Micromilling can fabricate complex features in a wide range of engineering materials with excellent finish but the limited flexural stiffness of the micro-end mill can result in catastrophic tool failure. This issue can be overcome by using high rotational speeds. Note that the combination of high rotational speeds and low flexural stiffness can induce process instability which is aggravated by the accelerated wear of the microtools at high speeds, specifically, for Ti-alloys. The effect of progressive tool wear on the stability has been investigated in micromilling of Ti6Al4V. For incorporating tool wear, the cutting force coefficients are modeled as a function of instantaneous cutting edge radius (CER). These coefficients also depend on feed/flute and the initial CER of the micro-tool (due to inherent variability in grinding process). A significant increase (85%-114%) in the instantaneous CER is observed with an increase in the length of cut. At high rotational speeds, a zero-order approximation frequency domain model may not be accurate. Therefore, a 2-DOF time domain model based on semidiscretization method has been used to characterize the evolution of stability limits with an increase in the length of cut. The frequency spectra of velocity and surface topography have been analyzed for the model validation. The progressive tool wear affects the stability limits along with the initial CER and the feed/flute. At higher speeds (90,000-110000 rpm), the effect of progressive tool wear is pronounced and the stability limits reduce by ~30% in that range.

This paper evaluates the performances of dry, minimum quantity lubrication (MQL), and MQL with nanofluid conditions in turning of the most common titanium (Ti) alloy, Ti-6Al-4 V, in a solution treated and aged (STA) microstructure. In particular, the nanofluid evaluated here is vegetable (rapeseed) oil mixed with small concentrations of exfoliated graphite nanoplatelets (xGnPs). This paper focuses on turning process that imposes a challenging condition to apply the oil or nanofluid droplets directly onto the tribological surfaces of a cutting tool due to the uninterrupted engagement between tool and work material during cutting. A series of turning experiments was conducted with uncoated carbide inserts, while measuring the cutting forces with a dynamometer under the dry, MQL and MQL with nanofluid conditions supplying oil droplets externally from our MQL device. The inserts are retrieved intermittently to measure the progress of flank and crater wear using a confocal microscopy. This preliminary experimental result shows that MQL and in particular MQL with the nanofluid significantly improve the machinability of Ti alloys even in turning process. However, to attain the best performance, the MQL conditions such as nozzle orientation and the concentration of xGnP must be optimized.

Conventional machining of Ti6Al4V/TiC composites is a very difficult process, which exhibits a peculiar cutting force pattern where the thrust forces are higher than the tangential forces. This behavior results in rapid tool wear and consequently very short tool life. This study is concerned with describing the reasons for the attendant behavior using experimentally validated 3D finite element simulations and alleviating this behavior via laser assisted machining (LAM). Simulations were conducted using an equivalent homogeneous model (EHM) and a multiscale heterogeneous model (MHM) of the Ti6Al4V/TiC composite. Results showed a good agreement between the tangential forces obtained from experiments, EHM, and MHM for conventional machining and LAM. However, only the MHM was able to successfully predict the unusual high thrust forces. The MHM simulation results showed that the tool/particle interaction along the tool nose region presented the highest resistance due to the high resistance against pushing the TiC particles by the tool into the machined surface. This resistance results from the efficient load transfer capability between the particles and the matrix below the machined surface. When using LAM, the stated resistance was decreased by the reduction in load transfer capability of the Ti6Al4V/TiC workpiece such that the thrust and tangential forces were reduced by 78% and 37%, respectively, according to the MHM simulation. The experimental results showed that the tool wear was improved by 68% by LAM. All the results demonstrated that the MHM successfully captured the underlying machining mechanism of the Ti6Al4V/TiC composites.

Titanium alloy Ti-5Al-5V-3Cr-0.5Fe (Ti-5553) is a new generation of near-beta titanium alloy that is commonly used in the aerospace industry. Machining is one of the manufacturing methods to produce parts that are made of this near-beta alloy. This study presents the machining performance of new generation near-beta alloys, namely, Ti-5553, by focusing on a high-speed cutting process under cryogenic cooling conditions and dry machining. The machining experiments were conducted under a wide range of cutting speeds, including high speeds that used liquid nitrogen (LN 2 ) and carbon dioxide (CO 2 ) as cryogenic coolants. The experimental data on the cutting temperature, tool wear, force components, chip breakability, dimensional accuracy, and surface integrity characteristics are presented and were analyzed to evaluate the machining process of this alloy and resulting surface characteristics. This study shows that cryogenic machining improved the machining performance of the Ti-5553 alloy by substantially reducing the tool wear, cutting temperature, and dimensional deviation of the machined parts. The cryogenic machining also produced shorter chips as compared to dry machining.

Trochoidal (TR) tool paths have been a popular means in high-speed machining for slot cutting, owing to its unique way of cyclically advancing the tool to avoid the situation of a full tool engagement angle suffered by the conventional type of slot cutting. However, advantageous in lowering the tool engagement angle, they sacrifice in machining efficiency—to limit the tool engagement angle, the step distance has to be carefully controlled, thus resulting in a much longer total machining time. Toward the objective of improving the machining efficiency, in this paper, we propose a new type of TR tool path for milling an arbitrary curved slot. For our new type of TR tool path, within each TR cycle, rather than moving circularly, the tool moves in a particular way such that the material removal rate is maximized while the given maximum engagement angle is fully respected. While this type of TR tool path works perfectly only for circular slots (including straight ones), by means of an adaptive decomposition and then a novel iso-arc-length mapping scheme, it is successfully applied to any general arbitrarily curved slot. Our experiments have confirmed that, when compared with the conventional TR tool paths, the proposed new type of TR tool path is able to significantly reduce the total machining time by as much as 25%, without sacrificing the tool wear.

Short tool service life is always a major concern when milling hard materials, such as Ni-based superalloy. In the current research of tool life optimization in multi-axis machining of freeform surfaces, the focus is mostly on choosing suitable cutting parameters and better application of coolant. In this paper, aiming at averaging the tool wear on the entire cutting edge and hence prolonging the tool service life, we report a study on how to generate a multilayer toolpath with a varying tool lead angle for multi-axis milling of an arbitrary freeform surface from an initial raw stock. The generated toolpath is guaranteed to be free of chatter, which is well known for its detrimental effect on the cutting edge. In this study, we first experimentally construct the chatter stability lobe diagram, which reveals the relationship between the lead angle and the cutting depth. With the chatter stability lobe diagram as the major constraint, we then generate the machining toolpath by selecting a proper pair of the best lead angle and cutting depth along the toolpath. While the proposed algorithm currently is restricted to the iso-planar type of toolpath, it can be adapted to other types of milling. The physical cutting experiments performed by us have convincingly confirmed the advantage of the proposed machining strategy as compared to the conventional constant lead angle and constant cutting depth strategy—in our tests the maximum wear on the cutting edge is reduced by as much as 39%.

A hybrid modeling approach based on computational fluid dynamics (CFD) and finite element method (FEM) is presented to simulate and study cryogenic machining (CM) of Ti–6Al–4V alloy. CFD analysis was carried out to study the characteristics of the fluid flow and heat transfer process of liquid nitrogen (LN2) jet used as a coolant in turning operation. The velocity, turbulence, gas volume fraction, and temperature of the impingement jet were investigated. Based on the analysis results, the coefficient of heat transfer (CHT) between the LN2 and cutting tool/insert was obtained and used in the FEM analysis to model the heat transfer process between the LN2 and the tool/chip/workpiece. A three-dimensional (3D) finite element (FE) model was developed to simulate a real CM operation. CM tests were carried out to validate the 3D FE model by comparing cutting forces and chip temperature. To evaluate LN2 cooling effect on tool temperature and tool wear, a two-dimensional (2D) FE model was developed for steady-state thermal analysis of cryogenic and dry machining. Based on the predicted temperatures, the tool wear was estimated, showing that LN2 cooling can significantly improve tool life.

It is environmentally friendly to use water-based emulsion instead of the oil when cold drawing Al alloy production. In this research, adopting specially prepared water-based emulsion, contact angles and tribological properties of as-selected multilayer diamond films are clarified. The contact angle on diamond film is much smaller than that on WC-Co with the same surface roughness, and tribological behaviors of the diamond film are much better. The effects of surface roughness R a of the film, lubrication, and water content in the emulsion W are studied, indicating that the contact angle increases with W or R a . For the diamond film, lower R a is beneficial for reducing the coefficient of friction (COF), Al alloy ball wear and oxidation, while lower W contributes to the reduction of the COF, ball oxidation, and coated disk wear. Finally, high-speed drawing of high-quality 6021 Al alloy wires (AWs) is accomplished, proving that using coated drawing dies, the water-based emulsion (W < 80 vol %) can totally replace oil, and coated dies under the water-based emulsion lubrication present much elongated lifetime and can guarantee the production quality, compared to uncoated ones under the oil lubrication, in spite of slightly severer wire oxidation.

Owing to its outstanding physical and mechanical properties, polycrystalline diamond (PCD) is ideal for cutting titanium alloys. However, the high temperature and stress caused by the interaction of tool surface and chip flow lead to different types of wear. This paper investigates the wear mechanisms of PCD tools in three different tribological regions: sticking zone, transition zone, and sliding zone, when machining titanium alloy Ti6Al4V. The tribological behavior of PCD tools in the wear processes were analyzed through both experiments and theoretical calculations. Analytical models of stresses and temperature distribution were developed and validated by turning experiments. PCD tools, consisting of diamond grains of different sizes: CTB002 (2 μ m), CTB010 (10 μ m), and CTM302 (2–30 μ m), were used to cut Ti6Al4V at the normal cutting speed of 160 m/min and high cutting speed 240 m/min. It was found that adhesion, abrasion and diffusion dominated the wear process of PCD tools in different worn regions. Microscopic characters showed that the wear mechanisms were different in the three tribological regions, which was affected by the distribution of stresses and temperature. “Sticking” of workpiece material was obvious on the cutting edge, abrasion was severe in the transition zone, and adhesion was significant in the sliding zone. The shapes and morphological characters in different worn regions were affected by the stresses distribution and the types of PCD materials.

In this paper, both traditional Inconel 718 parts and WC/Inconel 718 composites were fabricated by selective laser melting (SLM). The size of WC particles was observed to play a crucial role in determining the microstructural evolution, distortion, and microcracks around the WC particles, which inturn also affected the effective mechanical properties of WC/Inconel 718 composites. The use of the 5.25 μ m diameter WC particles resulted in fine dendrites at the interface between the WC particle and the Inconel 718 matrix. This was attributed to the formation of an annular heat flow and radially arranged temperature gradient directions around the WC particle that increased the contact area between the matrix and the particle, thereby also improving the interfacial bonding. A sound metallurgical bonding at the interface was achieved with negligible distortion and microcracks due to a relatively uniform temperature distribution and temperature gradient (4.7 × 10 3 °C/mm) at the interface. This also explains the generation of dense and smooth interfacial bonding, which yielded a low average friction coefficient of 0.21. The wear properties were improved since grooves and spallation were reduced with the decrease of the WC size.

This paper aims to investigate the effectiveness of super-hard ceramic coatings by evaluating tool wear when drilling carbon fiber-reinforced plastics (CFRP) composite. The drilling experiments of CFRP are conducted with diamond-like carbon (DLC) coated, AlMgB 14 (BAM) coated, AlCrN and Si 3 N 4 and TiN (simply denoted as (AlCrSi/Ti)N) coated, and uncoated tungsten carbide drills. Each coating, dictated by its unique processing technique, provides unique thickness and morphology, and its physical properties, which makes the comparison among the coatings much difficult but enables to deduce the desirable attributes in the prospective coating ideally suited in drilling CFRP. To do so, after the drilling experiments, the tool wear was captured using the scanning electron and confocal laser scanning microscopes to construct the wear evolution that enables us to evaluate each coating qualitatively as well as quantitatively. Among the drills tested, the (AlCrSi/Ti)N-coated drills provided the best performance despite of the fact that (AlCrSi/Ti)N coating particularly are not harder than any other coating. The superior performance of the (AlCrSi/Ti)N coating can be explained by the comparable stiffness to the carbide substrate, 7.3 μ m-thick coating consisting of the numerous nanoscale alternating layers between nanocomposite of AlCrN and Si 3 N 4 and TiN coatings and the enhanced adhesion, which provide the effective cutting of carbon fibers. However, the thin DLC coating despite of its superior hardness and the BAM coating despite of its low friction did not perform at the level that the (AlCrSi/Ti)N coating was able to achieve.

The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. Surface integrity is also tightly linked with tool wear. Tool wear is one of the most significant and necessary parameters to be considered for machining sustainability. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, data-dependent systems (DDS), a stochastic modeling and analysis technique, was applied to study the power of spindle motor during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored, and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of variance (ANOVA) was also used to determine factors significant to the power by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. The results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies highlighted by DDS methodology.

Machining industry has been evolving toward implementation of automation into the processes for higher productivity and efficiency. Although many studies have been conducted in the past to develop intelligent monitoring systems in various application scenarios of machining processes, most of them just focused on cutting tools without considering the influence of the nonuniform hardness of workpiece material. This study develops a compact, reliable, and cost-effective tool condition monitoring (TCM) system to detect the cutting tool wear in machining of the workpiece material with hardness variation. The generated audible sound signals during the machining process are analyzed by state-of-the-art artificial intelligent techniques, support vector machine (SVM) and convolutional neural network (CNN), to predict the tool wear and the hardness variation of the workpiece. A four-level classification model is developed for the system to detect the tool wear condition based on the width of the flank wear land and the hardness variation of the workpiece. This study also involves the comparative analysis between two employed artificial intelligent techniques to evaluate the performance of the model in prediction. The proposed TCM system has shown a high prediction accuracy in detecting the tool wear from the audible sound into the proposed multiclassification wear level in end milling of the nonuniform hardened workpiece.

This paper describes a robust tool wear monitoring scheme for turning processes using low-cost sensors. A feature normalization scheme is proposed to eliminate the dependence of signal features on cutting conditions, cutting tools, and workpiece materials. In addition, a systematic feature selection procedure in conjunction with automated signal preprocessing parameter selection is presented to select the feature set that maximizes the performance of the predictive tool wear model. The tool wear model is built using a type-2 fuzzy basis function network (FBFN), which is capable of estimating the uncertainty bounds associated with tool wear measurement. Experimental results show that the tool wear model built with the selected features exhibits high accuracy, generalized applicability, and exemplary robustness: The model trained using 4140 steel turning test data could predict the tool wear for Inconel 718 turning with a root-mean-square error (RMSE) of 7.80 μ m and requests tool changes with a 6% margin on average. Furthermore, the developed method was successfully applied to tool wear monitoring of Ti–6Al–4V alloy despite different mechanisms of tool wear, i.e., crater wear instead of flank wear.

This paper presents investigation findings on additive manufacturing (AM) aspects of Ti6Al4V by microplasma transferred arc powder deposition ( μ -PTAPD) process in continuous and dwell-time mode. Pilot experiments were conducted to identify feasible values of six important parameters of μ -PTAPD process for single-layer deposition followed by 27 main experiments varying three parameters. Energy consumption aspects were used to identify optimum values of parameters varied during main experiments for multilayer deposition. It revealed that higher values of flow rate of powder and travel speed of deposition head result in smaller values of power consumption per unit flow rate of powder and energy consumption per unit traverse length. Continuous and dwell-time modes were used to study deposition characteristics, microstructure, lamellae widths, wear characteristics, tensile properties, fractography of tensile specimen, wear mechanism, and microhardness of multilayer depositions. Dwell-time deposition yielded higher effective wall width (EWW), deposition efficiency (DE), yield strength, ultimate strength, microhardness, surface straightness, lower strain, wear volume and friction coefficient, and smaller lamellar width. It had good deposition quality with fine partial martensite and basket-weave microstructure. Fractography analysis exhibited fine dimple rupture for dwell-time multilayer deposition and occurrence of elongated regions for continuous multilayer deposition. Wear of dwell-time multilayer deposition occurred by microploughing and microcutting resulting in smaller wear debris. Comparison of Ti6Al4V depositions by different processes revealed that dwell-time μ -PTAPD process is cost-effective than laser-based processes and energy efficient than pulsed plasma arc process.

The analysis of various methods of machining of rope internal thread ISO 10208, DIN 20317 has been carried out and the criteria of high-efficiency machining have been formulated. The concept of the method has been developed, which supposes the designing of the construction of noncore tool and the calculation of the parameters of mechanical trajectory with the purpose of ensuring the machining per one pass on the computer numerical control (CNC) milling machine. The compensation procedure of dimensional wear of insert has been developed. While machining the production batch of the parts in an experimental way, the optimum cutting conditions have been determined which allow ensuring the maximum efficiency on reaching the required roughness and the dimensional accuracy of the profile of rope thread. The performed statistical analysis of the machined parts allowed to establish that dispersions of the actual values of profiles' roughness follow Gauss' law. In an experimental way, it has been proved that the application of the proposed method increased the efficiency of machining of the internal rope thread by 2.5 times. On the basis of comparison of engineering-and-economical performance, the efficient fields of application of high-efficient method of machining of the rope threads have been determined.

The Ti–6Al–4V is a widely used alloy in the aerospace industry. In order to improve the grindability of Ti–6Al–4V, a hybrid material removal process is proposed in this study. This process is a combination of ultrasonic assisted grinding (UAG) and electrochemical grinding (ECG), hereafter called ultrasonic assisted electrochemical grinding (UAECG). For confirming the feasibility of the proposed technique, an experimental setup was constructed and the fundamental machining characteristics of UAECG in the grinding of Ti–6Al–4V were experimentally investigated. The results obtained from the investigation can be summarized as follows: (1) the normal and tangential forces in UAECG were decreased approximately 57% and 56%, respectively, comparing with conventional grinding (CG). (2) The work-surface roughness Ra both in ECG and UAECG was negative correlation to the electrolytic voltage, U I , and the surface damage; (3) the wheel radius wear in UAECG was considerably smaller than that in ECG when U I < 10 V. The chip adhesion and the grain fracture mainly affected the working lives of the wheels in ECG and UAECG, whereas the wheel wear in CG was predominantly attributed to the grain drop out; (4) a titanium dioxide (TiO 2 ) layer, which had a 78 nm thickness was achieved on the work surface in the condition of U I = 20 V, leading that the Vickers microhardness of work surface in ultrasonic assisted electrochemical was lower than that in CG by 15%.

The purpose of this paper is to study the effect of cutting tool surface geometry and the atomization-based cutting fluid (ACF) spray parameters on the characteristics of the thin film formed in an ACF delivery system. A computational model is developed using three submodels that are used to predict the carrier gas flow, droplet trajectories and the film formation, respectively. The model is validated through film thickness measurements using a laser displacement sensor. Turning inserts with chip-breaking grooves along with a conventional flat insert are used to study the effect of cutting tool surface geometry on the model-predicted film characteristics, including film thickness and velocity. Machining experiments are also conducted to investigate the effect of film characteristics on the machining performance in terms of tool wear, which show that the tool wear is minimum at a certain desired film thickness value and large film velocity value. Carrier gas pressure and cutting fluid flow rate are also varied to study the effect of ACF spray parameters on the film characteristics. Increase in the fluid flow results in increase in both film thickness and velocity, while an increase in the gas pressure results in the reduction of the film thickness but an increase in the film velocity.

The emergence of cloud computing, industrial internet of things (IIoT), and new machine learning techniques have shown the potential to advance prognostics and health management (PHM) in smart manufacturing. While model-based PHM techniques provide insight into the progression of faults in mechanical components, certain assumptions on the underlying physical mechanisms for fault development are required to develop predictive models. In situations where there is a lack of adequate prior knowledge of the underlying physics, data-driven PHM techniques have been increasingly applied in the field of smart manufacturing. One of the limitations of current data-driven methods is that large volumes of training data are required to make accurate predictions. Consequently, computational efficiency remains a primary challenge, especially when large volumes of sensor-generated data need to be processed in real-time applications. The objective of this research is to introduce a cloud-based parallel machine learning algorithm that is capable of training large-scale predictive models more efficiently. The random forests (RFs) algorithm is parallelized using the MapReduce data processing scheme. The MapReduce-based parallel random forests (PRFs) algorithm is implemented on a scalable cloud computing system with varying combinations of processors and memories. The effectiveness of this new method is demonstrated using condition monitoring data collected from milling experiments. By implementing RFs in parallel on the cloud, a significant increase in the processing speed (14.7 times in terms of increase in training time) has been achieved, with a high prediction accuracy of tool wear (eight times in terms of reduction in mean squared error (MSE)).

A wear characterization study was performed to determine the useful lifetime of polycrystalline cubic boron nitride (PCBN) tooling for the friction stir welding (FSW) of stainless steel samples in support of a nuclear repair welding research and development program. In situ and ex situ laser profilometry were utilized as primary methods of monitoring tool geometry degradation, and volumetric defects were detected through both nondestructive and destructive techniques, as repeated welds of a standard sample configuration were produced. These combined methods of characterization allowed for the successful correlation of defect formation with tool condition. Additionally, the spectral content of weld forces was examined to search for indications of evolving material flow conditions, caused by significant tool wear, that would result in the formation of defects; this analysis established the basis for a system that would automatically detect these conditions. To demonstrate this type of system, an artificial neural network was trained and evaluated, and a 95.2% classification rate of defined defect states in validation was achieved. This performance constituted a successful demonstration of in-process monitoring of tool wear and weld quality in FSW of a high melting temperature, high hardness material, with implications for remote monitoring capabilities in the specific application of nuclear repair welding.