A novel quantifiable approach of estimating energy consumption, carbon emissions and cost factors in manufacturing of bearing steel based on triple bottom-line approach
Source abstract
Abstract
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.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Energy Efficiency and Management · Mechanical and Thermal Properties Analysis · Advanced machining processes and optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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