Operation Conditions Empirical Optimization for Sustainability Manufacturing

Hayder Zghair; Noah Wesley Bretz; Jeffrey Sumner

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Advancements in Sustainable Manufacturing Practices
Tagged Division: Manufacturing Division (MFG)
Permanent URL: https://peer.asee.org/47816

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Paper Authors

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Hayder Zghair Southern Arkansas University orcid.org/0009-0009-5029-7571

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Hayder Zghair is an assistant professor of Industrial Engineering in the Department of Engineering and Physics, and the director of Industrial Engineering Development in the College of Science and Engineering at Southern Arkansas University. Before joining SAU, served in several academic appointments including Assistant Teaching Professor of Industrial and Manufacturing Engineering at Pennsylvania State University and lecturer of Industrial and Manufacturing Engineering at Kettering University. Dr. Zghair's research interest covers a wide area of industrial and manufacturing engineering including Flexible-Automated Manufacturing, Robotics, Human Factors and Ergonomics, Sustainability Engineering, Analytical Modeling and Simulation, and Optimization, and has published many journal and conference papers. Dr. Zghair continues to explore new and cutting-edge areas for research and education in engineering disciplines and has recently had an extensive interest in industry 4.0 and 5.0 tech such as AI, IIoT, smart sensors, and cloud computation and control. He also has several professional memberships; ASEE, SAE, and IEOM. Dr. Zghair obtained his Doctor’s Degree in Manufacturing Systems Engineering from Lawrence Technological University, Michigan, USA, and has several professional affiliations: Affiliate Researcher at Penn State Institute of Energy and the Environment and Board Member of Division Directors in the Environmental Engineering Division at American Society of Engineering Education (ASEE). Dr. Zghair is the chair of the SAU campus sustainability committee and a member of several committees at the department, college, and university levels.

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Noah Wesley Bretz Southern Arkansas University

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Department of Engineering and Physics

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Jeffrey Sumner Southern Arkansas University

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Department of Engineering and Physics

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Abstract

Clean and smart manufacturing is vital towards combating climate change. Manufacturing is one of the leading contributors to climate change, with the EPA saying that 24% of all greenhouse gas emissions come from manufacturing. While progress has been made in the industry, most technologies that reduce emissions are too cost prohibitive for most businesses to implement. The main roadblock to cleaner manufacturing is cost effectiveness. Cleaner processes can cost as much as five times the cost of the processes they are trying to replace. However, there is hope that as these new technologies are implemented, their overall cost will begin to decrease allowing more businesses to implement these new technologies. These new technologies can be assessed using a life cycle assessment to determine the environmental consequences of each individual machine and process through analysis of the material, energy, and emissions at each stage of the process. This assessment assumed to help determine which machine and / or process is more sustainable. Through many trials, it has been determined that machining in a controlled environment with higher cutting speeds and feed rates is the most economical and sustainable approach. There are a number of criteria that can be used to determine the sustainability of a manufacturing process. Some of them are carbon emissions, estimate of remaining tool life, estimation of total machining cost, and the surface quality of the machined product. Turning is a process commonly done on a lathe machine where the metal stock is rotated at a very fast speed and a cutting tool. This process can be wasteful in terms of material and can create carbon dioxide emissions. A set of designing and conducting experimentations to test the carbon emissions and power consumed in the turning process. The experimentation is by changing a number of parameters including the types of stock material (aluminum, steel, brass, etc.), the speed of turning the stock, as well as the speed of the cutting. The carbon dioxide contamination from the turning process is measured using two of atmospheric sensors. These the current temperature and humidity of an area in the atmosphere at that current point are measured to be with standard. The differences in the values between before and after the process determined the change in carbon dioxide levels and gives the indication which variables have the greatest effect on carbon emissions and how they can be optimized to be more sustainable. Using an energy consumption device, the consumed power calculated and the effect of the changes in parameters observed as well. Resulted data showed an interpretation determined on what set up of experimentation was the most energy efficient and contamination reduction for each parameter as well as determine the most effective way to conduct turning overall on the lathe machine.

Citation
Format

Zghair, H., & Bretz, N. W., & Sumner, J. (2024, June), Operation Conditions Empirical Optimization for Sustainability Manufacturing Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47816

TY - CPAPER
AB - Clean and smart manufacturing is vital towards combating climate change. Manufacturing is one of the leading contributors to climate change, with the EPA saying that 24% of all greenhouse gas emissions come from manufacturing. While progress has been made in the industry, most technologies that reduce emissions are too cost prohibitive for most businesses to implement. The main roadblock to cleaner manufacturing is cost effectiveness. Cleaner processes can cost as much as five times the cost of the processes they are trying to replace. However, there is hope that as these new technologies are implemented, their overall cost will begin to decrease allowing more businesses to implement these new technologies. These new technologies can be assessed using a life cycle assessment to determine the environmental consequences of each individual machine and process through analysis of the material, energy, and emissions at each stage of the process. This assessment assumed to help determine which machine and / or process is more sustainable. Through many trials, it has been determined that machining in a controlled environment with higher cutting speeds and feed rates is the most economical and sustainable approach. There are a number of criteria that can be used to determine the sustainability of a manufacturing process. Some of them are carbon emissions, estimate of remaining tool life, estimation of total machining cost, and the surface quality of the machined product. Turning is a process commonly done on a lathe machine where the metal stock is rotated at a very fast speed and a cutting tool. This process can be wasteful in terms of material and can create carbon dioxide emissions. A set of designing and conducting experimentations to test the carbon emissions and power consumed in the turning process. The experimentation is by changing a number of parameters including the types of stock material (aluminum, steel, brass, etc.), the speed of turning the stock, as well as the speed of the cutting. The carbon dioxide contamination from the turning process is measured using two of atmospheric sensors. These the current temperature and humidity of an area in the atmosphere at that current point are measured to be with standard. The differences in the values between before and after the process determined the change in carbon dioxide levels and gives the indication which variables have the greatest effect on carbon emissions and how they can be optimized to be more sustainable. Using an energy consumption device, the consumed power calculated and the effect of the changes in parameters observed as well. Resulted data showed an interpretation determined on what set up of experimentation was the most energy efficient and contamination reduction for each parameter as well as determine the most effective way to conduct turning overall on the lathe machine.
AU - Hayder Zghair
AU - Noah Wesley Bretz
AU - Jeffrey Sumner
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Operation Conditions Empirical Optimization for Sustainability Manufacturing
UR - https://peer.asee.org/47816
ER -