Relevant bibliographies by topics / Manufacturing processes Energy consumption Data processing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Manufacturing processes Energy consumption Data processing'

Author: Grafiati
Published: 10 December 2022
Last updated: 19 July 2025

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Journal articles on the topic "Manufacturing processes Energy consumption Data processing"

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Zhang, Chaoyang, Juchen Zhang, Weixi Ji, and Wei Peng. "Data Acquisition Network Configuration and Real-Time Energy Consumption Characteristic Analysis in Intelligent Workshops for Social Manufacturing." Machines 10, no. 10 (2022): 923. http://dx.doi.org/10.3390/machines10100923.

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To achieve energy-saving production, one critical step is to calculate and analyze the energy consumption and energy efficiency of machining processes. However, considering the complexity and uncertainty of discrete manufacturing job shops, it is a significant challenge to conduct data acquisition and energy consumption data processing of manufacturing systems. Meanwhile, under the growing trend of personalization, social manufacturing is an emerging technical practice that allows prosumers to build individualized services with their partners, which produces new requirements for energy data pr
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Jones, Lewis C. R., Nicholas Goffin, Jinglei Ouyang, et al. "Laser specific energy consumption: How do laser systems compare to other manufacturing processes?" Journal of Laser Applications 34, no. 4 (2022): 042029. http://dx.doi.org/10.2351/7.0000790.

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Laser material interactions are routinely praised for their selective processing and high processing rates. However, this does not guarantee that the total manufacturing system has a low energy intensity compared to conventional manufacturing processes. This paper presents the results of a collaborative UK and China research project to improve the comprehension of the total energy consumption and carbon emissions for laser-based manufacturing. A range of individual laser cutting, welding, and cleaning processes were studied to assess their energy efficiency, including the laser and its ancilla
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VYAKINA, Irina V., and Anton V. SKRYNNIK. "Developing the fuel and energy complex and energy security of the Russian Federation in the context of reindustrialization." Economic Analysis: Theory and Practice 22, no. 10 (2023): 1805–30. http://dx.doi.org/10.24891/ea.22.10.1805.

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Subject. The article considers the impact of fuel and energy complex on economic development and energy security of the Russian Federation. Objectives. The aim is to identify trends and specific features of Russia’s fuel and energy complex development in conditions of reindustrialization, and to work out practical recommendations for improving its energy security, focusing on elimination of imbalances in the development of extractive and manufacturing industries. Methods. We employed methods of systems analysis and analysis of statistical data. The general scientific method of induction was ap
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Adeniyi Kehinde Adeleke. "INTELLIGENT MONITORING SYSTEM FOR REAL-TIME OPTIMIZATION OF ULTRA-PRECISION MANUFACTURING PROCESSES." Engineering Science & Technology Journal 5, no. 3 (2024): 803–10. http://dx.doi.org/10.51594/estj.v5i3.904.

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In the realm of ultra-precision manufacturing, the minutiae of process control and material handling are paramount to achieving the highest levels of product quality and manufacturing efficiency. The industry faces a significant challenge: maintaining and enhancing the precision of manufacturing processes in real-time to ensure optimal output quality while minimizing waste and energy consumption. This challenge is compounded by the increasing complexity of products and the materials used, requiring ever more precise and adaptive manufacturing techniques. The importance of addressing this chall
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Iten, Muriel, Miguel Oliveira, Diogo Costa, and Jochen Michels. "Water and Energy Efficiency Improvement of Steel Wire Manufacturing by Circuit Modelling and Optimisation." Energies 12, no. 2 (2019): 223. http://dx.doi.org/10.3390/en12020223.

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Industrial water circuits (IWC) are frequently neglected as they are auxiliary circuits of industrial processes, leading to a missing awareness of their energy- and water-saving potential. Industrial sectors such as steel, chemicals, paper and food processing are notable in their water-related energy requirements. Improvement of energy efficiency in industrial processes saves resources and reduces manufacturing costs. The paper presents a cooling IWC of a steel wire processing plant in which steel billets are transformed into wire. The circuit was built in object-oriented language in OpenModel
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Taphasanoğlu, Saime, Muhammet Raşit Cesur, and Elif Cesur. "A Precise Energy Consumption Model for Computer Numerical Control Machines: A Hybrid Approach." Sustainability 16, no. 23 (2024): 10659. https://doi.org/10.3390/su162310659.

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In today’s world, energy efficiency is becoming increasingly crucial, due to its impact on sustainability in production. Designing systems that consume less energy and manage resources efficiently is essential. Variations in operating speed can affect processing time, energy consumption, idle times of subsequent machines, work delays, and missed deadlines. While most studies focus on prediction parameters like cut depth and cut area to estimate the energy consumption or processing time, our approach emphasizes variations in G-code motion parameters. To enhance both precision and the adaptabili
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Ingarao, Giuseppe, Paolo C. Priarone, Francesco Gagliardi, Rosa di Lorenzo, and Luca Settineri. "Environmental Comparison between a Hot Extrusion Process and Conventional Machining Processes through a Life Cycle Assessment Approach." Key Engineering Materials 622-623 (September 2014): 103–10. http://dx.doi.org/10.4028/www.scientific.net/kem.622-623.103.

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Nowadays manufacturing technologies have to be evaluated not only for the technical features they can provide to products, but also considering the environmental perspective as well. As long as the technological feasibility of a given process is guaranteed, processes minimizing resources and energy consumption have to be selected for manufacturing. With respect to this topic, the research studies in the domain of metal processing technologies predominantly focus on conventional material removal processes as milling and turning. Despite some exceptions, many other non-machining technologies, su
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Willenbacher, Martina, Jonas Scholten, and Volker Wohlgemuth. "Machine Learning for Optimization of Energy and Plastic Consumption in the Production of Thermoplastic Parts in SME." Sustainability 13, no. 12 (2021): 6800. http://dx.doi.org/10.3390/su13126800.

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In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing
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Indzere, Zane, Kevin D. Manzano Martinez, Tereza Bezrucko, Zauresh Khabdullina, Ivars Veidenbergs, and Dagnija Blumberga. "Energy Efficiency Improvement in Thawing." Environmental and Climate Technologies 24, no. 2 (2020): 221–30. http://dx.doi.org/10.2478/rtuect-2020-0068.

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AbstractThe thawing process within fish processing is one of the most essential steps in manufacturing. Various processes of thawing can be used where efficiency varies between companies depending on such characteristics as energy consumption, the price of resources, etc. The main aim of the research is to increase the efficiency of thawing processes. Firstly, to analyse various thawing methods and to find the most efficient one by using multi-criteria decision making analysis method. Secondly, analysing data of thawing of existing company to find opportunities for improvements, including the
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Moon, Yeeun, Younjeong Lee, Yejin Hwang, and Jongpil Jeong. "Long Short-Term Memory Autoencoder and Extreme Gradient Boosting-Based Factory Energy Management Framework for Power Consumption Forecasting." Energies 17, no. 15 (2024): 3666. http://dx.doi.org/10.3390/en17153666.

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Electricity consumption prediction is crucial for the operation, strategic planning, and maintenance of power grid infrastructure. The effective management of power systems depends on accurately predicting electricity usage patterns and intensity. This study aims to enhance the operational efficiency of power systems and minimize environmental impact by predicting mid to long-term electricity consumption in industrial facilities, particularly in forging processes, and detecting anomalies in energy consumption. We propose an ensemble model combining Extreme Gradient Boosting (XGBoost) and a Lon
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Dissertations / Theses on the topic "Manufacturing processes Energy consumption Data processing"

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Jiang, Sheng. "Processing rate and energy consumption analysis for additive manufacturing processes : material extrusion and powder bed fusion." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111753.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 111-116).<br>Additive technologies have given birth to an expanding industry now worth 5.1 billion dollars. It has been adopted widely in design and prototyping as well as manufacturing fields. Compared to conventional technologies, additive manufacturing technologies provides opportunity to print unique complex-shaped geometries. However, it also suffers from slow production rate and high energy consumption. Imp
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Books on the topic "Manufacturing processes Energy consumption Data processing"

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Global Innovations Symposium (4th 2003 San Diego, Calif.). Energy efficient manufacturing processes: Proceedings of the technical sessions presented at the 132nd TMS Annual Meeting : San Diego, California, USA, March 2-6, 2003 : TMS Material Processing and Manufacturing Division Global Innovations Symposium. TMS, 2003.

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Saez, Doris A., Aldo Cipriano, and Andrzej W. Ordys. Optimisation of Industrial Processes at Supervisory Level: Application to Control of Thermal Power Plants (Advances in Industrial Control). Springer, 2001.

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Book chapters on the topic "Manufacturing processes Energy consumption Data processing"

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Mühlbauer, Matthias, Hubert Würschinger, Dominik Polzer, and Nico Hanenkamp. "Energy Profile Prediction of Milling Processes Using Machine Learning Techniques." In Machine Learning for Cyber Physical Systems. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62746-4_1.

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AbstractThe prediction of the power consumption increases the transparency and the understanding of a cutting process, this delivers various potentials. Beside the planning and optimization of manufacturing processes, there are application areas in different kinds of deviation detection and condition monitoring. Due to the complicated stochastic processes during the cutting processes, analytical approaches quickly reach their limits. Since the 1980s, approaches for predicting the time or energy consumption use empirical models. Nevertheless, most of the existing models regard only static snapshots and are not able to picture the dynamic load fluctuations during the entire milling process. This paper describes a data-driven way for a more detailed prediction of the power consumption for a milling process using Machine Learning techniques. To increase the accuracy we used separate models and machine learning algorithms for different operations of the milling machine to predict the required time and energy. The merger of the individual models allows finally the accurate forecast of the load profile of the milling process for a specific machine tool. The following method introduces the whole pipeline from the data acquisition, over the preprocessing and the model building to the validation.
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De Bernardez, Leopoldo, Giampaolo Campana, Mattia Mele, and Sebastian Mur. "Towards a Comparative Index Assessing Mechanical Performance, Material Consumption and Energy Requirements for Additive Manufactured Parts." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_34.

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AbstractThe increasing use of Additive Manufacturing technologies and systems in several industrial sectors and their numerous applications turn the attention of scientists and investigators to studying and evaluating the environmental impacts of these processes. Additive Manufacturing generally allows for a reduction of raw material consumption and waste generation. On the other hand, the need for long processing times and the necessary thermal conditioning of the manufacturing chamber to avoid product defects, lead to a considerable amount of consumed energy per produced item. Energy consumption has been a primary concern of the research on the sustainability of Additive Manufacturing indeed. More recent studies extended the analysis through more complete evaluation methods such as the Life Cycle Assessment. This approach allows a detailed description of environmental impacts but is affected by some concerns about the need for an interpretation of the final results, which can be non-univocal. This fact is particularly critical when the assessment is intended to be used for comparison between alternative solutions.In this study, a novel index is introduced including three main aspects: material consumption, energy requirements and mechanical performance. The proposed formulation makes the index immediately usable for comparing alternative solutions. Within the scope of this study, the index has been applied to one of the most widespread Additive Manufacturing processes, namely Fused Filament Fabrication. The presented case study demonstrates the suitability of the proposed method to compare and identify the optimal choice among alternative manufacturing scenarios.
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De Bernardez, Leopoldo, Giampaolo Campana, and Sebastian Mur. "Use of the Consumption Performance Sustainability Index as a Decisional Tool at a Preliminary Stage of Project Development." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-93891-7_9.

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Abstract The choice of materials and manufacturing processes based on the design of industrial products depends on several factors related to common practice, availability of materials and machines and reliability of the production processes. Furthermore, industrial products must achieve a zero-defect policy and be safe and sustainable by design. This work implements the Consumption Performance Sustainability Index and extends its use to compare two Additive Manufacturing processes that transform the same polymers into a reference geometry. The index considers the part design and production parameters related to the material transformation due to the manufacturing processes. In particular, the product’s mechanical performance, materials consumption, and energy used for manufacturing. It is here developed to evaluate, at an early stage, the product design by using the production equipment’s available technical data, manufacturing times and material consumption - estimated through specific software - and other data from the scientific literature. The index is proposed to assess the sustainability of products and find the best manufacturing alternative as a complementary tool to standard approaches based on life cycle sustainability assessment. The procedure includes optimising the geometry using topology or generative design, assessing applied stresses and production times, and evaluating the performance of the transformed material based on part orientation to optimise the manufacturing process.
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Miller, Eddi, Anna-Maria Schmitt, Tobias Kaupp, Rafael Batres, Andreas Schiffler, and Jan Schmitt. "A Peak Shaving Approach in Manufacturing Combining Machine Learning and Job Shop Scheduling." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77429-4_59.

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AbstractComputerized Numerical Control (CNC) plays an important role in highly autonomous manufacturing systems with multiple machine tools. The necessary Numerical Control (NC) programs to manufacture the parts are mostly written in standardized G-code. An a priori evaluation of the energy demand of CNC-based machine processes opens up the possibility of scheduling multiple jobs according to balanced energy consumption over a production period. Due to this, we present a combined Machine Learning (ML) and Job-Shop-Scheduling (JSS) approach to evaluate G-code for a CNC-milling process with respect to the energy demand of each G-command. The ML model training data are derived by the Latin hypercube sampling (LHS) method facing the main G-code operations G00, G01, and G02. The resulting energy demand for each job enhances a JSS algorithm to smooth the energy demand for multiple jobs, as peak power consumption needs to be avoided due to its expense.
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Wicaksono, Hendro, Tina Boroukhian, and Atit Bashyal. "A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning." In Dynamics in Logistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_8.

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AbstractThe spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.
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De Bernardez, Leopoldo, Cristian Sandre, and Juan Sanguinetti. "Comparison of an Additive with a Subtractive Method from the Perspective of Sustainability." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-77429-4_79.

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AbstractAdditive Manufacturing (AM) is increasingly used for the manufacture of parts in different industrial sectors and, therefore, it becomes relevant to evaluate the mechanical performance that can be achieved with this process and the possible impacts on the environment compared to traditional processes. In this paper, two alternative production methods for fabricating a standard stainless steel tensile specimen are compared: Selective Laser Melting (SLM) and machining. Functional tests were carried out until the fracture of the pieces. The amount of material used was measured. In addition, the energy consumed to produce the pieces was estimated. Both production processes were compared concerning the measured tensile properties, material consumption, and additional data from the literature. The results obtained from generative design and topological optimization of a part are discussed, as well as the implications regarding the sustainability of the processes.
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Krauß, Jonathan, Thomas Ackermann, Alexander D. Kies, David Roth, and Miriam Mitterfellner. "Virtual Experiments for a Sustainable Battery Cell Production." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_66.

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AbstractOn the path towards a sustainable society, the availability of energy storage systems is an essential step – leading to increased demand for batteries. To achieve a sustainable society, it is necessary to manufacture batteries also in a sustainable way. One approach lies in virtual experiments. They aim at identifying parameters, recipes, and technologies in the digital world, before applying them to the physical production system. Thus, manufacturing is optimized in regard to sustainability indicators such as material consumption, emission, and waste – but also in regard to costs, quality, and yield. The faster ramp-up is especially important in the production of battery cells, due to the highly complex processes and critical materials. In this paper, we introduce a concept for virtual experiments platform in battery cell production. It includes collection of data, data aggregation, a simulation environment, as well as an optimizer. Also, it is integrated into existing production and IT systems. The virtual experiments platform functions as a service of a digital twin. Validation is conducted by realizing the virtual experiments platform on the electrode production of lithium-ion batteries.
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Dubey, Vandana, Priti Kumari, Kavita Patel, Shikha Singh, and Sarika Shrivastava. "Amalgamation of Optimization Algorithms With IoT Applications." In Sustainable Development in Industry and Society 5.0. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-7322-4.ch009.

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The integration of optimization algorithms with IoT (internet of things) applications presents numerous benefits and diverse applications. Optimization algorithms help enhance the efficiency, scalability, and cost-effectiveness of IoT systems. This powerful combination offers advantages such as improved resource allocation, reduced energy consumption, enhanced decision-making, and better resource utilization. It finds applications in smart cities, agriculture, healthcare, manufacturing, and more, optimizing traffic management, precision agriculture, healthcare resource allocation, and supply chain management, among others. In summary, the union of optimization algorithms with IoT unlocks a wide array of opportunities for optimizing processes, conserving resources, and improving the quality of services in various domains. Optimization algorithms are used to find the best solution to a given problem, and when applied to IoT, they can help in various ways, including improving resource allocation, energy efficiency, data analysis, and more. Here the authors discuss some ways in which optimization algorithms can be combined with IoT applications such as resource allocation, energy efficiency, data routing and processing, quality of service (QoS), improvement, etc. The choice of the specific optimization algorithm depends on the nature of the problem and the application. Algorithms like genetic algorithms, particle swarm optimization, simulated annealing, and machine learning techniques (e.g., deep reinforcement learning) can be applied to various IoT optimization problems. Basically, the combination of optimization algorithms with IoT applications can lead to more efficient, cost-effective, and reliable IoT systems across a wide range of domains. It's essential to carefully assess the specific requirements of your IoT application and select the appropriate optimization techniques to achieve the goals.
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Wang, Jiangchao, Bitao Liu, Zhangjing Bao, et al. "Ongoing of Energy Saving and Emission Reduction during Fabrication Processing in China’s Shipyards." In Welding - Materials, Fabrication Processes, and Industry 5.0. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1002238.

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Due to the serious situation and deterioration tendency of the atmosphere environment, energy saving and emission reduction are concentrated and desired for each government and international organization. In this research, energy consumption and pollution emission during fabrication processing in shipyards and ocean engineering factories were holistically surveyed for the first time, while the ship industry is the key pillar of manufacturing for national economic development and dominant monitoring objects with severe environment pollution in China. With the visiting and investigation of six representative factories with construction and repair of ship and offshore structure, consumptions of electrical energy as well as chemical energy were summarized for each fabrication processing according with elementary manufacturing flow, which are mainly determined by working load, requirement of quality, and utilization efficiency of energy. Then, various pollutants generated during fabrication procedures were classified and surveyed, while their emission amounts were also summarized by considering their harm level to human health, atmosphere, and ecological environments. In addition, advanced and practical solutions for emission reduction of dust particles and VOCs (volatile organic compounds) were introduced and carried out while the application results were compared with requirements of corresponding laws and regulations.
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Schmitt, Thomas, Pavani Sakaray, Lars Hanson, Matías Urenda Moris, and Kaveh Amouzgar. "Frequent and Automatic Monitoring of Resource Data via the Internet of Things." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220127.

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The Internet of Things (IoT) offers potential for developing an intelligent and sustainable manufacturing system, allowing for better and more informed decisions that increase efficiency and cut down waste in production processes. The insights are generated from automatically collected data coming from machines and devices. While process data are already reported and support a close to real-time monitoring and evaluation of process efficiencies, data about resource consumption in manufacturing environments is more scarce but crucial for becoming more resource efficient. Through connected hardware and software applications, data from resource consumption of energy, water, and waste can be automatically collected. To achieve this, this study presents an IoT framework for monitoring resource efficiency in an automatic and frequent manner. Thus, the eco-efficiency and productivity of the process can be measured and integrated into the decision-making processes by sharing the data with shop floor and production management personnel via dashboards.
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Conference papers on the topic "Manufacturing processes Energy consumption Data processing"

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Gao, Shang, and Brahim Benyahia. "Robust Techno-economic Analysis, Life Cycle Assessment, and Quality and Sustainability by Digital Design of Three Alternative Continuous Pharmaceutical Tablet Manufacturing Processes." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.104102.

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This study presents a comprehensive comparison of the three alternative downstream manufacturing technologies for pharmaceuticals: i) Dry Granulation (DG) through roller compaction, ii) Direct Compaction (DC), and iii) Wet Granulation (WG) based on the economic, environmental and product quality performances. Firstly, the integrated dynamic mathematical models of the different downstream (drug product) processes were developed using gPROMS formulated products based on data from the literature or/and our recent experimental work. The process models were developed and simulated to reliably captu
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Das, Emon, and Raldo K. "Driving Design Towards a Sustainable Aviation Industry Product Using Environmental Impact Evaluation." In Vertical Flight Society 74th Annual Forum & Technology Display. The Vertical Flight Society, 2018. http://dx.doi.org/10.4050/f-0074-2018-12725.

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The goals of sustainable manufacturing, as articulated by Organization for Economic Co-operation and Development (OECD), are to reduce the intensity of material use, energy consumption, emissions and unwanted by-products - while maintaining or improving the value of products to society and to organizations. Benefits that can be achieved through this practice include improved working conditions, public image, staff morale, customer loyalty, brand value, profits, sales turnover, product performance, reduction in waste generation and staying ahead of regulatory concerns. Achieving such goals begi
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Muroyama, Alexander, Mahesh Mani, Kevin Lyons, and Bjorn Johansson. "Simulation and Analysis for Sustainability in Manufacturing Processes." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47327.

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“Sustainability” has become a ubiquitous term in almost every field, especially in engineering design and manufacturing. Recently, an increased awareness of environmental problems and resource depletion has led to an emphasis on environmentally friendly practices. This is especially true in the manufacturing industry where energy consumption and the amount of waste generated can be high. This requires proactive tools to be developed to carefully analyze the cause-effect of current manufacturing practices and to investigate alternative practices. One such approach to sustainable manufacturing i
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Wang, Xingtao, Robert E. Williams, Michael P. Sealy, Prahalada Rao, and Yuebin Guo. "Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling With a Focus on Tool Wear." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6511.

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The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, Data Dependent Systems (DDS), a stochastic modeling and analysis technique, was applied to study spindle motor energy consumption during a hard milling operation. The objective was to corr
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Bharambe, Ganesh, Prakash Dabeer, Kumar Digambar Sapate, and Suresh M. Sawant. "Energy Savings for Sustainability of Machining Process." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-53295.

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Processing of metals in industries is lifeline of economy of country, which helps to shape the country. Energy saving in this process is attributed to both the parts ie process of machining and energy consumed in machine tools itself. The process of material removal had experienced lot of improvements in last few decades. This consists of developments in pre-machining processes, metal cutting methods and developments in cutting theories and cutting tools. Cutting fluid is one of challenging field to yield more favourable results. Manufacturing practices beyond its existing limits, process and
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Nordlund, Alec, Rachel McAfee, Rebecca Ledsham, and Joshua Gess. "Cooling of High Powered GPUs Using Liquid Nitrogen Cold Plates Made With Additive Manufacturing." In ASME 2021 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/ipack2021-74108.

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Abstract Processor energy density is exceeding the capabilities of conventional air-cooling technology, but two-phase cooling has the potential to manage these resulting heat fluxes at reliable temperatures and higher electrical efficiency. When two-phase cooling is used in tandem with overclocking, data center footprints are reduced as individual chip processing power can be set at limits well beyond the manufacturer’s Thermal Design Power (TDP) or nominal operating condition. This study examines how Liquid Nitrogen (LN2) can be used with Additive Manufacturing (AM) and overclocking to increa
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Fehrenbacher, Axel, Joshua R. Schmale, Michael R. Zinn, and Frank E. Pfefferkorn. "Tool-Workpiece Interface Temperature Measurement in Friction Stir Welding." In ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/msec2012-7326.

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The objectives of this work are to develop an improved temperature measurement system for Friction Stir Welding (FSW). FSW is a novel joining technology enabling welds with excellent metallurgical and mechanical properties, as well as significant energy consumption and cost savings compared to traditional fusion welding processes. The measurement of temperatures during FSW is employed for process monitoring, heat transfer model verification and process control, but current methods have limitations due to their restricted spatial and temporal resolution and have found only few industrial applic
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Kalla, Devi K., Samantha Corcoran, Janet Twomey, and Michael Overcash. "Energy Consumption in Discrete Part Production." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50163.

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It is widely recognized that industrial production inevitably results in an environmental impact. Energy consumption during production is responsible for a part of this impact, but is often not provided in cradle-to-gate life cycles. Transparent description of the transformation of materials, parts, and chemicals into products is described herein as a means to improve the environmental profile of products and manufacturing machine. This paper focuses on manufacturing energy and chemicals/materials required at the machine level and provides a methodology to quantify the energy consumed and mass
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Hansen, Morten Hejlskov, Teodor Vernica, Rami Mansour, and Devarajan Ramanujan. "Three-Dimensional Visualization of Energy Consumption Data in Additive Manufacturing." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-143405.

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Abstract This paper addresses the growing interest in design for sustainable manufacturing, focusing particularly on Additive Manufacturing (AM). While AM offers advantages like customized geometries and reduced material usage, there’s a need to address energy consumption in 3D printing beyond the conventional emphasis on material reduction. While previous research has explored various techniques for predicting and reducing the energy consumption of AM processes, there is limited work on guiding designers to generate more energy-efficient designs via localized geometric changes. This paper int
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Song, Ruoyu, Yanglong Lu, Cassandra Telenko, and Yan Wang. "Manufacturing Energy Consumption Estimation Using Machine Learning Approach." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67679.

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Environmental impacts of manufacturing are often significant and influenced by part and process parameters. Energy consumption is one of the most critical factors for the overall environmental impact of manufacturing. To achieve energy reduction, one must estimate the manufacturing energy consumption throughout the design stage. This paper presents an efficient data-driven approach to utilize machine learning to estimate energy consumption of a manufacturing process from a CAD model. The approach enables quick cost estimation with limited knowledge about the exact process parameters. A case st
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Reports on the topic "Manufacturing processes Energy consumption Data processing"

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Wada, Yasutaka. Working Paper PUEAA No. 3. Parallel Processing and Parallelizing Compilation Techniques for "Green Computing". Universidad Nacional Autónoma de México, Programa Universitario de Estudios sobre Asia y África, 2022. http://dx.doi.org/10.22201/pueaa.001r.2022.

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The fourth technological revolution has brought great advances in manufacturing processes and human communications. Although processors have become increasingly efficient, both in speed, capacity and energy consumption, their functionality regarding this last point has yet to improve. The latest innovations represent an opportunity to create "green computing" and not only more environmentally friendly electronics and software, but also to use their new efficiency to improve our daily activities, as well as the designs of our cities themselves to make them more environmentally sustainable. Thes
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Ahammad, Ronju, and Francisco X. Aguilar. Socio-economic indicators for the assessment of sustainability in the Swedish forest sector, and linkages with the national environmental quality objectives. SLU Future forests, Swedish University of Agricultural Sciences, 2024. http://dx.doi.org/10.54612/a.6cbejge10k.

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Sweden’s Environmental Quality Objectives (EQOs) have been adopted to help describe the environment the country wishes to achieve, and are a promise to future generations of clean air, a healthy living environment, and rich opportunities to enjoy nature. Here, we assessed selected socio-economic indicators adapted from the Montréal Process for the Conservation and Sustainable Management of Temperate and Boreal Forests (MP) to examine trends in the Swedish forest sector of direct relevance to the EQOs. We did this with the aim of raising awareness about important socio-economic dimensions relat
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