Alternative machining methods lower carbon emissions in A6063 aluminum alloy processing, indicating sustainability benefits.
The increase in concern about climate change and the necessity for sustainable manufacturing has resulted in more and more eco-friendly machining processes to reduce the effect of machining on the environment without sacrificing productivity. Traditional machining uses a lot of cutting fluid and has high energy consumption rates, which account for a large portion of total carbon emissions. In this study, alternative turning methods using carbide tools were investigated during the machining of A6063 aluminum alloy on a CNC machine to evaluate their comparative performance under different conditions, includingflood cooling, minimum quantity lubrication (MQL), cryogenic cooling, and dry machining. This study evaluated how each of these four methods performed in terms of their respective optimizations measures and the results of the study showed that the eco-friendly methods outperformed traditional machining methods in most instances. Using dry machining, spindle speeds were increased by 30%, there was a reduction in tool wear by 63.3% and surface finish improved by 44%. With respect to energy consumption and CO₂ emissions, dry machining achieved a reduction of 72% and 75%, respectively. MQL resulted in a 40% reduction in tool wear, improved surface finish at 28%, energy savings between 68% and 70%, and a reduction of 78 to 80% in CO₂ emissions. Cryogenic cooling resulted in similar-performing systems but was more balanced with only a 10% spindle speed increase over dry machining, 48.3% reduction in tool wear,36% improvement in surface finish, and a savings of 62% in energy and 68.8% in CO₂ emissions compared to dry machining. Although all eco-friendly alternatives required lower feed rates than traditional machining, the remarkable environmental and performance benefits of each of the eco-friendly methods suggest that all four methods are feasible and sustainable alternatives to traditional machining.
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Mallela Komaleswararao (2026) studied this question.
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