The social context in relation to energy policies and advances in more energy efficient technologies is changing constantly, thus driving a need for change in the manufacturing sector. This study presents a knowledge-based framework that enables the understanding of the model for knowledge that has taken industrial energy efficiency to current levels and the analysis of the model in the current context of industry transition. The framework consists of three broader forms of knowledge and specific knowledge attributes that can capture the knowledge employed in industrial energy management. The framework is applied in a systematic literature review, analyzing the forms of knowledge and main aspects of energy management in manufacturing industries from 157 articles published between 2010 and 2020 in various academic journals. Besides the framework, the results show that the technical form of knowledge is the primary type of knowledge employed in energy management and that a paradigm-changing towards Industry 4.0. is seen. Another employed form of knowledge is process knowledge, which is concerned with the prerequisite information needed to implement energy management. Finally, leadership knowledge is also employed in energy management and a blend in these three forms of knowledge might move us beyond traditional knowledge towards new forms of knowledge that maximize the potential for energy management in manufacturing industries. The knowledge demands brought by Industry 4.0 for all forms of knowledge are identified and discussed. 

Knowledge demands for energy management in manufacturing industry - A systematic literature review

https://link.springer.com/content/pdf/10.1007/s42235-021-0064-5.pdf

Research Progress on Corrosion Resistance of Magnesium Alloys with Bio-inspired Water-repellent Properties: A Review

Wire electrical discharge machining (wire-EDM) is an energy-intensive process, and its success relies on a correct selection of cutting parameters. It is vital to optimize energy consumption, along with productivity and quality. This experimental study optimized three parameters in wire-EDM: pulse-on time, servo voltage, and voltage concerning machining time, electric power, total energy consumption, surface roughness, and material removal rate. Two different plate thicknesses (15.88 mm and 25.4 mm) were machined. An orthogonal array, signal-to-noise ratio, and means graphs, and an analysis of variance (ANOVA), determine the effects and contribution of cutting parameters on responses. Pulse-on time is the most significant factor for almost all variables, with a percentage of contribution higher than 50%. Multi-objective optimization is conducted to accomplish a concurrent decrease in all variables. A case study is proposed to compute carbon dioxide (CO2) tons and electricity cost in wire-EDM, using cutting parameters from multi-objective optimization and starting values commonly employed to cut that tool steel. A sustainable manufacturing approach reduced 5.91% of the electricity cost and CO2 tons when machining the thin plate, and these responses were diminished by 14.09% for the thicker plate. Therefore, it is possible to enhance the sustainability of the process without decreasing its productivity and quality.

Analysis and optimization of sustainable machining of AISI O1 tool steel by the wire-EDM process

A sustainable cooling/lubrication method focusing on energy consumption and other machining characteristics in high-speed turning of aluminum alloy

Method for evaluating the resource, energy, and environmental impact of the casting fault rectification process in patternless sand castings

Sustainability of unconventional machining industry considering impact factors and reduction methods of energy consumption: A review and analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Chen Zhou

Author Tools

Chat about Author

Papers

Sustainability of unconventional machining industry considering impact factors and reduction methods of energy consumption: A review and analysis