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The design of machine parts of different sizes and shapes has become relevant in the manufacturing industry which requires five-axis machine tools of high dynamic performance; different machining test pieces have been used to test and reflect the machine tool's performance. The S-shaped is still under development and consideration of which a new test piece better than the S-shaped part has been recommended to be put forward making the NAS979 the only standardized test piece; however, it has some limitations. Hence, this study proposes a new test piece to objectively satisfy the demand for machine tools with higher dynamic performance, which shows much improvement over the standard NAS979 and is the best alternative to the S-shaped test piece, and it combines the geometric and kinematic features of both test pieces. Geometrically, it has non-uniform surface continuity, variable twist angle, and variable curvature; and the cutting tool moves in close and opened angles along the tool path; there is sudden rise and fall of axes' velocity, acceleration, and jerk with much impact during machining which makes the S-cone test piece be machined by only five-axis machine tools with high dynamic performance, and has a better dynamic performance identification effect than the S-shaped test piece based on the trajectory test. Detailed work on the validation of the machine tool's dynamic performance using the S-cone part will be captured next part of this study. [ABSTRACT FROM AUTHOR]

Tungsten alloy has excellent performance and has been widely used in military, aerospace and nuclear energy, and other cutting-edge industries. However, tungsten alloy has large hardness, high strength, and poor plastic deformation ability, which resulted in high cutting force and serious tool wear during the cutting process, leading to low surface quality of the workpiece after molding. Therefore, it is of great significance to strengthen the research on tungsten alloy cutting technology to promote the development of tungsten alloy application. Firstly, the research progress of tungsten alloy cutting process technology has been systematically reviewed, and the current status of cutting parameters optimization, new cutting methods and devices, and cutting fluid technology have been emphatically reviewed. Secondly, the types of tungsten alloy cutting tools, the relevant tool micro-texture, and tool coatings technology have been briefly described, and the composite cutting technology such as cryogenic cutting, electroplastically assisted cutting, and ultrasonic vibration assisted cutting and the effect of cutting performance prediction technology on tungsten alloy machining performance have been summarized. Finally, the development prospect of tungsten alloy cutting technology has been prospected. [ABSTRACT FROM AUTHOR]

Nowadays, new challenges around increasing production quality and productivity, and decreasing energy consumption, are growing in the manufacturing industry. In order to tackle these challenges, it is of vital importance to monitor the health of critical components. In the machine tool sector, one of the main aspects is to monitor the wear of the cutting tools, as it affects directly to the fulfillment of tolerances, production of scrap, energy consumption, etc. Besides, the prediction of the remaining useful life (RUL) of the cutting tools, which is related to their wear level, is gaining more importance in the field of predictive maintenance, being that prediction is a crucial point for an improvement of the quality of the cutting process. Unlike monitoring the current health of the cutting tools in real time, as tool wear diagnosis does, RUL prediction allows to know when the tool will end its useful life. This is a key factor since it allows optimizing the planning of maintenance strategies. Moreover, a substantial number of signals can be captured from machine tools, but not all of them perform as optimum predictors for tool RUL. Thus, this paper focuses on RUL and has two main objectives. First, to evaluate the optimum signals for RUL prediction, a substantial number of them were captured in a turning process and investigated by using recursive feature elimination (RFE). Second, the use of bidirectional recurrent neural networks (BRNN) as regressive models to predict the RUL of cutting tools in machining operations using the investigated optimum signals is investigated. The results are compared to traditional machine learning (ML) models and convolutional neural networks (CNN). The results show that among all the signals captured, the root mean squared (RMS) parameter of the forward force ( F y ) is the optimum for RUL prediction. As well, the bidirectional long-short term memory (BiLSTM) and bidirectional gated recurrent units (BiGRU), which are two types of BRNN, along with the RMS of F y signal, achieved the lowest root mean squared error (RMSE) for tool RUL, being also computationally the most demanding ones. [ABSTRACT FROM AUTHOR]

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]

Machine learning has been widely used in diagnosing system faults as an integration part in modern intelligent manufacturing. However, sensor selection and index for machine tools are versatile and not standardized at this moment for manufacturing equipment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data-driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. As a result, a thoughtful flow for identifying key sensor index and to evaluate its impact on the performance of subsequent machine learning scheme would be essential. In this work, the status monitoring and prediction of a milling cutter wear problem is investigated as an example to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. In parallel, the well-used multilayer perception (MLP) artificial neural network (ANN) models are also used to elucidate the importance of the problem. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures. A procedure is developed to evaluate the sensitivity of each index and the key dominated indexes are eventually identified. To elucidate the importance of proper sensor index selection, three MLP ANN models are established based on different sensor features to examine the influence of selected sensors indexes on the prediction accuracy. The results show that with appropriate sensors signatures, even with less amount of experimental data, the model can indeed achieve a better prediction (98.7%) in comparison with that without proper sensor index selection (90.3). This implies that a rigorous process for identifying key feature is essential for system diagnosis in future intelligent manufacturing. [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 low-frequency vibration-assisted drilling (LFVAD) of CFRP/Ti6Al4V stacks, owing to the different mechanical and thermal performances of the involved carbon fiber–reinforced plastic (CFRP) composite and Ti6Al4V alloy, the short tool life of commonly used carbide (WC) tools and rapidly deteriorated machining quality are the major problems. This paper aims to explore whether the polycrystalline diamond (PCD) tool can achieve a competitive effect on drilling CFRP/Ti6Al4V stacks compared with previous CFRP drilling studies. To avoid the frequent occurrence of chisel edge fracture in LFVAD of Ti6Al4V alloy, a model of plowing behavior is established and parametric experiments based on it are performed with both PCD and WC drills. It is found that the periodically dynamic force is the major factor causing the chisel edge fracture of the PCD drill, which can be alleviated by reducing the feed rate. Besides, by using PCD drills instead of WC drills in Ti6Al4V drilling, the exit burr height, drilling diameter deviation, and surface roughness value are reduced under each given parameter. Finally, PCD and WC drill bits are used to perform a wear test on the CFRP/Ti6Al4Vstacks under optimized parameters. The PCD drill achieves superior performance, including lower wear rate, better hole quality, and higher consistency. The experiments discussed in this paper provide some suggestions for selecting tools and cutting parameters when drilling CFRP/Ti6Al4V stacks. [ABSTRACT FROM AUTHOR]

Machining of CFRP composites is usually described as a challenging process from tool life and surface quality perspectives. To achieve a flawless machined surface at reasonable cutting tool life/cost, an optimum combination of cutting tool material and geometry is the key. The cutting tool angles are of importance as these dictate the cutting mechanism as well as the cutting edge robustness and the introduction of vibration to the machining process opens up new horizons of improvement and stirs further research questions. This article investigates the effect of tools with different geometry possessing different rake angles when used in ultrasonic-assisted edge trimming operation dealing with multidirectional CFRP laminates. A full-factorial experimental design was adopted to analyze the effect of process parameters typically cutting speed, feed rate, rake angle, amplitude, and their interactions. Machining performance indicators were captured which were the cutting forces, tool wear, chip temperature, and surface roughness. The results showed that UAM mode contributed to an increase in the cutting forces, tool wear, and chip temperature compared to the conventional mode. On the other hand, UAM mode improved the quality of the machined surface. Additionally, the ultrasonic mode enhanced the material removal mechanism using a tool with a negative rake angle. [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]

Many techniques have been developed to improve the machinability of aeronautical materials titanium and nickel-based alloys such as ultrasonic-assisted turning, laser-assisted turning, and cryogenic-assisted turning. This collaborative scientific investigation presents the steps taken to gain insight into the phenomena of machining Nimonic 90 (a nickel-based alloy) alloy using ultrasonically assisted turning. The cutting speed, feed rate, depth of cut, and frequency are taken as input parameters and average surface roughness (Ra), power consumption (P), and chip formation are considered as output parameters. The experiments are carried out with the full factorial design. The UAT (ultrasonically assisted turning) process gives a significant improvement in average surface roughness and power consumption because of the intermittent cutting action of the cutting tool. UAT process shows a 70–80% reduction in average surface roughness (Ra) and a 6–15% reduction in power consumption as compared with CT (conventional turning) process. Ultrasonically assisted turning also resulted in the thin and smoother chips as compared with CT process which helps to achieve a more superior machining effect. Finite element modeling shows that the quasi-static nature of the stress induced in the UAT process leads to lower force and ultimately lower power generation. Moreover, a sustainability assessment model is implemented to investigate the effect of UAT in terms of machining performance as well as sustainability effectiveness in a single integrated approach. The novelty of this work lies in providing an integrated concept that combines experimental analysis and sustainability assessment when using ultrasonic vibrational energy during turning of Nimonic 90. [ABSTRACT FROM AUTHOR]

Aluminum alloys 6061-T6 are widely utilized in the automotive, aerospace, and marine industries due to high corrosion resistance, high strength, and good workability and machinability. The machining performance of these alloys depends on several factors including tool's material, coating, and geometry. Cutting tool edge radius is one of the most effective factors in cutting forces, energy requirement, and chip formation during metal cutting. The present article aims to study the interactions between the cutting edge radius and cutting speed, feed rate, and rake angle and examine the impacts of the aforementioned tool geometry and cutting conditions on machining forces, cutting temperature, and chip thickness in cutting an aluminum alloy 6061-T6. Special attention is devoted to examining the influence of the cutting edge radius on machining variables and comparing the results of conventional machining (CM) and high speed machining (HSM). A finite element model was developed to simulate the above interactions and was experimentally validated for different machining parameters. The results demonstrate that although increasing the cutting edge radius clearly raises the machining forces, it has a slight influence on the chip thickness. It is also found that the maximum cutting temperatures remain nearly constant with changes in the tool edge radius, while the average temperatures of the tool tip increase especially in HSM. Furthermore, it was found that the location at which the maximum cutting temperature occurs depends more on cutting conditions and tool geometry than workpiece and tool materials. [ABSTRACT FROM AUTHOR]

Machining performance of AISI 316 stainless steel turning under dry and spray impingement cooling environments was investigated. The input parameters such as depth of cut, feed rate, cutting speed, water pressure and air pressure were considered for the spray assisted turning with uncoated carbide inserts. All the experiments were carried out using Taguchi based L16 orthogonal array of the design of experiments. Cutting tool temperature, chip temperature, surface roughness and tool flank wear were the measured machining performance responses. The machining performance responses were simultaneously optimized using Taguchi based Weighted Principal Component Analysis approach. The optimal combination of the machining parameters under spray impingement cooling was found to be 0.2 mm depth of cut, 0.04 mm/rev of feed rate, 66 m/min of cutting speed, 3.5 bar of water pressure and 1.5 bar of air pressure. The optimized result was verified through confirmatory experiments and an improvement in the SN ratio of 18.1540 dB for Combined Quality Loss (CQL) was obtained. Machining under spray impingement cooling environment was most effective in turning AISI 316 steel in comparison with dry machining since reduction in chip temperatures were found out to be greater than 300%. [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]

The high-pressure coolant is considered as a very important point to improve the machining performance. Most of the papers concern the effect of high-pressure coolant on titanium where its efficiency is very important. This paper investigates the application of a high-pressure lubricant during the turning of stainless steel using coated carbide tools to control the chip size through an extensive experimental work. The effects of the cutting conditions, the cutting tool geometries, and the lubricant pressure were studied. A model was developed to clearly understand the mechanical effects of a lubricant on the chip fragmentation. This model is partially based on a Maln experimental analysis in which a high-speed camera was used to define the jet geometry. Finally, the relationship between the cutting conditions and the pressure is meticulously described. [ABSTRACT FROM AUTHOR]

The hard turning process is generally performed by PCBN or mixed ceramics tools, which have the mechanical properties that withstand the tribological conditions of the process imposed by the hardened machined material. Coated cemented carbides are also an option for hard machining processes, although relying on the coating deterioration. This paper aims to evaluate the tool wear rate in function of the cutting speed in the hard turning of the steel AISI 52100 with a hardness of 50 HRC for three different types of cutting tool materials: coated cemented carbide, mixed ceramic, and PCBN. The methodology applied to assess the tool wear is based on three-dimensional parameters (volumetric) obtained from a focus variation microscope (FVM). The tool wear rate (WRRM) is calculated based on ordinary least squares (OLS) adopting five values of WRM in five machining time intervals. The face turning experiments were performed at four cutting speeds: vc = 120, 150, 187.5, and 234 m/min. At vc = 120 m/min, the PCBN presented the lowest tool wear rate (182 μm3/s); at vc = 150 m/min, the coated cemented carbide (tool wear rate on the coating) had the best performance (417 μm3/s). The mixed ceramic tool presented a better performance at the higher cutting speeds of vc = 187.5 and 234 m/min, 1206 and 1878 μm3/s, respectively. The methodology applied was reliable to understand and discuss the performance of the machining process through the tool wear rate (WRRM), which is based on the volume of removed material from the tool (WRM). The three-dimensional tool wear parameters can also be applied to machining process optimization, cutting tool wear model creation, benchmarking, and development of new cutting tool materials and grades. Furthermore, the methodology can be considered more agile and precise when compared to the current industrial methodology of tool performance evaluation. Thus, this innovative methodology promotes important information for cutting tool manufacturers and for its customers such as automotive and aeronautic industry. [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]

Compared with conventional hole-making process, rotary ultrasonic machining (RUM) is commonly believed to improve the cutting performance, such as low cutting force, good hole quality, long tool life, and high material removal rate. However, this work proposes an interesting observation on hole-making process using longitude-torsional ultrasonic assisted helical milling (LT UAHM). Due to the special kinematic motion in LT UAHM, the trajectory of the cutting tool has complex characteristics which may generate a negative effect on machining performance. In this study, a comparative experiment between LT UAHM and conventional helical milling (HM) was carried out. The results showed that the axial cutting force is not always reduced, the machined hole quality is also not improved consistently in LT UAHM. To comprehend the cutting mechanism of LT UAHM incisively, a kinematic trajectory model was proposed. By the theoretical kinematic trajectory model, LT UAHM can generate the ultrasonic elliptical cutting in the process, and the phase shift is a critical factor that can affect the elliptical trajectory shape even the vibration cutting direction of the tool's cutting edge. The equivalent cutting angle and direction of friction between the rake face and chip in processing change accordingly. As a result, the magnitude of cutting force and machined hole quality in terms of burr height in this process will be significantly affected. [ABSTRACT FROM AUTHOR]

Titanium alloys are typical difficult-to-machine materials. The experiment is conducted by turning Ti-6Al-0.6Cr-0.4Fe-0.4Si-0.01B (TC7) with PCD and PCBN tools under dry condition. The paper focuses on studying the performance of PCD tools in dry turning TC7 and investigating the machinability of TC7. Wear mechanisms of two cutting tools are analyzed and compared by scanning electronic microscopic (SEM) images of worn inserts. The effect of cutting parameters (cutting speed, feed rate and depth of cut) on tool life is studied by measuring the average flank wear with 3D super-depth-of-field instrument. The influence of cutting parameters on cutting temperature, surface roughness, and surface microhardness is also investigated. The results show that although non-uniform wear band forms on both tools of PCD and PCBN, the wear of PCBN is serious than that of PCD. The adhesion-dissolution-diffusion mechanism takes effect on two tools during machining. For PCD tool, obvious adhesion and chipping occur. For PCBN tool, adhesion, notching, and crater occur. The main conclusion is that PCD tool has better performance than PCBN tool. PCD tool is suitable for machining TC7. The machinability of TC7 is poorer than that of TC4. The hardening of workpiece surface layer occurs. [ABSTRACT FROM AUTHOR]