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Goutam Mohapatra
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Series: ASM Handbook
Volume: 4F
Publisher: ASM International
Published: 01 February 2024
DOI: 10.31399/asm.hb.v4F.a0007017
EISBN: 978-1-62708-450-5
Abstract
Mathematical models have been used for over five decades in industrial heat-treating operations. Most of these modeling efforts have emanated from academia or research institutes, with the primary approach of mathematically capturing heat-treating processes and validating quality predictions. In this article, a contrarian but more realistic scenario is considered, where two industrial problem descriptions become the starting point. The technical complexity of the industry problem has been elaborated for a deeper understanding of the issue along with elaboration of the approach and potential methods for determining a solution. Then, quantitative analyses of practical industrial problems are demonstrated. Finally, the potential shift in these approaches with the advent of Industry 4.0 is outlined.
Series: ASM Handbook
Volume: 18
Publisher: ASM International
Published: 31 December 2017
DOI: 10.31399/asm.hb.v18.a0006386
EISBN: 978-1-62708-192-4
Abstract
This article provides a detailed literature overview of wear in agriculture equipment and implements. It introduces them with specific description of the wear situation due to ground or crop engagement. The article provides information on operational parameters, component design, and selection of implements. It illustrates their quantitative correlations to wear. The article details wear mitigation strategies for metallic components, such as materials selection, coating, design, and processing. It reviews wear testing approaches for equipment and implements. The article discusses the role of modeling and simulation for understanding and managing wear.
Journal Articles
Journal: AM&P Technical Articles
AM&P Technical Articles (2014) 172 (3): 44–47.
Published: 01 March 2014
Abstract
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Manufacturing data together with heat treating knowledge can be synthesized with physics and data-based modeling approaches in a closed loop to provide insight for improving process efficiency and product quality for overall reduction in operating and energy costs. This article provides case studies of data analytics for coil batch annealing and batch carburizing.