Manufacturing structural monolithic components for the aerospace market often involves machining distortion, which entails high costs and material and energy waste in industry. Despite the development of distortion calculation and avoidance tools, this issue remains unsolved due to the difficulties in accurately and economically measuring the residual stresses of the machining blanks. In the last years, the on-machine layer removal method has shown its potential for industrial implementation, offering the possibility to obtain final components from blanks with measured residual stresses. However, this measuring method requires too long an implementation time to be used in-process as part of the manufacturing chains. In this sense, the objective of this paper is to provide a machining distortion prediction method based on bulk residual stress estimation and hybrid modelling. The bulk residual stresses estimation is performed using reduced layer removal measurements. Considering bulk residual stress data and machining-induced residual stress data, as well as geometry and material data, real-part distortion calculations can be performed. For this, a hybrid model based on the combination of an analytical formulation and finite element modelling is employed, which enables us to perform fast and accurate calculations. With the developments here presented, the machining distortion can be predicted, and its uncertainty range can be calculated, in a simple and fast way. The accuracy and practicality of these developments are evaluated by comparison with the experimental results, showing the capability of the proposed solution in providing distortion predictions with errors lower than 10% in comparison with the experimental results. Full article

Micro-EDM is an unconventional technology used to machine every type of electrically conductive material regardless of its mechanical properties. Material removal occurs through electrical discharges between the workpiece and the electrode immersed in a dielectric fluid. In drilling operations, the technology is able to realise microholes with excellent quality in terms of precision, quality surface, roundness, and taper to the detriment of the machining time, which is less than other technologies. Several efforts are being made to improve different features related to the process performance that are severely affected by both the operative conditions, such as the electrode material or the type of dielectric, and process parameters. The typical indexes used to characterise the performance are the machining time, the material removal rate, and the geometric indexes. These indexes are very effective and are easily measurable, but they do not give information about the evolution of the drilling process, which could be irregular due to the different phenomena occurring during machining. The aim of this paper is the development of a method able to elaborate the motion law of the electrode during the micro-EDM drilling operation. In order to do this, a single hole was manufactured in several steps, recording both the machining time and electrode wear for each step. In this way, the actual position of the electrode during the drilling can be measured without the use of a predictive model for electrode wear. It was tested to confirm that the multistep procedure did not introduce new phenomena, in contrast to the traditional drilling operation. This method was used to study the effects of the electrode diameter, the type of electrode, the length of the electrode out of the spindle, and the entity of the run-out on the process performance. The tests were executed on titanium alloy sheets using a tungsten carbide electrode and hydrocarbon oil as the dielectric. It was found that the descent of the electrode into the workpiece was not regular, but it depended on the level of debris concentration in the machining zone. The debris concentration was influenced by the type and diameter of the electrode, its length out of the spindle, and, to a lesser extent, the run-out. This method was found to be a useful method for an in-depth analysis of the micro-EDM drilling process, contributing to a better understanding of the physical aspects of the process. Full article

While Inconel 718 is a widely used engineering material in industrial fields such as the aerospace and automotive fields, the machined surface integrity has a significant effect on the performance of its components and parts. In this work, the laser-assisted micro-milling process of Inconel 718 is investigated using a combination of experiments and finite element simulations. Firstly, an experimental platform of laser-assisted milling is built, and a three-dimensional thermal–mechanical coupled finite element model of laser-assisted milling of Inconel 718 is then established. Secondly, laser-assisted milling experiments and finite element simulations are conducted to investigate the impact of laser assistance on cutting force, chip morphology, tool wear and surface topography of Inconel 718 under a milling process. The results indicate that laser-assisted milling results in a moderate reduction in cutting forces while enhancing surface integrity and chip continuity, as compared with ordinary milling. Thirdly, orthogonal experiments of laser-assisted milling of Inconel 718 are conducted to discover the optimal processing parameters, including spindle speed, feed per tooth, milling depth and laser parameters. Finally, single-factor experiments are conducted to investigate the effect of laser power on cutting force, chip morphology, tool wear, groove burr and surface roughness in the laser-assisted milling of Inconel 718. And, a minimal surface roughness Sa of 137 nm for Inconel 718 accompanied by minimal tool wear is experimentally obtained via laser-assisted milling. These findings highlight the effectiveness of applying laser assistance in enhancing the machinability of difficult-to-machine materials for achieving desirable machined surface integrity. Full article

Diamond burnishing is a finishing precision machining that is often used to improve the quality characteristics of previously machined surfaces. With its help, the surface roughness can be reduced, the surface hardness can be increased, and the tensile stresses remaining in the surface after cutting can be transformed into compressive ones, and these changes can increase the service life of the components. Diamond burnishing was typically developed for processing cylindrical surfaces and is most often used for this type of surface. In this manuscript, we present a new method with the help of sliding burnishing, which can also be used on flat surfaces. By using the clamping head of a special tool into the main spindle of the vertical milling machine and moving it along a suitable path, the flat surface can be burnished. Machining experiments were carried out with the new type of tool on general-purpose, unalloyed, structural carbon steel samples on which the flat surfaces were previously generated by face milling. The examined parameters were the burnishing force F, the feed f_b, and the number of passes (NoP). The L9 Taguchi experiment design was applied for executing flat slide burnishing, and the examination was conducted by ANOVA analysis. This research contributes to the field by providing insights into optimizing the burnishing process parameters for achieving desired surface quality in milling operations. Full article

Diamond tools are extensively used in ultra-precision machining due to their exceptional performance. However, when machining challenging materials like Ti6Al4V, diamond tools experience significant wear due to poor machining properties and catalytic effects. Tool wear not only impacts machining quality but also escalates machining costs and energy consumption. Cutting fluids are commonly employed to mitigate interfacial reactions and suppress tool wear. However, traditional cutting fluids are difficult to penetrate the cutting area and have limited lubrication and cooling capabilities. Therefore, in this paper, a technique combining graphene nanofluid and minimum-quantity lubrication (MQL) is used to suppress diamond tool wear. Firstly, micro-milling experiments for Ti6Al4V alloy are conducted using diamond tools in the graphene nanofluid MQL and under a dry environment. The experimental results show that tool wear is effectively suppressed by graphene nanofluids. Subsequently, the cutting process in both environments (graphene nanofluid MQL, dry) is simulated. The suppression mechanism of graphene nanofluid MQL for diamond tool wear is evaluated from phase transition, atomic transfer process, and amorphous behavior of diamond structure. The simulation results show that the contact characteristics, cutting force, and cutting temperature are improved by graphene nanofluids. Tool wear is effectively reduced. In addition, the removal efficiency of workpiece materials has also been improved. This work provides a technical basis for exploring the application of graphene nanofluids in diamond tool damage suppression and micro-milling. Full article

The roughness origin of machined surfaces is caused by the following physical causes: the copying of the shape and the roughness of the cutting part of the tool into the workpiece, the existence of vibration of the tool, and the existence of the build-up edge (BUE) on the cutting edge. The current work aims to analyze the vibration amplitude of tools. The roughness of the machined surfaces was observed on hardened steel workpieces. Internal turning technology was used, and we used several different boring bars (steel; carbide; tuned mass damper—TMD; impact damper—ID) and an internal turning operation using CBN inserts. We revealed the tool’s slenderness coefficient (TSC) values for stable cutting operations. For the steel holder, the value is TSC ≤ 4.25; for the carbide holder, the value is TSC ≤ 5.5; for the TMD holder, the value is 4.5 ≤ TSC ≤ 7.75; and for the ID holder, the value is TSC ≤ 8. The surface’s roughness was practically unchanged within the limits of stable machining. However, if the tools exceed the presented stable limits, vibration and roughness parameters deteriorate significantly; an example parameter (Ra) deteriorated from 0.350 μm to 1.832 μm. Full article

Consistent high volumetric performance of machine tools is an essential requirement for high-quality machining. Periodic machine tool calibration ensures said performance and allows for timely corrective actions preventing scrap or rework. Reducing the duration of the calibration process decreases associated cost through non-productive downtime and allows for data acquisition in thermal real-time. The authors enhance an indirect calibration method based on measuring points within the machine volume using a laser tracker by removing the necessity for standstill. To circumvent requiring high fidelity space and time synchronization between metrology system and machine tool, only deviations perpendicular to the path are considered. To do so, the 3D laser tracker data are rotationally transformed such that one axis aligns with the motion direction and can subsequently be omitted as input data for the system of equations solving for geometric errors. Due to the absence of unique measurement-point-to-machine-point mapping, data alignment between nominal path and measurement data is proposed as an iterative alignment process of points to path. The method is tested simulatively and experimentally. It demonstrated conformity to the simulation as well as to the pre-existing calibration method based on laser trackers and shows good agreement with the direct calibration device API XD Laser. Full article

Honing processes are abrasive machining processes which are commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These processes can be employed to obtain a cross-hatched pattern on the internal surfaces of cylinders. In this present study, fuzzy artificial neural networks are employed for modeling surface roughness parameters obtained in finishing honing operations. As a general trend, main factors influencing roughness parameters are grain size and pressure. Mean spacing between profile peaks at the mean line parameter, on the contrary, depends mainly on tangential and linear velocity. Grain Size of 30 and pressure of 600 N/cm² lead to the highest values of core roughness (Rk) and reduced valley depth (Rvk), which were 1.741 µm and 0.884 µm, respectively. On the other hand, the maximum peak-to-valley roughness parameter (Rz) so obtained was 4.44 µm, which is close to the maximum value of 4.47 µm. On the other hand, values of the grain size equal to 14 and density equal to 20, along with pressure 600 N/cm² and both tangential and linear speed of 20 m/min and 40 m/min, respectively, lead to the minimum values of core roughness, reduced peak height (Rpk), reduced valley depth and maximum peak-to-valley height of the profile within a sampling length, which were, respectively, 0.141 µm, 0.065 µm, 0.142 µm, and 0.584 µm. Full article

The machining of Nitinol shape memory alloys (SMA) through conventional machining techniques imposes several challenges due to the alloys’ comprehensive mechanical qualities. Wire electrical discharge machining (WEDM) process is a non-conventional machining technique that is suitable mainly for producing complex shape geometries with excellent surface features for difficult-to-cut materials. The current study attempted the use of a near-dry WEDM process for Nitinol SMA with the consideration of multiple response variables. The studied literature and machine capabilities have identified input factors of pulse-on-time (T_on), pulse-off-time (T_off), and current and output factors of MRR, SR, and RLT. Through the Taguchi approach, a total of nine experimental trials were designed to analyze the performance of the process. The statistical significance of input factors on the performance measures was studied with the help of ANOVA techniques. Statistical analysis for all the output measures has shown that the generated regression terms had a significant influence. For single output measures, the current was found to have a substantial effect on both MRR and SR, while T_off was the most significant contributor in the case of RLT. The obtained results of residual plots for all performance measures implied good ANOVA results. The effect of near-dry WEDM variables was studied on output measures through main effect plots. Grey relational analysis (GRA) has been employed to attain optimal parametric settings of multiple performance measures. GRA technique for the optimal parametric settings of simultaneous performance measures of MRR, SR, and RLT was found to have a T_on of 30 µs, T_off of 24 µs, and current of 4 A. Validation trials were conducted to check the adequacy of the GRA technique. The minor acceptable deviation was recorded among the anticipated and recorded values. This clearly reveals the acceptability of the integrated approach of the Taguchi–Grey method. The surface morphology for the near-dry and wet-WEDM has also been investigated through scanning electron microscopy (SEM). The author considers that the present study will be beneficial for users working in WEDM and near-dry WEDM processes for hard machining materials. Full article

Acoustic emission-based monitoring of the milling process holds the potential to detect undesired damages of fibre-reinforced plastic workpieces, such as delamination or matrix cracking. In addition, abrasive tool wear, tool breakage, or coating failures can be detected. As measurements of the acoustic emission are impacted by attenuation, dispersion, and reflection as it propagates from source to sensor, the waveforms, amplitudes, and frequency content of a wave packet differ depending on the propagation length in the workpiece. Since the distance between acoustic emission sources and a stationary sensor attached to the workpiece changes continually in circumferential milling, the extraction of meaningful information from the raw measurement data is challenging and requires appropriate signal processing and frequency-dependent amplification. In this paper, practical and robust approaches, namely experimentally identified transfer functions and frequency gain parameter tables for attenuation modelling, which in reverse enable the reconstruction of frequency spectra emitted at the acoustic emission source, are presented and discussed. From the results, it is concluded that linear signal processing can largely compensate for the influence of attenuation, dispersion, and reflection on the frequency spectra and can therefore enable acoustic emission based process monitoring. Full article

This paper introduces a novel approach to in-process monitoring of the cutting temperature at the tool-chip interface (TCI). Currently, there are no tools available in the commercial market for measuring and monitoring cutting processes at the TCI region. Therefore, most of the studies about evaluating cutting temperature rely on simulation results without knowing the true temperature at the actual TCI region. In addition, recent cutting temperature measurement techniques have measurement errors occurring resulting from difficulty in estimations at the TCI region. However, the proposed method enables the measuring of cutting temperature by directly probing the localized TCI using a cutting tool coated with dielectric material. The study was conducted by utilizing the impedance characteristics of the dielectric outer layer of the cutting tool. A chemical vapor deposition (CVD) diamond coated insert that is commercially available was considered for the study to avoid wear effect. Impedance response of the dielectric layer under varying temperature conditions is assessed by Nyquist diagram using an impedance analyzer. The result of the Nyquist diagram showed temperature-dependent impedance characteristics that showed good agreement with the results from the thermal experiment which was a comparison between impedance response and elevated temperature. The impedance at the TCI for monitoring cutting temperature is measured under a turning process on a lathe using a constant current source. The impedance responses showed a significant decrease in impedance under various machining conditions which indicates a rise in cutting temperature. Moreover, different machining conditions showed different temperature profiles. The impedance responses were further characterized for depth of contact, which found that a drop in impedance corresponded to an increase in depth of contact. Therefore, the study showed that in-process monitoring of the cutting temperature is possible using an impedance model of the dielectric coating layer at the local TCI. Furthermore, with its versatility, this method is expected to measure the vibration, chatters, cutting force, and so on, as the results showed that impedance is not only sensitive to temperature but also to contact area. The application and expectation of this study is to provide real-time machining data to help end users in manufacturing industry to improve product quality, productivity, and prolonged lifespan of cutting tools. Full article

Acoustic emission (AE) signals collected from different locations might provide various sensitivities to tool wear condition. Studies for tool wear monitoring using AE signals from sensors on workpieces has been reported in a number of papers. However, it is not feasible to implement in the production line. To study the feasibility of AE signals obtained from sensors on spindles to monitor tool wear in micro-milling, AE signals obtained from the spindle housing and workpiece were collected simultaneously and analyzed in this study for micro tool wear monitoring. In analyzing both signals on tool wear monitoring in micro-cutting, a feature selection algorithm and hidden Markov model (HMM) were also developed to verify the effect of both signals on the monitoring system performance. The results show that the frequency responses of signals collected from workpiece and spindle are different. Based on the signal feature/tool wear analysis, the results indicate that the AE signals obtained from the spindle housing have a lower sensitivity to the micro tool wear than AE signals obtained from the workpiece. However, the analysis of performance for the tool wear monitoring system demonstrates that a 100% classification rate could be obtained by using spindle AE signal features with a frequency span of 16 kHz. This suggests that AE signals collected on spindles might provide a promising solution to monitor the wear of the micro-mill in micro-milling with proper selection of the feature bandwidth and other parameters. Full article