Akademik Veri Yönetim Sistemi
Multidiscipline Modeling in Materials and Structures, cilt.21, sa.4, ss.942-958, 2025 (SCI-Expanded)
Purpose: This research aims to address the critical challenge of optimizing machining processes for serial aluminum alloys, focusing on reducing carbon emissions and energy consumption while maintaining high surface quality. The study introduces the specific carbon footprint (SCF) model to evaluate CO2 emissions per unit material removed, aiming to enhance sustainable production practices in mass manufacturing. Design/methodology/approach: Using response surface methodology (RSM), experiments were conducted on 5,000, 6,000 and 7,000 series aluminum alloys to assess the impact of cutting speed and feed rate on surface quality, energy consumption and carbon footprint. Energy usage data were collected, and analysis of variance was used to identify the contributions of process parameters. Findings: The results revealed that feed rate is the most influential factor, contributing 51.8% to the SCF, followed by cutting speed at 32%. Optimal conditions reduced CO2 emissions by 37%, cutting the carbon footprint from 516.4 tons to 325 tons annually. Among the materials tested, the 6,000 series exhibited the best machinability, balancing low energy consumption and high surface quality. Research limitations/implications: The proposed SCF model serves as a novel metric for sustainable manufacturing, enabling precise evaluation of carbon emissions in machining processes. This work establishes a benchmark for optimizing machining parameters, significantly reducing environmental impact in mass production scenarios. Originality/value: This study pioneers the integration of SCF into machining optimization and offers actionable insights for sustainable manufacturing. It highlights the potential of using RSM to simultaneously optimize energy efficiency, surface quality and carbon emissions, providing a valuable framework for future research and industrial applications.