffer allocation in green manufacturing

Buffer allocation, which is an important research topic in manufacturing system design, typically focuses on system performance and cost. However, few previous studies have been performed to investigate energy-saving buffer allocation, which can decrease operational energy consumption in green manufacturing. Furthermore, the computational efficiency of solving the buffer allocation problem requires further investigation. This paper proposes a data-driven hybrid algorithm based on multi-evolutionary sampling strategies for solving energy-saving buffer allocation that can maximize the throughput rate and minimize energy consumption. Two evolutionary sampling strategies, that is, global search and surrogate-assisted local search, are integrated to balance exploitation and exploration. In addition, a database containing historical data pertaining to buffer allocation solutions is used to develop surrogate models that can rapidly predict the throughput and energy consumption and improve the evaluation efficiency of the local search strategy. Experimental results demonstrate the efficacy of the proposed algorithm. This study contributes to an efficient buffer allocation and presents a new perspective on energy-saving measures for green manufacturing designs.

An Energy-Efficient Unrelated Parallel Machine Scheduling Problem with Batch Processing and Time-of-Use Electricity Prices

The extensive consumption of energy in manufacturing has led to a large amount of greenhouse gas emissions that have caused an enormous effect on the environment. Therefore, investigating how to reduce energy consumption in manufacturing is of great significance to cleaner production. This paper considers an energy-conscious unrelated parallel batch processing machine scheduling problem under time-of-use (TOU) electricity prices. Under TOU, electricity prices vary for different periods of a day. This problem is grouping jobs into batches, assigning the batches to machines and allocating time to the batches so as to minimize the total electricity cost. A mixed-integer linear programming model and two groups of heuristics are proposed to solve this problem. The first group of heuristics first forms batches, assigns the batches to machines and finally allocates time to the batches, while the second group of heuristics first assigns jobs to machines, batches the jobs on each machine and finally allocates time to each batch. The computational results show that the SPT-FBLPT-P1 heuristic in the second group can provide high-quality solutions for large-scaled instances in a short time, in which the jobs are assigned to the machines based on the shortest processing time rule, the jobs on each machine are batched following the full-batch longest processing time algorithm, and the time is allocated to each batch following an integer programming approach. The MDEC-FBLPT-P1 heuristic that uses the minimum difference of the power consumption algorithm to assign the jobs also performed well.

EcoPrintAnalyzer: Assessing Sustainability in Material Extrusion Additive Manufacturing for Informed Decision-Making

Sustainability is fundamental in the field of additive manufacturing (AM) for improving eco-consciousness and driving evolution toward environmentally responsible production methods. Compared to traditional manufacturing processes, AM technologies can be more resource-efficient and offer innovative solutions for creating eco-friendly processes and products. Nevertheless, there is significant potential for improvement in additive manufacturing sustainability. The key factors driving this improvement include design optimization and increased awareness. Designers and engineers can create designs that optimize material efficiency and reduce support structures. Raising awareness and educating stakeholders about the environmental benefits of AM can promote responsible choices throughout the industrial process. The development of a tool to assess the environmental impact of AM processes could be a significant contribution to advancing sustainability in the AM field. The EcoPrintAnalyzer, introduced as a complementary plugin for UltiMaker Cura, offers data on the equivalent carbon dioxide footprint and energy consumption in material extrusion additive manufacturing. This tool facilitates informed decision-making regarding materials, designs, and settings, enabling users to optimize their AM processes for reduced waste and enhanced energy efficiency. Beyond aiding decision-making, the EcoPrintAnalyzer fosters environmental consciousness and encourages the adoption of sustainable practices within the AM ecosystem. The efficacy of the tool is demonstrated through the 3DBenchy model case study, showcasing its intuitive interface and seamless integration within the AM process workflow for immediate and comparative environmental impact assessments across different process configurations.

Study on energy use and carbon emission from manufacturing of OPC and blended cements in India.

The demand for production of cements is ever increasing to meet the infrastructure development globally. The energy and emission factors available for cements in most of the life cycle assessment (LCA) databases may not exactly suit for all the geographical locations. The main challenge in Indian scenario is the absence of database for LCA study. This study attempts to develop the energy and emission factors for the manufacturing of cements in Indian context. In the present study, five different cement manufacturing plants located in north, south, east, west and central zones of India are considered to assess the energy dissipation and carbon dioxide emission involved during the production of ordinary Portland cement (OPC). Most of the data is collected from the field, so that the energy and emission factors determined will be suitable for the zonal study. The study is then extended to assess the energy consumption and carbon dioxide emission for three blended cements, viz. Portland Pozzolan cement (PPC), Portland slag cement (PSC) and composite cement (CC) with permissible known replacement levels of fly ash, granulated blast furnace slag and both fly ash and slag, respectively. The average energy use and carbon emission is found to be on higher side in India by 15.14% and 12.64%, respectively, compared to other countries in manufacturing of cements. An average energy consumption in manufacturing of PPC, PSC and CC is found to be respectively 24.5%, 35.3% and 43.13% less compared to that of OPC. The CO2 emission intensity for OPC is found to vary between 893 and 940kg/tonne of cement from five different zones, and an average of respectively 24.8%, 40.97% and 47.18% lower CO2 emission was observed from PPC, PSC and CC compared to OPC. From the inventory results, CC has proven to be a more sustainable cement with low energy consumption and lower CO2 emission compared to other cements.

Bringing Light into the Dark-Overview of Environmental Impacts of Carbon Fiber Production and Potential Levers for Reduction.

Carbon fibers (CFs) are a crucial material for lightweight structures with advanced mechanical performance. However, there is still a paucity of detailed understanding regarding the environmental impacts of production. Previously, mostly singled-out scenarios for CF production have been assessed, often based on scarce transparent inventory data. To expand the current knowledge and create a robust database for future evaluation, a life cycle assessment (LCA) was carried out. To this end, a detailed industry-approved LCI is published, which also proved plausible against the literature. Subsequently, based on a global scenario representing the market averages for precursor and CF production, the most relevant contributors to climate change (EF3.1 climate change, total) and the depletion of fossil energy carriers (EF3.1 resource use, fossil) were identified. The energy consumption in CF manufacturing was found to be responsible for 59% of the climate change and 48% of the fossil resource use. To enable a differentiated discussion of manufacturing locations and process energy consumption, 24 distinct scenarios were assessed. The findings demonstrate the significant dependence of the results on the scenarios' boundary conditions: climate change ranges from 13.0 to 34.1 kg CO2 eq./kg CF and resource use from 262.3 to 497.9 MJ/kg CF. Through the investigated scenarios, the relevant reduction potentials were identified. The presented results help close an existing data gap for high-quality, regionalized, and technology-specific LCA results for the production of CF.

Development of asymmetric ceramic membranes for dairy wastewater treatment – A comparison between co-sintering and conventional firing process

Ceramic membranes have high potential for wastewater recovery. However, the high cost of raw materials and the high energy consumption for manufacturing, limit their use. This study aimed to prepare ceramic membranes using kaolin and alumina, emphasizing firing in a single step. On the support, corn starch was used as a porogenic agent and to evaluate the performance of the membrane, a simulation was used for the dairy industry effluent. The supports were manufactured by the extrusion process, which were sintered (1100, 1200 and 1300 °C), for the analysis of their properties. A suspension was applied to the inner surface of the green supports, and the cosintering process was employed. The supports containing 15 % corn starch sintered at 1100 °C showed high porosity (48 %), pure water permeate flux of 1385 L/h.m2 at 2 bar and mechanical strength of 8 MPa. The active layer exhibited pore sizes that indicate that the membrane can be used in the microfiltration process, with a permeate flux of 897 L/h.m2. The membrane showed adequate microfiltration performance for the removal of components present in the milk solution and responsible for the turbidity of the permeate, with a permeate flux stabilized at 30 L/h.m2.

Printing, Debinding and Sintering of 15-5PH Stainless Steel Components by Fused Deposition Modeling Additive Manufacturing.

Metal FDM technology overcomes the problems of high cost, high energy consumption and high material requirements of traditional metal additive manufacturing by combining FDM and powder metallurgy and realizes the low-cost manufacturing of complex metal parts. In this work, 15-5PH stainless steel granules with a powder content of 90% and suitable for metal FDM were developed. The flowability and formability of the feedstock were investigated and the parts were printed. A two-step (solvent and thermal) debinding process is used to remove the binder from the green part. After being kept at 75 °C in cyclohexane for 24 h, the solvent debinding rate reached 98.7%. Following thermal debinding, the material's weight decreased by slightly more than 10%. Sintering was conducted at 1300 °C, 1375 °C and 1390 °C in a hydrogen atmosphere. The results show that the shrinkage of the sintered components in the X-Y-Z direction remains quite consistent, with values ranging from 13.26% to 19.58% between 1300 °C and 1390 °C. After sintering at 1390 °C, the material exhibited a relative density of 95.83%, a hardness of 101.63 HRBW and a remarkable tensile strength of 770 MPa. This work realizes the production of metal parts using 15-5PH granules' extrusion additive manufacturing, providing a method for the low-cost preparation of metal parts. And it provides a useful reference for the debinding and sintering process settings of metal FDM. In addition, it also enriches the selection range of materials for metal FDM.

Continuous Assessment of the Environmental Impact and Economic Viability of Decarbonization Improvements in Cement Production

Growing awareness of the importance of mitigating climate change is driving research efforts toward developing economically viable technologies for reducing greenhouse gas emissions. The high energy consumption and carbon-intensive nature of cement manufacturing make it worthwhile to examine the environmental and economic characteristics of process improvements in cement production. This study examines the environmental impact of cement production and its economic considerations and demonstrates an IoT-inspired deployment framework for continuously assessing these. It contributes a practical approach to integrating sustainability into cement manufacturing and analyzes four different scenarios from a combination of two cement types (ordinary Portland cement, Portland-limestone cement) and two energy sources for thermal heating (coal, dried biosolids). It indicates that increased production and adoption of blended cement that has up to 15% limestone as an alternative to ordinary Portland cement can significantly reduce climate change effects from cement production (6.4% lower carbon footprint). In addition, significant emission reduction is possible with the use of waste from sewage sludge as a combustion fuel for heating in the cement production process (7.9% reduction compared with baseline). The information on environmental and financial trade-offs helps informed decisions on cement production improvements and can potentially contribute to greenhouse gas reduction targets.

A Learning-Based Memetic Algorithm for Energy-Efficient Flexible Job-Shop Scheduling With Type-2 Fuzzy Processing Time

Green flexible job shop scheduling problem (FJSP) aims to improve profit and reduce energy consumption for modern manufacturing. Meanwhile, FJSP with type-2 fuzzy processing time is proposed to predict the uncertainty in timing constrain for better simulating the practical production. This study addresses the multi-objective energy-efficient flexible job shop scheduling problem with type-2 processing time (ET2FJSP), where the minimization of makespan and total energy consumption are considered simultaneously. The previous studies do not propose the model verification and energy-saving strategy. Moreover, the best parameters required by an algorithm in different stage is different. Therefore, we propose a mixed integer linear programming model and design a learning-based reference vector memetic algorithm (LRVMA). Its main features are: i) four problem-specific initial rules are presented for initialization to generate diverse solutions; ii) four problem-specific local search methods are incorporated to enhance the exploitation; iii) an effective solution selection method depending on the Tchebycheff decomposition strategy is utilized to balance the convergence and diversity; iv) a reinforcement learning-based parameter selection strategy is proposed to improve the diversity of nondominated solutions, and v) an energy-saving strategy is designed to reduce energy consumption. To verify the effectiveness of LRVMA, it is compared against other related algorithms. The results demonstrate that LRVMA outperforms the compared algorithms for solving ET2FJSP.

LCA approach for environmental impact assessment within the maritime industry: Re-design case study of yacht’s superstructure

Sustainable development, one of the main challenges of our time, is a policy focused on the perfect balance between three fundamental pillars: environmental, economic and social sustainability. As regards the environmental protection, the Life Cycle Assessment (LCA) methodology allows to evaluate the sustainability profile of the overall Life-Cycle (LC) of products, processes and services, based on an inventory (in terms of materials/energy consumption and emissions/waste production) referred to all LC stages. The paper describes an application of LCA in the maritime transportation field, after a careful analysis of the state of the art. In particular, the case study consists in the environmental comparison of two alternative design solutions for the superstructure of a Azimut-Benetti yacht, designed by Corporate R&D department and manufactured in one of Benetti botyards. The competing construction options are a Glass Fiber reinforced Vinylester-isophthalic Resin (GFVR) and a Carbon Fiber reinforced Epoxy Resin (CFER) component, and they are assessed in terms of Global Warming Potential through the CML2001 Life Cyle Impact Assessment (LCIA) method. The study takes into account the entire LC of the superstructure component, divided into production (including raw materials, manufacturing and transportations), use (including both fuel consumption and exhaust air emissions) and End-of-Life (EoL). The Life Cycle Inventory (LCI) is mainly based on primary data (materials and energy consumption for manufacturing) directly provided by the construction company; missing data are retrieved from secondary sources (literature and LCI database provided by the GaBi6 environmental software). Results show that, despite the higher impact in production stage, the innovative solution allows achieving a significant quota of GWP over the entire LC (more than 16%), which is mainly associated with decreased amount of fuel needed and lowered CO2 exhaust emissions during operation. The sensitivity analysis reveals that the environmental advantage provided by the CFER design becomes bigger as both component life-time and yacht consumption increase.

Roll-to-roll solvent-free manufactured electrodes for fast-charging batteries

Roll-to-roll solvent-free manufactured electrodes for fast-charging batteries

Selective LASER melting part quality prediction and energy consumption optimization

Selective LASER Melting (SLM) popularity is increasing because of its ability to quickly produce components with acceptable quality. The SLM process parameters, such as LASER power and scan speed, play a significant role in assuring the quality of customized SLM products. Therefore, the process parameters must be tuned appropriately to achieve high-quality customized products. Most existing methods for adjusting the SLM’s parameters use multiple inputs and one or two outputs to develop a model for achieving their desired quality. However, the number of the model’s input and output parameters to be considered can be increased to achieve a more comprehensive model. Furthermore, energy consumption is also a factor that should be considered when adjusting input parameters. This paper presents a multi-inputs-multi-outputs (MIMO) artificial neural network model to predict the SLM product qualities. We also try to combine training data from different sources to achieve a more general model that can be used in real applications by industries. The model inputs are LASER power, scan speed, overlap rate, and hatch distance. Moreover, four critical product quality measures: relative density, hardness, tensile strength, and porosity, are used as the model’s outputs. After finding a proper model, an energy optimization method is developed using the genetic algorithm in this paper. The objective of the optimization is to minimize the energy consumption of SLM manufacturing with a less compromised output quality. The results of this study can be used in the industry to decrease energy consumption while maintaining the required quality.

Energy efficiency in industrial environments: an updated review and a new research agenda

Nowadays, discussions about the use of energy efficiency in industries have intensified due to the need to optimize the resources used, especially the high cost in the production process. In this context, a literature review and a research agenda on energy efficiency in industrial environments can assist in the search for solutions and challenges to reduce energy consumption in manufacturing, as well as identify future trends for the topic. Therefore, this article proposes to update the energy efficiency research agenda in industrial environments, through a systematic review of the literature, between the years 2015 and 2020, in search of articles published in journals with current practices and the new strategies used in energy management. For the review of the research agenda, one hundred and eighty-five articles were selected, which were read and used in the bibliometric analysis, which was compared to the results obtained and observed in the base article for this update, thus helping in directing the creation of an agenda for studies and practices in energy efficiency in manufacturing environments.

Technical feasibility of powering U.S. manufacturing with rooftop solar PV

The use of renewable electricity is vital for the decarbonization of industry. Industrial firms source renewables through off-site power purchase agreements or on-site installations, though the latter currently supplies <0.1% of industrial electricity demand in the U.S. Manufacturing buildings typically have large, flat rooftops that are ideal for solar photovoltaic (PV) arrays. This study investigates the feasibility of using rooftop solar PV to cover the net annual electricity needs of industry across all U.S. states and manufacturing sectors. Modeled electricity supply intensity for solar PV arrays is compared with the electricity demand per unit of floorspace for average manufacturing buildings derived from the U.S. Department of Energy Manufacturing Energy Consumption Survey. Results show that, depending on geographical location, rooftop solar PV can completely fulfill the electricity requirements of 5%–35% of manufacturing sectors considered on a net annual basis (assuming net metering). Furniture, textiles, and apparel manufacturing can be powered through on-site means in nearly every location, representing 2% of U.S. manufacturing electricity use and 6% of floorspace. Considering seasonal potential during summer months expands the list of feasible sites, particularly in the U.S. Southwest. Compared to off-site sourcing of renewable energy, pursuing on-site PV installations can also enable manufacturers to maintain limited operations during periods of grid disruption, especially when coupled with on-site energy storage. Overall, the results indicate a substantial physical opportunity for industrial firms to expand rooftop solar PV from currently low levels to help meet decarbonization goals.

A novel quantifiable approach of estimating energy consumption, carbon emissions and cost factors in manufacturing of bearing steel based on triple bottom-line approach

The theory of “triple bottom line (TBL)” widens the traditional measures of corporate success to account for their positive effects on three additional dimensions: society, the natural world, and the economy. People, planet, and prosperity are commonly referred to collectively as the “three P's” of sustainability. The measurement of social and ecological categories presents difficulties in implementing the TBL approach in the manufacturing sector. The present work deals with the novel quantifiable approach of estimating energy consumption, carbon emissions and cost factors in the machining manufacturing of bearing steel based on a triple bottom-line approach. The AISI-52100 bearing steel was selected for machining tests under minimum quantity lubrication (MQL) and hybrid MQL-dry ice blasting cooling conditions. Then, the technological aspects such as product quality and cutting forces were evaluated along with the energy consumption, carbon footprints and economical aspects in machining of AISI-52100 bearing steel. Based on the results, it was determined that the MQL reduced cutting forces up to 20.7%, energy consumption up to 13.3%, while MQL-dry ice blasting reduced the surface roughness up to 15%. The cost of dry ice accounted for almost 30% of total machining expenses. Overall performance index (OPI) results for a proposed hybrid MQL dry ice blasting system favored MQL due to its superior sustainability features. Therefore, the purpose of this research is to organize our understanding of sustainability in a way that takes into account the environmental and financial implications of various industrial processes.

A hybrid model compression approach via knowledge distillation for predicting energy consumption in additive manufacturing

Recently, additive manufacturing (AM) has received increased attention due to its high energy consumption. By extracting hidden information or highly representative features from energy-relevant data, knowledge distillation (KD) reduces predictive model complexity and computational load. By using almost predetermined and fixed models, the distillation process restricts students and teachers from learning from one model to another. To reduce computational costs while maintaining acceptable performance, a teacher assistant (TA) was added to the teacher-student architecture. Firstly, a teacher ensemble was combined with three baseline models to enhance accuracy. In the second step, a teacher ensemble (TA) was formed to bridge the capacity gap between the ensemble and the simplified model. As a result, the complexity of the student model was reduced. Using geometry-based features derived from layer-wise image data, a KD-based predictive model was developed to evaluate the feasibility and effectiveness of two independently trained student models. In comparison with independently trained student models, the performance of the proposed method has the lowest RMSE, MAE, and training time.

Holistic System Modelling and Analysis for Energy-Aware Production: An Integrated Framework

Optimizing and predicting the energy consumption of industrial manufacturing can increase its cost efficiency. The interaction of different aspects and components is necessary. An overarching framework is currently still missing, and establishing such is the central research approach in this paper. This paper provides an overview of the current demands on the manufacturing industry from the perspective of digitalization and sustainability. On the basis of the developed fundamentals and parameters, a superordinate framework is proposed that allows the modelling and simulation of energy-specific properties on several product and process levels. A detailed description of the individual methods concludes this work and demonstrates their application potential in an industrial context. As a result, this integrated conceptual framework offers the possibility of optimizing the production system, in relation to different energy flexibility criteria.