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Machining optimization has been studied for low machining cost, high production rate, and energy saving. Nevertheless, most of the studies did not consider the part geometry. The complex part geometry results in varying contact conditions between the cutting tool and the workpiece. Therefore, the conventional tool life equation may not be applicable, and it is impossible to expect the same performance of machining different parts even with the same cutting parameters. In addition, the amount of material removal is different from part to part. Either the underuse or overuse of the tool is considered production loss in machining process. As a result, it is better for the cutting tool to be utilized until it reaches the end of its life when the operation is completed. Taking the conditions above into considerations, an objective function including the machining cost and the loss of tool usage is proposed in this study. Three turning operations, one straight turning and two profiling, were investigated to show the effect of part geometry on the machining optimization and tool wear. The results showed the different optimization processes and tool wear patterns although the initial cutting parameters and the amount of material removal were identical. The optimization processes were converged in 5 iterations. The cost of three cases reduced 34%, 33%, and 19%, respectively, with different optimized cutting parameters, and the tool approached the end of its life when the operation was done. [ABSTRACT FROM AUTHOR]

In the present investigation, machining performance of Ti6Al4V is studied under dry environment, pressurized air supply and distilled water based Minimum Quantity Lubrication (MQL). Machining (turning) performance is assessed in purview of cutting force (tangential component) magnitude, tool-tip temperature, width of tool flank wear progression, morphology of evolved chips and severity of vibrations at varied cutting speeds as well as cooling media. Characteristic features of spatial temperature distribution profile (at the vicinity of tool-tip) as influenced by varied cooling media are studied with the help of thermographs of the cutting zone. Mechanisms of cutting tool wear are studied as well. It is experienced that amongst three cutting environments tested, application of water-MQL is beneficial for machining of Ti6Al4V at low cutting speed. Severity of vibrations gets 50.39% reduced during machining under water-MQL than dry condition. Consequently, as compared to dry machining, water-MQL causes 79% reduced tool flank wear and 82% reduced crater wear at low cutting speed. Under water-MQL, reduced tool-tip temperature (66% reduced than dry condition) suppresses severity of tool wear. In comparison with high cutting speed, performance of water-MQL is found much better at low cutting speed. 'Unaffected zones' are identified at the worn-out tool rake face under water-MQL. High amplitude of vibration (maximum absolute mean value) causes low chip-segmentation ratio. On the contrary, chip reduction coefficient gets truncated with decrement in vibration amplitude. Vibration amplitude has positive influence on degree of chip-curl. [ABSTRACT FROM AUTHOR]

In the machining process, the machining performance which mainly refers to machined surface quality and cutting forces is hard to predict under different tool wear status. In this work, an improved case-based reasoning (ICBR) method is proposed to predict both the cutting force and machined surface roughness. With the emergence of new problem, ICBR method obtains solutions to new problem through case retrieval and reuse. In case retrieval stage, K similar cases to the new problem were retrieved using K nearest neighbor method. By means of the K similar cases, support vector regression machine (SVR) model was established to give the solution of the new problem in case of the reuse stage of ICBR method. Artificial neural network (ANN) was introduced to estimate the influence of machining parameters and tool wear on machining performance. As the ANN and SVR models contain unknown parameters, the novel particle swarm optimization algorithm was proposed to train these models for its capability of fast convergence and global optimum. The proposed ICBR method was used to predict the surface roughness and cutting force. The results showed the proposed ICBR method can give superior prediction accuracy and lower Mean square error than other popular intelligent models. Meantime, the ICBR method possesses good robustness and can be used for the actual machining process. [ABSTRACT FROM AUTHOR]

This paper presents the result of pulsating lubrication strategy (PLS), an alternative metalworking fluids approach to improve machining performance. The formation of built-up edge (BUE) and built-up layer (BUL) is among the problems encountered during the machining of aluminum alloy, which degrades cutting performance and thus affects the machined surface quality. This study focuses on chip removal effectiveness by jetting a coolant intermittently of PLS on the cutting tool to create a hydraulic shock. The water stream coolant was set to pulsate at 2000 pulse/min with the pressure of 0.76 MPa. Applying this method improves chip evacuation. Hence, minimizing the formation of BUE and BUL more effectively than dry cutting. [ABSTRACT FROM AUTHOR]

Since the 1960s, the Inconel Alloy 718 has been a standard nickel-based superalloy due to its high strength, balanced mechanical properties, and strong corrosion resistance at relatively low costs and has been widely used in critical aircraft engine components. With the aim of improving productivity and product quality by implementing advanced tools and new process designs, models such as the FE model are utilized to predict the machining performance such as the cutting forces, the tool life, and the surface integrity. In the research area of FEM chip formation simulation, the influence of flank wear on predictions has not been investigated, especially not on the underestimation of the cutting normal force. In this paper, a 2D FEM chip formation model with the coupled Eulerian-Lagrangian (CEL) method has been built to predict cutting forces as well as other material loadings (Brinksmeier et al. Procedia CIRP 13:429–434, 2014; Buchkremer and Klocke Wear 376-377:1156–1163, 2017) in the machining of Direct Aged 718. In order to validate the performance of the FE model, fundamental investigations have been performed in orthogonal cutting with different cutting parameters. Two kinds of cemented carbide cutting tools with different cobalt contents have been applied to achieve different tool wear behaviors. Moreover, the underestimation of the cutting normal force by FEM chip formation simulation has been investigated and solved in consideration of the flank wear. The introduced FE-modeling approach shows precise predictions in terms of the cutting forces and the chip formation. [ABSTRACT FROM AUTHOR]