Modelling and optimization of machining processes towards economic and environmental sustainability

In recent years, the increase in energy demand and carbon emission constraints have forced industry sector to improve the process efficiency with respect to environmental sustainability. Therefore, resource saving has become not only an added value, but a real priority for manufacturing in the Industry 4.0 era. Life-Cycle Assessment (LCA) is a common practice for estimating the environmental impact of products during their life-cycle, and can be used more widely and easily if specific models focusing on each life-cycle phase are available. In this thesis, the manufacturing phase of machined products has been modelled by analyzing different process performance metrics. Both the economic efficiency and the environmental sustainability have been accounted for. The Specific Production Time (SPT) is proposed as indicator of the manufacturing productivity; the Specific Production Cost (SPC) is developed in order to quantify the direct and indirect costs related to the manufacturing process; finally, the Specific Energy Requirement (SER) and the Specific Carbon Emission (SCE) indices are proposed in order to assess the environmental sustainability of the manufacturing phase in terms of primary energy demand and carbon footprint, respectively. The models have been developed in order to be valid for conventional machining processes in which cutting tools with defined cutting edge are used. The models are also aimed at the identification of optimum process parameters which allow to minimize each specific goal. In particular, optimum tool life values can be computed as a function of the machine tool, the cutting tool, the metalworking fluid, and the workpiece material. As a consequence, optimum process parameters such as cutting speed can be selected with respect to a specific tool life criterion. The high-efficiency machining range (widely known in literature) has been extended by considering all the four optimal cutting speeds (or tool life values) that minimize each production indicator. Hence, a trade-off criterion is proposed and developed by the introduction of a holistic function which can assign different weights on each optimization target. This advanced optimization method is suggested in order to identify a unique value of cutting speed (or tool life) which can be seen as a compromise among the different criteria of time, cost, and environmental sustainability. Four case studies have been considered in order to apply the proposed models and are focused on the turning of two titanium-based alloys conventionally used for aerospace applications: a Ti-6Al-4V alloy and a Ti-48Al-2Cr-2Nb intermetallic alloy. A Graziano SAG 101 CNC turning lathe was used in the experiments in order to obtain inventory data to test the models. Various set of process parameters such as depth of cut, feed, and cutting speed have been tested in order to identify the coefficients of the Taylor's tool life equation which plays a key role within the proposed models. Three different cutting tools were used. Finally, four lubrication/cooling conditions were adopted such as dry, wet, Minimum Quantity Lubrication (MQL), and Minimum Quantity Cooling (MQC). Overall, the four case studies are presented in order to assess the influence of (1) process parameters, (2) cutting tool geometries, (3) workpiece materials, and (4) lubrication/cooling conditions onto the machining performance measured by the proposed models. The wide applicability of the developed models has been proved by the results related to the analyzed case studies. In particular, the results highlighted that the proposed metrics are suitable for a proper selection of machining conditions that enable at the same time resource savings as well as reduced environmental impacts