High-speed Milling Experiments Research Articles

SIMULATION AND EXPERIMENTAL RESEARCH STUDIES ON TEMPERATURE FIELD FOR THE PRODUCTION OF DIAMOND-COATED MILLING TOOLS BY HFCVD DEVICE WITH SEPARATED WORKTABLES

Diamond-coated tools have extremely extraordinary physical and chemical properties and have become ideal tools for machining the emerging materials that are hard to machine. In mass production of diamond-coated milling tools, the homogeneity of diamond coatings has always been sought for and is highly influenced by the uniformity of temperature field near the tool substrate. In order to further improve the uniformity of the temperature field and deposit diamond coatings with uniform thickness, a novel hot filament chemical vapor deposition (HFCVD) device with separated worktables and plates for mass production of diamond-coated milling tools is proposed. First, the simulation models of previous device with integrated worktable versus new device with separated worktables are established using finite volume method. The simulation results shows that the temperature distribution is more uniform in the device with separated worktables. Second, temperature measurement experiments are conducted, which prove the correctness of simulation model. Subsequently, experiments involving the deposition of diamond coatings on milling tools are conducted using two kinds of worktables, utilizing the same parameters as in the simulation. Finally, high-speed milling experiments are conducted to test the diamond-coated milling tools. According to the results of SEM, Raman and high-speed milling experiments, it can be concluded that the homogeneity of coating thickness and cutting performance of the milling tools produced by HFCVD device with separated worktables and plates are better.

Physics-informed Gaussian process for tool wear prediction

The tool wear monitoring (TWM) system plays an increasingly important role to ensure high quality finishing and system safety in advanced CNC machining process. The pure data-based TWM approaches generally needs to develop complex machine learning models and require massive sensory data to learn the models to reach high monitoring accuracy, while the physics-based tool wear models are simple but hard to adapt to varied working conditions. In order to incorporate the benefits of both methods, a novel physics-informed Gaussian process model is developed to predict the tool wear. Different from the traditional approaches, three tool wear physical models are introduced to develop the physics-informed Gaussian process regression (PB-GPR) model. The wear model is applied to constrain the mean function of the Gaussian process, so that the PB-GPR is more in line with the actual tool wear. At the same time, the model can initiate small data training to meet limited tool wear labels in practice, and then update the model with new measurements. Multi-sensor signals are collected and multi-domain features are extracted for the model learning. The proposed approach is validated from high speed milling experiments. The results show a significant performance improvement including tool wear prediction accuracy and robustness in extrapolation compared to the conventional machine learning methods.

Cutting performance of binderless nano-polycrystalline cBN and PcBN milling tools for high-speed milling of hardened steel

Since difficult-to-machine materials are widely used in high-end equipment such as aerospace and marine components as well as precision molds, the development of high-performance cubic boron nitride (cBN) tools has been an important area of research. In this paper, we evaluate a new type of finer-grained, binderless end mill (binderless nano-polycrystalline cBN, BNNC) by comparing it to polycrystalline cBN (PcBN) end mills for high-speed machining of hardened steel. First, the process of tool preparation based on the above novel cBN is described and high-speed milling experiments were designed. Then, the cutting performance of the BNNC and PcBN tools during high-speed milling is analyzed and compared in four aspects: the cutting force coefficients, the friction coefficient of the rake face, the milling temperature and the tool wear. In addition, the wear mechanism of the two types of end mills was analyzed by energy dispersive X-ray (EDX). Afterwards, the trend of cutting temperature was further investigated by 2D finite element simulation. Finally, grey correlation analysis was applied to describe the correlation between the above factors and tool wear. This study shows that compared with PcBN end mills, BNNC end mills have longer tool life under the same cutting condition and are more reliable at high temperatures, which exhibit great application potential.

Robust [formula omitted] parameterization with low tuning complexity of cascaded control for feed drives

This paper presents a robust parameterization method for the well-established cascaded control structure of feed drives and applies it to both proportional (P) and sliding mode (SMC) position controllers. The controller parameterization is based on the structured μ-synthesis technique, which allows direct robust tuning of fixed control structures with respect to the conflicting goals of tracking and disturbance rejection over different frequency ranges. The proposed strategy explicitly takes into account both matched and unmatched uncertainties and considers the dynamics of the Kalman filter and feedforward control, ensuring robust stability and performance based on a simple low-order plant model with only two parameters. To ensure industrial applicability, the parameterization problem is formulated in a signal-based manner and the tuning complexity is limited to the selection of only two hyperparameters by directly extracting the information from measurements. Extensive experimental results are performed on an industrial feed drive to validate the performance and robustness of the proposed approach. For example, compared to P-PI cascade control with feedforward and nominally tuned SMC position control, the robustly parameterized SMC position controller reduces the root-mean-square tracking error by 79% and 61% during high-speed milling experiment. Even under variation of model parameters by 20%, the robustly parameterized SMC still cuts the tracking error by more than 44% in the comparison.

The effect of chip formation on the cutting force and tool wear in high-speed milling Inconel 718

Coated carbide tools are widely used in the processing of nickel-based superalloys due to their excellent wear resistance, high strength, and good hardness at high temperatures. In this paper, the high-speed milling experiments and finite element simulation of Inconel 718 are carried out by using PVD TiAlN-coated carbide tools. Simulations of tool temperature, cutting force, and chip morphology were performed to analyze the effect of cutting speed on the degree of sawtooth chip formation and the effect of sawtooth chip formation on the cutting force and tool wear. The results show that the cutting temperature mainly focuses on the rake face, and with the cutting speed increasing from 60 to 120 m/min, the maximum temperature of the rake face increases from 580 to 660 ℃. The maximum temperature region (MTR) on the rake face gradually approaches the tool nose with a decrease in the cutting speed. The generation of sawtooth chips leads to fluctuations in the cutting force component. As the cutting speed increases, the degree of chip sawing increases. The effect of the sawtooth chip on the fluctuation of the cutting force will also increase, thus increasing the degree of tool wear.

Prediction Model of Sound Signal in High-Speed Milling of Wood-Plastic Composites.

The accuracy of the acoustic signal prediction model for wood–plastic composites milling has an important influence on the condition monitoring of the cutting process and the improvement of the machining environment. To establish a high-precision prediction model of sound signal in the high-speed milling of wood–plastic composites, high-speed milling experiments on self-developed wood–plastic composites were carried out with cemented carbide tools. A mathematical model of the relationship of the four milling parameters, including axial cutting depth, radial cutting depth, feed rate and cutting speed, and the sound signal of wood–plastic composites milling, was established by using the full-factor test method. The experimental data obtained by the orthogonal test method were used as the test samples in the mathematical model. Test results show that the prediction accuracy of the mathematical model of the sound signal in the milling of wood–plastic composites exceeds 95.4%. To further improve the prediction accuracy of the sound signal in the milling of wood–plastic composites, a prediction model was established using back propagation (BP) neural network. Then, the particle swarm optimization (PSO) algorithm was used to optimize the BP neural network, obtaining the PSO–BP neural network prediction model. The test results show that the prediction accuracy of the PSO–BP prediction model for the sound signal in the high-speed milling of wood–plastic composites exceeds 97.5%. The PSO–BP model has a better global approximation ability and higher prediction accuracy than the BP model. The research results can provide a reference basis for sound signal prediction in the high-speed milling of wood–plastic composites.

Interpretable deep learning approach for tool wear monitoring in high-speed milling

Tool wear monitoring (TWM) is critical in modern high-speed milling, and an effective TWM system will improve machining precision, increase tool life and reduce production costs. As a novel data-driven approach with strong learning capability, deep learning has been introduced and studied for manufacturing process monitoring, but it is rarely applied as an independent method in practice for TWM due to the poor interpretability of the monitoring results. In this study, a multi-scale pyramid attention network (MPAN) is proposed. MPAN can not only accurately monitor tool wear based on sensory signals, but also introduce the interpretability from both the aspect of network structure design and feature extraction. With the prior knowledge of signal periodicity is introduced into the structure design, the extracted multi-scale features can cover almost all the characteristic periods. In addition, the periodicity of interest can be studied based on the attention distribution. The effectiveness and feasibility of this method are verified on high-speed milling experiments. This is the first attempt to interpret deep-learning based approach for TWM.

Tool Wear and Surface Roughness Characteristics in the High-Speed Milling of Pure Ti and Ti Alloy Using TiAlN Coated Carbide Radius End Mill

The workpiece materials used in the experiment were pure Ti and Ti alloy, which are commonly applied in the biomedical and aircraft industries. Although they are attracting increasing interest due to their superior mechanical properties, they are also known to be difficult-to-machine. This study investigated the cutting conditions for realizing minimum tool wear and optimum surface roughness using a TiAlN coated carbide end mill with a diameter of 6 mm, corner radius of 0.5 mm, and four blades in high-speed milling experiments based on the dry cutting process. In this case, peripheral speed was set as the main parameter. Cutting resistance and cutting temperature are also important parameters, but they are difficult to use as indicators for setting cutting conditions directly from the obtained data. Therefore, changing the rotational speed is a practical way to change the peripheral speed, while changing the machining conditions directly from the cutting resistance and temperature is difficult and impractical.

Analysis of High-Speed Milling Surface Topography and Prediction of Wear Resistance.

Surface topography parameters are an important factor affecting the wear resistance of parts, and topography parameters are affected by process parameters in order to explore the influence law of process parameters on surface topography parameters and to find the quantitative relationship between milling surface topography parameters and wear resistance. Firstly, this paper took the surface after high-speed milling as the research object, established the residual height model of the milled surface based on static machining parameters, and analyzed the relationship between the residual height of the surface and the machining parameters. Secondly, a high-speed milling experiment was designed to explore the influence law of processing parameters on surface topography and analyzed the influence law of processing parameters on specific topography parameters; Finally, a friction and wear experiment was designed. Based on the BP neural network, the wear resistance of the milled surface in terms of wear amount and friction coefficient was predicted. Through experimental verification, the maximum error of the prediction model was 16.39%, and the minimum was 6.18%.

A new study for prediction and optimisation of energy consumption during high-speed milling

ABSTRACT High-speed milling (HSM) aims to create high-quality surfaces and complex profiles and to increase the ability to eliminate excess material. This affects total energy consumption (EC), which in turn has an impact not only on the environment but also on production costs. Therefore, accurately evaluating how much energy is consumed by HSM is very important. EC is affected by stochastic tool wear, that is, when the latter increases, it leads to a corresponding increase in cutting force and vibration, thereby increasing EC. Therefore, the nonlinear processes caused by tool wear are determined through singularity vibration analysis combined with consideration of the geometry of the cutting edge with the aim of providing a predictive model for real-time prediction of the cutting force. Based on the cutting force components, an EC calculation model has been established. In particular, a new hybrid algorithm based on back-propagation neural network and multi-objective particle swarm optimisation is developed to determine the optimal cutting parameters, which can help minimise the total EC. HSM experiments were conducted to confirm the accuracy and availability of the proposed online monitoring and optimisation model. The proposed optimisation method based on the improvement model can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model is also verified through experimental processes.

Pyramid LSTM Network for Tool Condition Monitoring

Tool condition monitoring is important to guarantee the product quality and improve the productivity in high-performance computer numerical control (CNC) machining. Due to the harsh working conditions and non-stationary milling process, it is difficult to establish a monitoring model suitable for complex conditions. In this article, a pyramid long short-term memory (LSTM) auto-encoder is proposed to monitor tool wear. Unlike the classic stacked LSTM, the pyramid model is constructed based on the frequency spectrum of cutting signals. Each layer focuses on one periodic scale, and each unit focuses on one periodic fluctuation. The features are compressed layer by layer according to the frequency spectrum. The learned patterns are restricted by the spectrum-based structure, which simplifies the monitoring task and reduces the model complexity. The length of the cutting signal is also no longer limited by the memory capacity of LSTM. In the meantime, the efficiency of long-term signal processing is also greatly improved by reducing the number of units. In addition, the introduction of auto-encoder can further improve the accuracy of the model under complex working conditions through unsupervised learning. The good performance on high-speed milling experiments shows the accuracy of the model under unknown tools and milling parameters. Compared with the classic stacked LSTM, the pyramid LSTM auto-encoder has advantages in computational speed, stability, and prediction accuracy.

Multi-objective optimization of TC17 high-speed milling parameters using genetic algorithm

To optimize the high-speed milling parameters of titanium alloy TC17, the influence of cutting parameters on cutting force and surface roughness was analyzed according to TC17 high-speed milling experiments. Moreover, the prediction model of the cutting force and the surface roughness was established by the regression method, and the multi-objective optimization using genetic algorithm obtains the range of cutting parameters. Ultimately, the optimized parameters combination was gotten, and its reliability was verified. The result shows that the cutting depth plays a significant role in the cutting force and the surface roughness. The optimized parameters combination can effectively improve machining efficiency and quality.

Application of singularity vibration for minimum energy consumption in high-speed milling

To provide a model for real-time prediction of cutting forces, vibrations and optimal energy consumption (EC) model, a new hybrid algorithm based on Back-Propagation Neural Network and Multi-Objective Particle Swarm Optimization (BPNN-MOPSO) was developed to determine the optimal cutting parameters with minimal total energy consumption. High-speed milling experiments were conducted to confirm the proposed online monitoring and optimal model’s accuracy and availability. The proposed optimal method, based on the proposed improvement model, can reduce EC by 10.49% compared to the empirical option method. The feasibility and effectiveness of the proposed model have been verified through experimental processes.

Microstructure and Microhardness Alterations of Inconel 718 under Cryogenic Cutting

This paper presents the experimental investigations to identify the effect of different machining parameters on the microstructure and microhardness alterations beneath the machined surface of Inconel 718. The high-speed milling experiments were conducted using PVD multi-coated ball nose carbide inserts, under cryogenic CO2 condition at controlled cutting speed of 120-140 m/min, feed rate of 0.15-0.25 mm/tooth, depth of cut of 0.3–0.7 mm and a fixed width of cut at 0.4 mm. The experimental results discovered that the formation of plastic deformation could only be observed when cutting with the highest value of feed rate and depth of cut, deepened until 8.87 µm from the machined surface. At the same time, those factors also significantly increased machined surface hardness, made the workpiece harder than its bulk hardness. However, the hardness values were found reduced towards the sub-surface and back to its bulk hardness approximately 250 - 300 µm from the top surface. This study suggests that the finishing milling process of Inconel 718 under cryogenic CO2 conditions and at predetermined parameters can be performed at a depth of cut less than 0.3 mm as its machined sub-surface microhardness and microstructure alteration were observed to be inconsequential. Thus, a more economical machining process can be performed.

Research on tool wear factors for milling wood-plastic composites based on response surface methodology

A high-speed milling experiment on wood-plastic composites was performed using cemented carbide tools, and the resulting wear pattern was studied. The influence of the cutting parameters, the cutting speed, feed speed, and axial cutting depth on the tool wear was studied via response surface methodology, and the influence of the interaction of the cutting parameters on tool wear was analyzed. Three-dimensional surface graphs and contour plots of the tool wear results were established. According to the experimental results, a mathematical model of the tool wear based on the second-order response surface methodology was established, and the model was utilized to verify its feasibility. The results show that the nose width (NW) increases with the increase of the cutting speed and axial cutting depth and decreases with the increase of feed speed. Among the factors affecting tool wear, the cutting speed had the greatest influence, followed by the feed rate, with the axial cutting depth affecting tool wear the least. According to the results of the interaction between the tool wear and the cutting parameters, a low feed speed and small axial cutting depth can be selected to ensure long tool life; for low-speed cutting, a high feed speed and large axial cutting depth can be adopted to ensure tool life while improving machining efficiency.

A comprehensive method for selecting cutting tool materials

Although there are many types of cutting tool materials, no single cutting tool material meets all the needs of machining applications because of the complex and changeable cutting conditions. It is crucial to study the matching between tool materials and workpiece materials for improving productivity, obtaining good surface quality and reducing machining cost. In this paper, rule-based reasoning (RBR) method and gray complex proportional assessment (COPRAS-G) method are applied to solve the problem of cutting tool material selection. RBR is used for primary selection of tool materials to reduce the number of alternatives and the computational complexity in COPRAS-G method. Ten attributes of tool materials including physical, mechanical, chemical, and cost parameters are selected as criteria in the COPRAS-G method, and entropy method and analytic hierarchy process (AHP) are used to determine the objective weights and subjective weights of attributes, respectively. Finally, the ranking of several alternatives is obtained to select the most suitable cutting tool material. Taking the cutting tool material selection of machining CGI as an example to validate the method proposed in this paper. The research results revealed that the alumina ceramic has the best comprehensive performance, alumina ceramic cutting tools containing carbides to improve the flexural strength and fracture toughness, or cemented carbide cutting tools with alumina coating are preferred for machining CGI. High-speed milling experiments of CGI were carried out, and the results showed that the alumina-coated tungsten carbide tools had longer tool life than the silicon nitride ceramic tools, which confirmed the effectiveness of the proposed cutting tool material selection method. And this method can be extended to other engineering applications in material selection problems.

Modeling and experimental validation on temperature diffusion mechanism in high-speed bone milling

• High-speed bone milling heat is mainly caused by the friction effect between the bone chip and the cutter tooth. • Cutting edge temperature is determined by the equivalent static plane heat source. • The bone material temperature distribution is obtained according to the constant temperature moving heat source. • The thermal damage factors could be calculated based on the temperature diffusion mechanism model in bone milling. In orthopedic surgery, thermal damage of bone affects the accuracy of the prosthesis fixation and delays the postoperative recovery time. In order to avoid thermal damage in high-speed bone machining, it is necessary to establish an effective relationship among machining parameters, tool parameters, and temperature distribution of bone materials. Based on the characteristics of bone material and high-speed processing, this study proposes a novel temperature diffusion mechanism model (TDMM) in high-speed bone milling. According to the model, heat during high-speed bone milling is mainly generated by the friction effect between the bone chip and the cutter tooth. Then the cutting edge temperature of the milling cutter is determined by the equivalent static heat source, and cutting edge is regarded as a moving heat source with constant temperature. Under the action of the moving heat source, the bone material temperature distribution was determined. Moreover, the degree of thermal damage of bone materials under different machining and tool parameters was evaluated. Next, a high-speed bone milling experiment platform was established, in order to obtain accurate cutting edge temperature and bone material temperature. According to a large number of experimental measurements, the theoretical prediction temperature values of the cutting edge and bone materials were found to be within the reasonable error range of experimental measurements. The proposed model was proved to reasonably predict the temperature of the cutting edge and bone materials in high-speed bone milling. Experimental results indicate that the processing methods, tool diameter, cooling method, and cutting layer have a noteworthy impact on bone material distribution in bone milling. Spray cooling method can reduce the bone material temperature significantly. And the bone material temperature outside the cutting layer can be greatly lower than that in the cutting layer. This study also indicates that according to the TDMM, it is convenient for surgeons to select the appropriate machining parameters, and for engineers to optimize tool design.

An improved case based reasoning method and its application in estimation of surface quality toward intelligent machining

In the high speed milling process, the accurate predictions of surface roughness and residual stress can avoid the deterioration of machined surface quality. But it’s hard to estimate the surface roughness and residual stress under different tool wear status and cutting parameters. In this work, a novel intelligent reasoning method-improved case based reasoning (ICBR) was proposed to predict the surface roughness and residual stress. The inputs of ICBR are cutting parameters and tool wear status. The corresponding outputs of ICBR are surface roughness and residual stress. In the ICBR, K-nearest neighbor method and artificial neural network (ANN) as case retrieval was introduced to retrieve the K similar cases to the inputs. Through retrieving K similar cases, the Gaussian process regression (GPR) model as case reuse was established to output the surface roughness and residual stress. The vibration particle swarm optimization algorithm is proposed to optimize the ANN and GPR models. The high speed milling experiments of Compacted Graphite Iron was performed to validate the performance of ICBR. The experimental results showed that the cutting speed is the most important factor affecting the surface roughness. The feed rate is the most important factor affecting the residual stress. The ICBR gives the accurate estimation of surface roughness with the Mean Absolute Percentage Error of 11.6%. As for residual stress, the prediction accuracy using ICBR is 87.5%. Compared with Back-Propagation neural network, standard CBR and GPR models, the ICBR has better predictive performance and can be used for estimations of surface roughness and residual stress in the actual machining process.

High-speed milling of CFRP composites: a progressive damage model of cutting force

Three-dimensional Hashin failure criterion and material stiffness degradation model were compiled by VUMAT subroutine. The Abaqus/Explicit solver was performed to establish progressive damage model of cutting force for CFRP high-speed milling, and high-speed milling experiments with different cutting parameters were carried out. Further, the impact mechanism of fiber cutting angle and cutting parameters on cutting force, stress, and material failure during milling was explored, and the material removal mechanism in high-speed milling of CFRP was revealed. The results show that the error between the experimental and simulated of cutting forces is less than 5%, which indicates that the progressive damage model is feasible. The fiber cutting angle has significant influence on cutting force and stress in cutting process, and the cutting direction has a significant influence on cutting force. In addition, cutting parameters play a critical role in cutting force, and the feed per tooth is the most significant factor affecting the cutting force. Simultaneously, the progressive damage model predicts that the shear failure of materials mainly concentrates in the cutting area and extends along the feed direction. Finally, the material removal mechanism of CFRP in high-speed milling was revealed combining cutting force experiment.

Investigation on specific milling energy and energy efficiency in high-speed milling based on energy flow theory

Manufacturing energy consumption affects both the environment and manufacturing costs. Therefore, evaluating the energy consumption of the metal cutting processes is required. Accordingly, the idle power and total power were separately measured with a multimeter. A mechanical milling force model was constructed and validated by high-speed milling experiments with the dynamometer. The specific milling force coefficients (with respect to different cutting parameters) were obtained through high-speed milling experiments and a mechanical milling force model. The specific milling coefficients show size effect, with the feed per tooth, axial milling depth and radial milling width decreasing owing to the size effect of the specific cutting force. A general mechanical milling power model considering the size effect was established and validated by the milling power measurements in the milling experiments. The specific milling energy shows the same trend as the specific milling energy coefficients. The specific removing energy increases with rotation speed, feed rate per tooth, axial cutting depth and cutting width decreasing, owing to the idle energy of spindle. The energy efficiency increases as the milling parameters increase, owing to the larger material removal rate (MRR) and the reduced consumption of idle power in specific removing volume. A larger feed per tooth is recommended in high-speed milling, owing to the higher energy efficiency and marginal utility.