Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Porous metals, typically produced through powder metallurgy, represent a class of relatively new materials with wide industrial applications, lately extending into the microscale domain. Although produced in near-net shapes, most components fabricated from these materials still require some form of secondary machining. Despite the progress made in the field, relatively little is known either on the inherent cutting mechanism or on the behaviour of these materials under micromachining conditions. The present study reviews the main cutting theories proposed in macroscale machining, along with one of the primary parameters used to describe its machinability performances, namely cutting forces. Then, the feasibility of macroscale concepts is discussed in the context of micromachining technology that is characterized by comparable tool and pore sizes. The microslot cutting experiment performed in a porous titanium sample outlined the relative interplay between the magnitude of the cutting force and porosity of the material. Based on this, it was concluded that the impact of structural porosity on cutting forces experienced during micromachining is significant and therefore further in-depth investigations will be required.

Micro-electrodischarge machining (EDM) can produce microhole and other complex three-dimensional features on a wide range of conductive engineering materials such as titanium super alloy, inconel, etc. The micromachining of titanium super alloy (Ti—6Al—4V) is in very high demand because of its various applications in aerospace, automotive, biomedical, and electronics industries, owing to its good strength-to-weight ratio and excellent corrosion-resistant properties. The present research study deals with the response surface methodology (RSM) and artificial neural network (ANN) with back-propagation-algorithm-based mathematical modelling. Furthermore, optimization of the machining characteristics of micro-EDM during the microhole machining operation on Ti—6Al—4V has been carried out. The matrix experiments have been designed based on rotatable central composite design. Peak-current (Ip), pulse-on time (Ton), and dielectric flushing pressure have been considered as process parameters during the microhole machining operation and these parameters were utilized for developing the ANN predicting model. The performance measures for optimization were material removal rate (MRR), tool wear rate (TWR), and overcut (OC). The ANN model was developed using a back-propagation neural network algorithm, which was trained with response values obtained from the experimental results. The Levenberg—Marquardt training algorithm has been used for a multilayer feed-forward network. The developed model was validated using data obtained by conducting a set of test experiments. The optimal combination of process parametric settings obtained are pulse-on-time of 14.2093 μs, peak current of 0.8363 A, and flushing pressure of 0.10 kg/cm² for achieving the desired MRR, TWR, and OC. The output of RSM optimal data was validated through experimentation and the ANN predicted model. A close agreement was observed among the actual experimental, RSM, and ANN predictive results.

Understanding and estimating the energy consumed by machining are essential tasks as the energy consumption during machining is responsible for a substantial part of the environmental burden in manufacturing industry. Facing the problem, the present paper aims to analyse the correlation between numerical control (NC) codes and energy-consuming components of machine tools, and to propose a practical method for estimating the energy consumption of NC machining. Each energy-consuming component is respectively estimated by considering its power characteristics and the parameters extracted from the NC codes, and then the procedure estimating energy consumption is developed by accounting for the total energy consumption of the components via the NC program. The developed method is verified by comparing the estimated energy consumption with the actual measurement results of machining two test workpieces on two different machine tools, an NC milling machine and an NC lathe, and is also applied to evaluate the energy consumption of two different NC programs on the NC milling machine. The results obtained show that the method is efficient and practical, and can help process planning designers make robust decisions in choosing an effective energy-efficient NC program.

Optimizing the energy efficiency of processes has become a priority in the manufacturing sector; driven by soaring energy costs and the environmental impact caused by high energy consumption levels. The energy consumed by a machine tool performing a turning process consists of not only the energy required by the tool tip for material removal but also the energy used for auxiliary functions. Traditionally, the energy required for the cutting process is estimated based on cutting force prediction equations. However, this estimation is limited to the energy consumption of the tool tip. Thus, the aim of this paper is to develop a reliable method to predict the total energy consumption of a selected machine tool performing a turning operation. In order to compare the energy consumption under different cutting conditions, the specific energy consumption is defined as a functional unit: the energy consumed to remove 1 cm³ of material. An empirical model is obtained based on power measurements under various cutting conditions, and it is able to provide a reliable prediction of energy consumption for given process parameters. Additional investigations are conducted in order to understand and explain each coefficient in the energy consumption model.

Hybrid machining processes growing popularity in the processing of difficult-to-cut materials due to their distinct merits over individual machining processes attributed by an amalgamation of two or more machining mechanisms simultaneously. This research study deals with the response surface methodology and artificial neural network with backpropagation algorithm–based mathematical modeling of electro-discharge diamond grinding of Inconel 718 superalloy. The matrix experiments were designed based on central composite design. The wheel speed, current, pulse-on-time, and duty factor were chosen as control factors, while material removal rate and average surface roughness ( R_a) were chosen as performance parameters. The analysis of variance test shows that the wheel speed is the major factor influencing both the material removal rate and the R_a and contributes 89.03% and 79.10% on material removal rate and R_a, respectively, followed by current which contributes 4.43% and 8.38% on material removal rate and R_a, respectively. The modeling and predictive abilities of developed artificial neural network model (4-24-2) were related to the response surface methodology model using root mean square error and absolute standard deviation. The predicted values of material removal rate and R_a by response surface methodology and artificial neural network are in close agreement with the actual experimental results.

It is estimated that the industrial sector is responsible for 29% of the United States’ greenhouse gas emissions. Recent efforts have sought to reduce the carbon footprint of manufacturing activities. Research suggests that a key to further reducing industrial-based greenhouse gas emissions is to more accurately characterize the carbon footprint of manufacturing processes. Life cycle assessment is a powerful tool that can be utilized to characterize the life cycle environmental impacts of a process. Current life cycle inventory databases that are used in life cycle assessments only have limited coverage on manufacturing operations. This article develops a parameterized process model for computer numerical control grinding, which enables the calculation of life cycle inventory data in a relatively rapid fashion and has some substantial characteristics, such as transparency, engineering quality, and the ability to reflect changes when new information is secured. Surface grinding of cobalt–chromium alloy knee implants is used as a case study to demonstrate the approach.

The new developed metal/composite co-cured material composed of carbon fiber–reinforced plastic and Al phases has been increasingly applied for manufacturing of attitude control flywheel in aerospace industry. However, drilling of co-cured material is still a challenging task to produce holes with high quality and low cost in the assembly chain and dynamic balance debugging of attitude control flywheel. In other words, the relevant mechanisms and experimental findings involved in the drilling process of carbon fiber–reinforced plastic/Al co-cured material is not clearly defined, which impedes the progress of attitude control flywheel production. To this end, this article specially addresses the experimental studies on the drilling process of carbon fiber–reinforced plastic/Al co-cured material with standard TiAlN-coated cemented carbide twist drill. The significance of this work aims to reveal the regardful cutting responses of the hole characteristics and tool wear modes during the practical drilling process of co-cured material. A full factorial experiment including three levels of feed rate and four levels of cutting speed was performed. The hole diameter shows different values in different positions while it indicates consistent pattern regardless of the cutting variables: the largest in the Al phase, followed by the upper and lower carbon fiber–reinforced plastic phases, respectively. Grooves and matrix degradation are the major machining defects for carbon fiber–reinforced plastic layers, while a great chip debris adhered to the machined surface is the case for Al layer. Subsequent wear analysis showed that abrasion was mainly maintained at the vicinity of major/minor cutting edges and drill edge corner, followed by chip adhesion on the chisel edge region. Carbide substrate of drill flank face is exposed, and thereafter cavities are formed under the strong mechanical abrasion. These results could provide several implications for industrial manufacturers during the attitude control flywheel production.

In this study, a new kind of ultra-high-speed combined machining of electrical discharge machining and arc machining was developed. A rotating graphite electrode and a workpiece were connected to the negative and positive poles of the power supply, which consisted of a pulse generator and direct current power. Efficient electrode injection flushing and side flushing yielded a noncontinuous arc and achieved a maximum material removal rate of 12,688 mm³/min at a relative electrode wear ratio of 2.3% during quenched mold steel machining. The characteristics of the combined machining process were determined by studying the effects of flushing, rotation, peak current, and peak voltage on process performance, such as on the material removal rate and relative electrode wear ratio. The recast layer and surface defects were also investigated. The result shows that the novel combined machining process has great potential to reduce machining time.

In this work, the optimization of a finish hard turning process for the machining of D2 steel with ceramic tools is carried out. With the help of replicate experimental data at 27 different cutting conditions, radial basis function neural network models are fitted for predicting the surface roughness and tool wear as functions of cutting speed, feed, and machining time. A novel method for neural network training is proposed. The trained neural network models are used as a black box in the optimization routine. Two types of optimization goal are considered in this work: minimization of production time and minimization of the cost of machining. One novel feature of this work is that the surface roughness is considered in the tool life instead of as a constraint. This is possible owing to the availability of the relationship of surface roughness with time in the neural network model. The results of optimization will be dependent on the tool change time and the ratio of operating cost to tool change cost. The results have been presented for the possible ranges of these parameters. This will help to choose the appropriate process parameters for different situations, and a sensitivity analysis can be easily carried out.

Porous metals with high melting points can be manufactured by the lost carbonate sintering (LCS) process either via the dissolution route or via the decomposition route. In the current paper, porous copper and steel samples with porosity in the range of 50 to 85 per cent and cell size in the range of 50 to 1000 μm have been produced via the decomposition route. The effectiveness of carbonate loss and the characteristics of the decomposition route have been studied. In comparison with the dissolution route, the decomposition route can be applied to a wider range of conditions and often requires shorter times to achieve a complete carbonate removal. The porous metal samples produced by the decomposition route generally have higher tensile strength and higher flexural strength than those produced by the dissolution route.