Improving machining performance and meeting the requirements of sustainable production at the same time represents a major challenge for the metalworking industry and scientific community. One approach to satisfying the above challenge is to apply different types of cutting fluids or to optimise their usage during the machining process. The fact that cutting fluids are well known as significant environmental pollutants in the metalworking industry has encouraged researchers to discover new environmentally friendly ways of cooling and lubricating in the machining process. Therefore, the main goal is to investigate the influence of different machining conditions on the efficiency of hard machining and find a sustainable solution towards smart manufacturing. In the experimental part of the work, the influence of various machining parameters and conditions on the efficiency of the process was investigated and measured through the surface roughness, tool wear and cutting force components. Statistical data processing was carried out, and predictive mathematical models were developed. An important achievement is the knowledge of the efficiency of compressed cold air cooling for hard milling with the resulting lowest average flank wear of 0.05 mm, average surface roughness of 0.28 µm, which corresponds to grinding procedure roughness classes of N4 and N5, and average tool durability increase of 26% compared to dry cutting and conventional use of cutting fluids. Becoming a smart machining system was assured via technological improvement achieved through the reliable prediction of tool wear obtained by radial basis neural networks modelling, with a relative prediction error of 3.97%.

The development of sustainable cooling technologies and increased concern for recycled materials will affect the reduction of greenhouse gas emissions, which primarily originate from the production industry. In this research paper, a twofold contribution to sustainability is made through the efficient application of a workpiece, obtained by recycling waste in the form of metal chips, and the machining of the obtained workpiece by using alternative cooling techniques comparing them to cutting fluids. Minimum quantity lubrication and cold compressed air cooling were selected as two sustainable, alternative cutting environments. Using Taguchi’s L9 orthogonal array, the influence of cutting speed, feed rate and cutting environment on drilling thrust force, built-up edge formation and hole deviation was observed. Using the analysis of variance method, feed rate was identified to have the highest influence on the output parameters (31%), followed by cooling and lubrication techniques (18%) and lastly by cutting speed (5%). Based on the grey relation analysis, optimal controllable factors were identified. This analysis indicated that low cutting speeds and feed rates, coupled with the MQL cutting environment, produced the lowest thrust force, deviation of hole and built-up edge formation.