Cutting Force Signals Research Articles

Real-time monitoring of depth of cut in the multi-axis milling process with ball-end cutter

Machining accuracy is one of the most critical issues in multi-axis CNC milling. However, various factors have an adverse effect on the production of precise surfaces such as imprecision of tool-setting, force-induced deformation, and machining errors of the previous cut. Monitoring the depth of cut (DOC) in the milling process has become increasingly important. The paper develops a deep neural network for real-time monitoring of the DOC based on the measured cutting force signal. The network is a variant of ResNet and soft-thresholding is embedded for denoising. Raw force signals and other parameters can be handled by the network without preprocessing. The network is trained with multiple milling tests of various DOC and cutter axis inclination angles, and a comparison is made with classic deep residual networks. The results show that the proposed network DRSN64 performs better under high noise levels with high computational efficiency. An average absolute error of 0.03 mm is reached in the real-time tracking test with varying cutter axis inclination angle and DOC.

Consideration of SLM additive manufacturing supports on the stability of flexible structures in finish milling

The supports in additive manufacturing can be used in an innovative way by being considered as supports for machining operation. This innovative use of manufacturing supports can facilitate the finishing of functional thin structures. But the flexible global workpiece-supports system can potentially cause vibrations during the machining operation. This can cause irregular surfaces with bad quality. This study highlights the importance of additively manufactured support structures on the stability of Ti-6Al-4 V parts milling by using supports as a machining fixture. Nowadays, the control of the support stiffness and mechanical properties is not proposed by specific AM software. A way to develop a numerical method to optimize the post-processing of additive manufacturing parts is to use specific lattice structures as supports. Indeed, by adjusting the topology and the beam diameter of lattices, the relative stiffness and the relative density of the global structure can be controlled. The objective of this study is to show that the stiffness of the manufacturing supports is crucial for the machining operation. To validate this concept, milling tests are proceeded on thin-walled plates produced by Selective Laser Melting (SLM) using defined finish milling cutting conditions. Three types of results are obtained: cutting forces signals, displacements and surface qualities by confocal microscopy. The study reveals that milling can induces chatters. Also, surface qualities and dimensional deviations depend on the support choice. The control of mechanical properties of support structures appears to be a good way to favor machining operation of flexible and thin-walled structures. Topology and dimensional parameters of supports have to be considered in preliminary design steps of the additive manufacturing digital chain.

Sensitivity of Micromilling Responses to Grain Variations in Wire Arc Additively Manufactured Aluminum Alloy 4043

Abstract Given the recent trend toward hybrid processing involving the integration of wire arc additive manufacturing (WAAM) and machining capabilities, this paper aims to identify and correlate microstructural variations observed in wire arc additively manufactured aluminum alloy 4043 workpieces to their specific micromilling responses. This is done with the explicit goal of assessing the feasibility of using micromilling responses to detect microstructural variations in WAAM workpieces. As part of this effort, variations in the interlayer cooling time are used to induce changes in the microstructure of a thin-wall WAAM workpiece. The microstructures are first characterized using in-process thermographic imaging, optical microscopy, polarized light microscopy, and indentation. Micromilling slotting experiments are then conducted on different regions within the workpiece. The findings suggest that cutting force signals are the premier candidate for in situ extraction of information regarding microstructural variations within WAAM workpieces. In particular, in situ analysis of the cutting force frequency spectrum can provide critical information regarding dominant failure mechanisms related to the underlying microstructure. Other key micromilling responses such as surface roughness, burr formation, and tool wear also correlate well with the underlying microstructural variations. While these early stage findings hold promise, future research efforts spanning multiple metal alloys systems and micromachining processes are needed to mature the proposed concept.

Micro-milling tool wear monitoring under variable cutting parameters and runout using fast cutting force coefficient identification method

Extracting discriminative tool wear features is of great importance for tool wear monitoring in micro-milling. However, due to the dependency on tool runout and cutting parameters, the traditional tool wear features are incompetent to monitor the tool wear condition in micro-milling with significant tool runout and varied cutting parameter interactions. In this study, micro-milling cutting force is represented by a parametric model including variable cutting parameters, tool runout, and tool wear. The cutting force coefficient in the model, which is not only discriminative to the tool wear condition but also independent to the tool runout and cutting parameters, is extracted as the micro-milling tool wear feature. To reduce the computation cost, a fast neural network–based method is proposed to identify the tool runout and the cutting force coefficient from the cutting force signal. Experimental results show that the proposed cutting force coefficient–based approach is efficient to monitor the micro-milling tool wear under varied cutting parameters and tool runout.

Experimental Study on the Minimum Undeformed Chip Thickness Based on Effective Rake Angle in Micro Milling.

Micro milling is widely used to manufacture micro parts due to its obvious advantages. The minimum undeformed chip thickness, the effective rake angle, and size effect are the typical characteristics and closely related to each other in micro milling. In this paper, the averaging method is proposed to quantitatively estimate the effective rake angle in the cutting process. The minimum undeformed chip thickness is explained based on the effective rake angle and determined to be 0.17 rn (tool cutting edge radius). Then, micro milling experiment was conducted to study the effect of the minimum undeformed chip thickness. It is found that the minimum undeformed chip thickness results in the unstable cutting process, the uneven peaks on cutting force signal, and the dense characteristic frequency distribution on frequency domain signal. The dominant ploughing effect induces the great specific cutting energy and the deteriorated surface roughness due to the minimum undeformed chip thickness.

Stochastic modeling of the cutting force in turning processes

The main goal of this study is to introduce a stochastic extension of the already existing cutting force models. It is shown through orthogonal cutting force measurements how stochastic processes based on Gaussian white noise can be used to describe the cutting force in material removal processes. Based on these measurements, stochastic processes were fitted on the variation of the cutting force signals for different cutting parameters, such as cutting velocity, chip thickness, and rake angle. It is also shown that the variance of the measured force signal is usually around 4–9% of the average value, which is orders of magnitudes larger than the noise originating from the measurement system. Furthermore, the force signals have Gaussian distribution; therefore, the cutting force model can be extended by means of a multiplicative noise component.

Study and characterization of the ductile-brittle transition zone in sintered zirconia

Abstract Ceramics, being brittle in nature are highly prone to surface damage during machining. However, at depths of cut lower than a critical depth of cut, brittle materials undergo microscopic plastic shearing resulting in a crack-free surface. The transition from ductile to brittle takes place over a finite time and depth variation. The identification and characterization of the transition zone is important to attain damages free surface grinding. This work focuses on the identification of the ductile-brittle transition (DBT) zone in yttria stabilized sintered zirconia via the analyses of the force signatures. An algorithm has been developed for the prediction of the onset and end of the transition zone based on the gradients and fluctuations in the cutting force signals. The visual inspection of the ground surface validates the proposed methodology based on force signal processing. The transition zone characteristics were found to be affected by the process parameters and the tool geometry. The increase in the scratch speed reduced the transition onset depth but had negligible effect on the transition end depth. Smaller tool tip radius resulted in increased surface damage whereas larger tip radius gave a relatively smoother surface finish. This methodology of accurate prediction of DBT zone using cutting/thrust force behavior allows the manufacturers to achieve crack-free surfaces in brittle materials without keeping track of cutting depth during machining. Thus, the prediction of the ductile regime by the analysis of force signals opens new horizons in deciding threshold force and depth values in real time, which should be maintained to achieve a damage free surface during grinding.

Study on physical and technological effects of precise turning with self-propelled rotary tool

This study is focused on some physical and technological aspects of a precise turning with Self-Propelled Rotary Tool (SPRT). As part of experiment, the cutting forces, on-line insert run-out and machined surface topography were investigated. Turning tests were carried in the range of variable feeds, depths of cut and cutting speeds for a hardened 41Cr4 steel with the use of a coated carbide round indexable inserts. The effect of cutting conditions on the tool rotation and the dynamics of cutting force signal were evaluated. In particular, changes of cutting forces vs. cutting lengths for different cutting parameters were analyzed in terms of the cutting insert rotation, and Power Spectral Density (PSD) analysis. Moreover, the original method based on cutting force oscillations was employed to estimate the on-line insert run-out. Ultimately, the analysis of a machined surface topography was conducted, considering the interactions between the process inputs and outputs. It was shown that application of high cutting speeds and feeds during SPRT turning can lead to the improvements in surface finish, as well as the stabilization of cutting force values.

Unsupervised online prediction of tool wear values using force model coefficients in milling

Tool wear prediction is an important research in metal cutting, which aims to improve machining accuracy and production efficiency, maximize tool utilization, and reduce machining cost. However, due to high complexity and nonlinearity of tool wear process, it is difficult to establish a general tool wear prediction model, which limits its application in industrial production. To solve this problem, an unsupervised online prediction method for tool wear values is proposed. In the method, a milling force model considering tool wear is established by using analytical method, and parameters varying with tool wear in the force model are integrated into five force model coefficients. The coefficients are solved and updated continuously using the least square estimation method according to the cutting force signals collected in real time. Based on study of relationship between the coefficients and tool wear, a tool flank wear value estimation model is constructed, combined with a time series analysis model, to achieve prediction of tool flank wear values. Experiments are conducted to test the prediction accuracy of tool wear values using the proposed method, and the results show that the average online prediction accuracy reached 72.0%, without supervision. The method has the advantages of low cost and strong adaptability, and can be used for online prediction of tool wear in machining industry.

Online reconstruction of surface topography along the entire cutting path in peripheral milling

This paper presents a novel method for online reconstruction of 3D surface topography in peripheral milling. Compared with the traditional surface topography modeling only focusing on local features as well as losing the time and location information, the presented method can accurately depict not only the variation of dynamic characteristics and the topography features within the local scope, but also along the entire cutting path. Meanwhile, the actual machining time and workpiece location information are introduced in surface topography modeling by using sensor information. The multi-sensor information, including cutting force signals and the eddy current sensor information in multiple directions, is employed to model dynamic factors, namely the vibration relating to deflection of cutting tool and the machining system characteristics, respectively. Two parts of work are carried out on modeling the vibration relating to deflection along the entire cutting path. First, a method based on the Kolmogorov-Smirnov test is applied to the real-time force signals, where the force signals between the entrance and exit points of the cutting teeth along the entire cutting path can be automatically extracted. Second, combining with an axial weight distribution model of the instantaneous cutting thickness, the extracted force signals can be transformed into the required force factors and correspondingly nonlinearly distributed along the instantaneous cutting depth. A piecewise cubic Hermite algorithm is then introduced to create the discrete values of the dynamic factors as continuous models without losing original precision. The continuous models are successfully coupled into ideal cutting edge trajectory equation to form a surface topography model. Further, a numerical algorithm with the characteristics of self-searching function is performed to solve the surface topography model, any point on the workpiece surface can be precisely reconstructed and thus formed the reconstructed surface topography. Eventually, cutting experiments were conducted to validate the effectiveness of the proposed method by comparing the measured and reconstructed results. A good agreement on all the quantitative and qualitative indicators is found. The results demonstrate that the proposed method can accurately realize an online reconstruction of the surface topography along the entire cutting path, and is also an appropriate monitoring approach for a number of features on surface topography (e.g., surface roughness, form deviation).

Vibration recognition for peripheral milling thin-walled workpieces using sample entropy and energy entropy

Due to the poor rigidity of thin-walled parts, vibration (chatter) is extremely easy to occur during the cutting process, affecting the precision, surface quality, and efficiency of part processing. The chatter in milling thin-walled workpieces becomes a more complex, nonlinear, and unstable signal as the dynamics of thin-walled workpieces change with time and position. To realize chatter detection, a method using ensemble empirical mode decomposition (EEMD) and nonlinear dimensionless indicators is proposed in this paper. Firstly, the EEMD is adopted to decompose the raw signal because it is suited for nonlinear and nonstationary signal. Subsequently, the correlation analysis is used to obtain chatter-related intrinsic mode function (IMF) components. When chatter occurs in the milling, time series complexity is changed and energy is transferred to the chatter bands. Therefore, the nonlinear sample entropy (SE) and energy entropy (EE) of IMFs can be extracted as two indicators. Then, principal component analysis (PCA) is adopted to further reduce the feature vector dimension. After that, an improved support vector machine (SVM) is developed to identify the chatter. Among them, genetic algorithm (GA) and grid explore (GE) are used to explore the best parameters of the SVM. In addition, off-line chatter prediction is employed to determine the cutting status under different machining parameters used in the experiments. At last, the cutting force signals are performed to verify the proposed method. The results show the proposed method using SE and EE can effectively detect the chatter, which provides an option for chatter detection.

Optimization and analysis of process parameters for flank wear, cutting forces and vibration in turning of AISI 5140: A comprehensive study

Abstract Monitoring of process parameters enhances the productivity with increased tool life. Variations of cutting forces and Vb have direct influence on VB. In this study the effects of cutting parameters and tool geometry on process variables were investigated with ANOVA. The correlation between Vb and cutting forces with VB were examined and RSM based multiple optimization was performed during turning of AISI 5140. ANOVA results indicated that measured variables are reliable (FC (92.5%), FF (87.5%), FR (74.5%) Vb (81.3%), VB (91.4%)) which shows their availability for correlation. According to graphical results, cutting force signals can be utilized to monitor VB and Vb signals provide supportive information about tool condition. RSM provided optimization with a high success rate (87.5%) and confirmed with an additional experiment. The paper differs from past studies by demonstrating that VB, Vb and cutting forces are correlated with each other, statistically reliable and convenient for multiple optimization.

Machining vibration monitoring based on dynamic clamping force measuring in thin-walled components milling

In milling of thin-walled structures such as impellers or blisks, critical workpiece vibrations occur due to the excitation by the cutting forces and the dynamic interface between the workpiece and the fixture. Vibrations may cause instable state of the milling process, thus decreasing the production outcome by causing rejects. In respect to the milling process of thin-walled workpiece, a challenging task is to monitor the critical vibrations occurred in the workpiece itself or the contact surface between the workpiece and the clamping system. Vibrations can be reflected in the clamping system, and thus the clamping force variation monitoring is a potential scenario or solution to the challenging task. This article introduces a measurement prototype for the real-time dynamic clamping force monitoring. With respect to the measurement of clamping force, two case studies are carried out by a direct sensing method with PVDF thin-film sensors. In the first study, the measurement prototype with embedded PVDF sensors is located on the dynamometer, and an additional eddy-current transducer is applied for monitoring the workpiece deflection during the milling process. Deflection signals and cutting force signals from dynamometer are used as two referential signals to demonstrate the reliability of the prototype with embedded PVDF sensors. In the second study, milling test with the same tool and workpiece is implemented with only PVDF sensors monitoring. Dynamic clamping signal varying with different cutting durations in time, frequency, and time-frequency domain and different milling surface results is performed. Relationship between the collected signals and machining results is preliminarily analyzed. Case studies with thin-walled workpiece show the performance of the measurement prototype.

Milling chatter detection using scalogram and deep convolutional neural network

In this paper, a novel approach of the real-time chatter detection in the milling process is presented based on the scalogram of the continuous wavelet transform (CWT) and the deep convolutional neural network (CNN). The cutting force signals measured from the stable and unstable cutting conditions were converted into two-dimensional images using the CWT. When chatter occurs, the amount of energy at the tooth passing frequency and its harmonics are shifted toward the chatter frequency. Hence, the scalogram images can serve as input to the CNN framework to identify the stable, transitive, and unstable cutting states. The proposed method does not require the subjective feature-generation and feature-selection procedures, and its classification accuracy of 99.67% is higher than the conventional machine learning techniques described in the existing literature. The result demonstrates that the proposed method can effectively detect the occurrence of chatter.

Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors.

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

Modeling and analysis of tool wear prediction based on SVD and BiLSTM

Wear is one of the main forms of tool failure during machining. The prediction of tool wear is of great significance for ensuring the high quality of the workpiece. In order to improve prediction accuracy of tool wear, a tool wear prediction model based on singular value decomposition (SVD) and bidirectional long short-term memory neural network (BiLSTM) is proposed. The cutting force signal is taken as the monitoring signal. Firstly, the raw cutting force signal is reconstructed by Hankle matrix, and the SVD of the reconstructed matrix is performed to extract the signal features. Then, SVD features of the current sampling period and the previous four sampling periods are taken as the input, and the tool wear prediction value at the current time is obtained based on the BiLSTM. The experimental results show that the proposed SVD-BiLSTM model can effectively predict the tool wear and obtain higher prediction accuracy than other comparison models.

Fractal analysis implementation for tool wear monitoring based on cutting force signals during CFRP/titanium stack machining

Hybrid structures of metals and composite materials are increasingly common in aerospace industry, and the optimization and monitoring of the machining of these stacks are an area of active research. Online tool condition monitoring in particular is a valuable capability and is facilitated by real-time treatment of cutting force signals. Cutting force signals are considered one of the most important measures for tool condition monitoring. The present work treats online cutting force time series with fractal analysis. The signal features generated are central to tool wear assessment. This work evaluates the fractal dimension of the cutting force signals from orbital drilling of a stack of carbon fiber–reinforced plastics (CFRP) and Ti6Al4V titanium alloy as a measure of the “roughness” of these signals. It is shown that distinct wear stages are adequately identified using fractal signal features. Low machining quality may thereby be prevented. Additionally, to address the inconvenient need for long machining tests when studying the application of these techniques to CFRP and titanium alloys, a novel fractal index is proposed to improve the monitoring process without requiring extensive experimentation.

Application of the wavelet transform to acoustic emission signals for built-up edge monitoring in stainless steel machining

Built-up edge (BUE) has a significant influence on the process outputs of machining. Unstable BUE can damage the cutting tool edge and adversely affect the machined workpiece surface. However, stable BUE formation can protect the cutting tool surface from further wear, improving the productivity of AISI 304 stainless steel machining. This research proposes a new approach for monitoring and classification of BUE formation with 178 sets of turning experimental tests at different cutting speeds and wear states. The Daubechies wavelet transform is used to differentiate the BUE formation signals from the tool wear signals at different cutting velocities. The acquired acoustic emission (AE) and cutting force signals were filtered using both discrete wavelet, and wavelet packet transforms. Then, wavelet coefficients were processed in both the time and frequency domains with various features being extracted. An adaptive-network fuzzy inference system (ANFIS) model was implemented to detect BUE height at different cutting speeds and different wear states. Finally, two distinct criteria are processed to categorize the BUE state as well as the machined surface roughness throughout the cutting test. The results confirmed the ability of the monitoring system to predict BUE height using AE and force signals.

Improved dynamic cutting force modelling in micro milling of metal matrix composites Part I: Theoretical model and simulations

Cutting force modelling for micro machining is of great importance for better understanding of the cutting mechanics in the process. As precision machining of metal matrix composites (MMCs) is much more complex than machining homogeneous materials, the accurate prediction of cutting force is critical to control the cutting performance and tooling life. This paper presents an improved theoretical dynamic cutting force model in MMCs micro milling process by using straight flute PCD end mills. The theoretical model is modified and improved based on the conventional milling force modelling while taking account of various factors including tooling geometries, the material microstructure, size effect and chip formation in the cutting process. An innovative expression on dynamic cutting force in micromachining MMCs is presented through the multiscale modelling and analysis. The cutting force coefficients are further defined through instantaneous cutting force signals analysis. Simulation results under varied cutting process variables are presented against a series of MMCs micro milling trials, which indicate that the improved dynamic cutting force model can predict the cutting force accurately and reveal more details on the cutting force variations in the process due to the material inhomogeneous nature. In addition, the optimal process variables can be readily explored through the dynamic force modelling and simulations so as to further improve the cutting performance.

Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signals and Cutting Force Signals by Machine Learning Technique

The state of cutting tool determines the quality of surface produced on the machined parts. A faulty tool produces poor surface, inaccurate geometry and non-economic production. Thus, it is necessary to monitor tool condition for a machining process to have superior quality and economic production. In the present study, fault classification of single point cutting tool for hard turning has been carried out by employing machine learning technique. Cutting force and vibration signals were acquired to monitor tool condition during machining. A set of four tooling conditions namely healthy, worn flank, broken insert and extended tool overhang have been considered for the study. The machine learning technique was applied to both vibration and cutting force signals. Discrete wavelet features of the signals have been extracted using discrete wavelet transformation (DWT). This transformation represents a large dataset into approximation coefficients which contain the most useful information of the dataset. Significant features, among features extracted, were selected using J48 decision tree technique. Classification of tool conditions was carried out using Naïve Bayes algorithm. A 10 fold cross validation was incorporated to test the validity of classifier. A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.