This work focuses on optimising process parameters in dry machining, which is a sustainable alternative to conventional machining that eliminates the need for cutting fluids. The aim is to identify the best operating conditions to reduce environmental impact, particularly energy consumption and CO2 emissions, while maintaining high production efficiency and machining quality.
A mathematical model has been developed to evaluate and optimise dry machining parameters by integrating economic and environmental indicators such as power consumption, tool wear, cutting forces and greenhouse gas emissions. The model employs decision variables relating to cutting speed, feed rate and depth of cut. A simulation-based approach was employed, and a numerical case study was conducted to validate the model. A sensitivity analysis was also performed to assess the influence of key parameters on overall environmental performance.
The results demonstrate that optimising dry machining conditions can significantly reduce energy consumption and CO2 emissions without compromising productivity. The study demonstrates that selecting parameters with environmental considerations in mind leads to an extended tool life and lower operational costs, thereby supporting the shift towards more environmentally friendly manufacturing practices.
The present study focuses on a specific case of dry machining using 42CrMo4 steel; consequently, it is acknowledged that its findings may not be generalizable to other materials or machining contexts. The model operates under the assumption of stable operating conditions, thus failing to incorporate real-time variations such as tool wear progression or machine vibrations. Furthermore, environmental costs are estimated using average emission factors, which may vary by energy source or geographic region. It is recommended that future research extend the model to include dynamic process monitoring, multi-material analysis and life cycle assessment (LCA) with a view to providing a more comprehensive view of environmental impacts across different manufacturing environments.
This study provides manufacturers with a decision-support tool to optimise dry machining operations by balancing productivity and environmental impact. The integration of critical operational parameters, including cutting speed, tool wear, energy consumption and CO2 emissions, into a unified model facilitates the identification of parameter settings that optimise both operational efficiency and environmental impact. The two-phase strategy offers a high degree of flexibility in production planning, thus enabling companies to adapt to market demands for both high-end and cost-sensitive products. This model is of particular value to industries seeking to transition towards sustainable manufacturing practices without compromising output quality or profitability.
This research is original in its integration of environmental metrics into the optimisation of dry machining processes. It provides a comprehensive framework to support industrial decision-making aimed at reducing the environmental impact of manufacturing operations.
