Government legislation and public opinion are the main drivers behind the movement of manufacturing companies towards sustainable production. Fundamentally, companies want to avoid future financial penalties and the industry is therefore under pressure to adapt new techniques and practices in order to become environmentally friendly. The cost efficiency of metal cutting operations is highly dependent on accuracy, excellent surface finish and minimised tool wear and, to this end, has traditionally made abundant use of cutting fluid in machining operations. However, these cutting fluids have been a major contributor to environmental and health issues. In recent years an enormous effort to eradicate these adverse effects has been made with one important focus being the implementation of minimum quantity lubrication (MQL). In the present work the authors have reviewed the current state of the art in MQL with a particular focus on drilling, turning, milling and grinding machining operations.Overall it is concluded that MQL has huge potential as a substitute for conventional flood cooling.

Metal Matrix Composites (MMC) is a material which has been widely used in the aerospace and automobile industries since the 1980s, and has been classified as a hard to machine material. During the intervening years only a limited amount of research has been conducted into the cutting action of MMCs. As with traditional materials it is important to understand the wear mechanisms that contribute to tool wear reducing tool life. This review has been carried out to establish the optimum machining parameters vital to maximizing tool life whilst producing parts at the desired quantity and quality. The objective of this research is to evaluate the effectiveness of the machining parameters for these hard to machine material MMC.

The machining of aerospace materials, such as metal matrix composites, introduces an additional challenge compared with traditional machining operations because of the presence of a reinforcement phase (e.g. ceramic particles or whiskers). This reinforcement phase decreases the thermal conductivity of the workpiece, thus, increasing the tool interface temperature and, consequently, reducing the tool life. Determining the optimum machining parameters is vital to maximising tool life and producing parts with the desired quality. By measuring the surface finish, the authors investigated the influence that the three major cutting parameters (cutting speed (50-150 m/min), feed rate (0.10-0.30 mm/rev) and depth of cut (1.0-2.0 mm)) have on tool life. End milling of a boron carbide particle-reinforced aluminium alloy was conducted under dry cutting conditions. The main result showed that contrary to the expectations for traditional machined alloys, the surface finish of the metal matrix composite examined in this work generally improved with increasing feed rate. The resulting surface roughness (arithmetic average) varied between 1.15 and 5.64 mm, with the minimum surface roughness achieved with the machining conditions of a cutting speed of 100 m/min, feed rate of 0.30 mm/rev and depth of cut of 1.0 mm. Another important result was the presence of surface microcracks in all specimens examined by electron microscopy irrespective of the machining condition or surface roughness.

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This paper presents a new approach to the tolerance synthesis of the component parts of assemblies by simultaneously optimizing three manufacturing parameters: manufacturing cost, including tolerance cost and quality loss cost; machining time; and machine overhead/idle time cost. A methodology has been developed using the Genetic Algorithm (GA) technique to solve this multiobjective optimization problem. The effectiveness of the proposed methodology has been demonstrated by solving a wheel mounting assembly problem consisting of five components, two subassemblies, two critical dimensions, two functional tolerances, and eight operations. Significant cost saving can be achieved by employing this methodology.

Remanufacturing is recognized as a major circular economy option to recover and upgrade functions from post-use products. However, the inefficiencies associated with operations, mainly due to the uncertainty and variability of material flows and product conditions, undermine the growth of remanufacturing. With the objective of supporting the design and management of more proficient and robust remanufacturing processes, this paper proposes a generic and reconfigurable simulation model of remanufacturing systems. The developed model relies upon a modular framework that enables the user to handle multiple process settings and production control policies, among which token-based policies. Customizable to the characteristics of the process under analysis, this model can support logistics performance evaluation of different production control policies, thus enabling the selection of the optimal policy in specific business contexts. The proposed model is applied to a real remanufacturing environment in order to validate and demonstrate its applicability and benefits in the industrial settings.

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