Accurate Cutting-Force Measurement with Smart Tool Holder in Lathe.

Cutting force in lathe work is closely related to tool wear and affects the turning quality. Direct measurement of the cutting force by measuring the strain of the tool holder is challenging because the tool holder design aims to be highly rigid in order to undertake large cutting forces. Accordingly, the most popular dynamometer designs modify the standard tool holder by decreasing the structural rigidity of the holder, which reduces the machining precision and is not widely accepted. In order to solve the issue of the low stiffness of the dynamometer reducing the machining precision, in this paper, the ultra-low strain on the tool holder was successfully detected by the highly sensitive semiconductor strain gauges (SCSG) adjacent to the blade cutting insert. However, the cutting process would generate much heat, which increases the force measuring area temperature of the tool holder by about 30 °C. As a result, the readout drifted significantly with the temperature changes due to the high temperature coefficient of SCSG. To solve this problem, the temperature on the tool holder was monitored and a BP neural network was proposed to compensate for temperature drift errors. Our methods improved the sensitivity (1.14 × 10-2 mV/N) and the average relative error of the BP neural network prediction (≤1.48%) while maintaining the original stiffness of the tool holder. The smart tool holder developed possesses high natural frequency (≥6 kHz), it is very suitable for dynamic cutting-force measurement. The cutting experiment data in the lathe work show comparable performance with the traditional dynamometers and the resolution of the smart tool holder is 2 N (0.25% of total range).

A Novel Multivariate Cutting Force-Based Tool Wear Monitoring Method Using One-Dimensional Convolutional Neural Network.

Tool wear condition monitoring during the machining process is one of the most important considerations in precision manufacturing. Cutting force is one of the signals that has been widely used for tool wear condition monitoring, which contains the dynamical information of tool wear conditions. This paper proposes a novel multivariate cutting force-based tool wear monitoring method using one-dimensional convolutional neural network (1D CNN). Firstly, multivariate variational mode decomposition (MVMD) is used to process the multivariate cutting force signals. The multivariate band-limited intrinsic mode functions (BLIMFs) are obtained, which contain a large number of nonlinear and nonstationary tool wear characteristics. Afterwards, the proposed modified multiscale permutation entropy (MMPE) is used to measure the complexity of multivariate BLIMFs. The entropy values on multiple scales are calculated as condition indicators in tool wear condition monitoring. Finally, the one-dimensional feature vectors are constructed and employed as the input of 1D CNN to achieve accurate and stable tool wear condition monitoring. The results of the research in this paper demonstrate that the proposed approach has broad prospects in tool wear condition monitoring.

Design and Development of a Tri-Axial Turning Dynamometer Utilizing Cross-Beam Type Force Transducer for Fine-Turning Cutting Force Measurement.

The main focus of this work was the design and development of a cross-beam force transducer for use in the construction of a tri-axial dynamometer. This dynamometer would be able to measure the cutting force along all three axes simultaneously during turning operations. The force transducer was built on the concept of the Maltese cross-beam, but it had been modified and improved so that it had a higher sensitivity and reduced the amount of interference error or cross-talk error that it produced. An investigation into the distribution of strain, as well as the determination of sensor locations within the transducer construction was carried out by means of finite element analysis. In order to develop a prototype of a turning dynamometer, a number of piezoresistive strain gauges were utilized in the transducer. In order to determine sensitivity, linearity, hysteresis, and repeatability, calibration tests were performed in three directions that were perpendicular to one another. To investigate the dynamic properties and capabilities of the dynamometer for use in turning applications, both modal analysis and actual turning tests were performed. The results of the experiments demonstrated that the newly developed turning dynamometer is a realistic approach for measuring cutting force in machining without reliability and accuracy.

Cutting Force Transition Model Considering the Influence of Tool System by Using Standard Test Table.

The cutting force prediction model usually uses the classical oblique transformation method, which introduces the orthogonal cutting parameters into the oblique milling edge shape, and combines the geometric parameters of the tool to convert the orthogonal cutting force into the actual cutting force, thereby predicting the cutting force. However, this cutting force prediction method ignores the impact of tool vibration in actual machining, resulting in a large difference between the prediction model and the actual measurement. This paper proposes a cutting force conversion model considering the influence of the tool system. The proposed model fully considers the impact of tool vibration on the cutting force. On the basis of the orthogonal model, superimposing the additional cutting force generated by tool vibration makes the predicted value of the model closer to the actual cutting force. The results of milling experiments show that the conversion model can obtain higher prediction accuracy. Moreover, compared with the original conversion model, the accuracy of the proposed model is significantly improved.

Cutting Forces Assessment in CNC Machining Processes: A Critical Review.

Machining processes remain an unavoidable technique in the production of high-precision parts. Tool behavior is of the utmost importance in machining productivity and costs. Tool performance can be assessed by the roughness left on the machined surfaces, as well as of the forces developed during the process. There are various techniques to determine these cutting forces, such as cutting force prediction or measurement, using dynamometers and other sensor systems. This technique has often been used by numerous researchers in this area. This paper aims to give a review of the different techniques and devices for measuring the forces developed for machining processes, allowing a quick perception of the advantages and limitations of each technique, through the literature research carried out, using recently published works.

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.

Optimization and Analysis of Surface Roughness, Flank Wear and 5 Different Sensorial Data via Tool Condition Monitoring System in Turning of AISI 5140.

Optimization of tool life is required to tune the machining parameters and achieve the desired surface roughness of the machined components in a wide range of engineering applications. There are many machining input variables which can influence surface roughness and tool life during any machining process, such as cutting speed, feed rate and depth of cut. These parameters can be optimized to reduce surface roughness and increase tool life. The present study investigates the optimization of five different sensorial criteria, additional to tool wear (VB) and surface roughness (Ra), via the Tool Condition Monitoring System (TCMS) for the first time in the open literature. Based on the Taguchi L9 orthogonal design principle, the basic machining parameters cutting speed (vc), feed rate (f) and depth of cut (ap) were adopted for the turning of AISI 5140 steel. For this purpose, an optimization approach was used implementing five different sensors, namely dynamometer, vibration, AE (Acoustic Emission), temperature and motor current sensors, to a lathe. In this context, VB, Ra and sensorial data were evaluated to observe the effects of machining parameters. After that, an RSM (Response Surface Methodology)-based optimization approach was applied to the measured variables. Cutting force (97.8%) represented the most reliable sensor data, followed by the AE (95.7%), temperature (92.9%), vibration (81.3%) and current (74.6%) sensors, respectively. RSM provided the optimum cutting conditions (at vc = 150 m/min, f = 0.09 mm/rev, ap = 1 mm) to obtain the best results for VB, Ra and the sensorial data, with a high success rate (82.5%).

A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends.

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

Establishment and Verification of the Cutting Grinding Force Model for the Disc Wheel Based on Piezoelectric Sensors.

In this paper, a new model of cutting grinding force for disc wheels is presented. Initially, it was proposed that the grinding cutting force was formed by the grinding force and cutting force in combination. Considering the single-grit morphology, the single-grit average grinding depth, the effective number of grits, and the contact arc length between the grit and the workpiece comprehensively, the grinding force model and the cutting force model were established, respectively. Then, a universal grinding cutting force model was optimized by introducing the effective grit coefficient model, dependent on the probability statistical method and the grit height coefficient model with Rayleigh's distribution theory. Finally, according to the different proportions of the grinding force and cutting force, the grinding cutting force model, with multi-particles, was established. Simulation and experimental results based on piezoelectric sensors showed that the proposed model could predict the intermittent grinding cutting force well. Moreover, the inclusion of the grit height coefficient and the effective grits number coefficient improved the modeling accuracy. The error between the simulation and experimental findings in grinding cutting force was reduced to 7.8% in comparison with the traditional model. In addition, the grinding cutting force can be divided into three segments; increasing, steadiness, and decreasing, respectively found through modeling.

Cutting Forces Measurement for Milling Process by Using Working Tables with Integrated PVDF Thin-Film Sensors.

In the milling process, cutting forces contain key information about the machining process status in terms of workpiece quality and tool condition. On-line cutting force measurement is key for machining condition monitoring and machined surface quality assurance. This paper presents a novel instrumented working table with integrated polyvinylidene fluoride (PVDF) thin-film sensors, thus enabling the dynamic milling force measurement with compact structures. To achieve this, PVDF thin-film sensors are integrated into the working table to sense forces in different directions and the dedicated cutting force decoupling model is derived. A prototype instrumented working table is developed and validated. The validation demonstrates that profiles of the forces measured from the developed instrumented working table prototype and the dynamometer match well. Furthermore, the milling experiment results convey that the instrumented working table prototype could also identify the tool runout due to tool manufacturing or assembly errors, and the force signal spectrum analysis indicates that the developed working table can capture the tool passing frequency correctly, therefore, is suitable for the milling force measurement.

Development and Testing of an Integrated Rotating Dynamometer Based on Fiber Bragg Grating for Four-Component Cutting Force Measurement.

Cutting force measurement is of great importance in machining processes. Hence, various methods of measuring the cutting force have been proposed by many researchers. In this work, a novel integrated rotating dynamometer based on fiber Bragg grating (FBG) was designed, constructed, and tested to measure four-component cutting force. The dynamometer consists of FBGs that are pasted on the newly designed elastic structure which is then mounted on the rotating spindle. The elastic structure is designed as two mutual-perpendicular semi-octagonal rings. The signals of the FBGs are transmitted to FBG interrogator via fiber optic rotary joints and optical fiber, and the wavelength values are displayed on a computer. In order to determine the static and dynamic characteristics, many tests have been done. The results show that it is suitable for measuring cutting force.

Design and Analysis of a Sensor System for Cutting Force Measurement in Machining Processes.

Multi-component force sensors have infiltrated a wide variety of automation products since the 1970s. However, one seldom finds full-component sensor systems available in the market for cutting force measurement in machine processes. In this paper, a new six-component sensor system with a compact monolithic elastic element (EE) is designed and developed to detect the tangential cutting forces Fx, Fy and Fz (i.e., forces along x-, y-, and z-axis) as well as the cutting moments Mx, My and Mz (i.e., moments about x-, y-, and z-axis) simultaneously. Optimal structural parameters of the EE are carefully designed via simulation-driven optimization. Moreover, a prototype sensor system is fabricated, which is applied to a 5-axis parallel kinematic machining center. Calibration experimental results demonstrate that the system is capable of measuring cutting forces and moments with good linearity while minimizing coupling error. Both the Finite Element Analysis (FEA) and calibration experimental studies validate the high performance of the proposed sensor system that is expected to be adopted into machining processes.

Methods and Research for Multi-Component Cutting Force Sensing Devices and Approaches in Machining.

Multi-component cutting force sensing systems in manufacturing processes applied to cutting tools are gradually becoming the most significant monitoring indicator. Their signals have been extensively applied to evaluate the machinability of workpiece materials, predict cutter breakage, estimate cutting tool wear, control machine tool chatter, determine stable machining parameters, and improve surface finish. Robust and effective sensing systems with capability of monitoring the cutting force in machine operations in real time are crucial for realizing the full potential of cutting capabilities of computer numerically controlled (CNC) tools. The main objective of this paper is to present a brief review of the existing achievements in the field of multi-component cutting force sensing systems in modern manufacturing.

Analysis of Cutting Forces and Geometric Surface Structures in the Milling of NiTi Alloy.

This paper presents a study of the total cutting force used and selected parameters of the geometric structure of the surface (e.g., Sa, Sz) during the end milling process of NiTi alloy. The input parameters included are cutting speed (vc), feed per tooth (fz), and radial depth of cut (ae). A Box-Behnken experimental design was employed to conduct the research. The obtained experimental results were utilized within the framework of a response surface methodology (RSM) to develop mathematical and statistical models capable of predicting cutting force components and selected 3D surface parameters. These models provide valuable insights into the relationships between the cutting parameters and the output variables, facilitating the optimization of the NiTi alloy milling process. The findings of this study contribute to a better understanding of the behavior of NiTi alloy during the milling process and offer information for process optimization. By employing a Box-Behnken experimental design, it was possible to investigate the effects of different parameter combinations on the components of total cutting force and selected 3D surface parameters according to ISO 25178, thus aiding in the identification of optimal milling conditions to achieve desired outcomes in the machining of NiTi alloy.

Force and power requirement for development of cumin harvester: a dynamic approach.

An experimental setup was developed for simulating the field conditions to determine the force and power required for cutting cumin crops in dynamic conditions. The effect of cutter bar speeds, forward speeds, and blade type on cutting force and power requirement for cutting cumin were also studied. Experiments were carried out at three levels: cutter bar speeds, forward speeds, and blade type. The results showed that all the factors significantly affected cutting force. The cutting force followed a decreasing trend with the increase in cutter bar speed. Whereas it followed an increasing trend with the increase in forward speed. The maximum cutting force for all three blades was observed at a cutter bar speed of 2.00 strokes.s-1 and forward speed of 0.46 m.s-1. The idle power and actual power required for cutting the cumin crop were also determined based on the cutting force. The results obtained were validated by the power drawn from the power source while operating the cutter bar blades. The R2 values for Blade-B1, Blade-B2, and Blade-B3 were 0.90, 0.82, and 0.88, respectively. The cutting force was primarily affected by the cutter bar speed, resulting in PCR values of 74.20%, 82.32%, and 81.75% for Blade-B1, Blade-B2, and Blade-B3, respectively, followed by the forward speed, which also had an impact on PCR values of 16.60%, 15.27%, and 18.25% for Blade-B1, Blade-B2, and Blade-B3, respectively. The cutting force for Blade-B1, Blade-B2, and Blade-B3 varied from 15.96 to 58.97 N, 21.08 to 76.64 N, and 30.22 to 85.31, respectively, for the selected range of cutter bar speed and forward speed. Blade-B1 had 18 and 30% less power consumption than Blade-B2 and Blade-B3, respectively.

Experiment Comparative Analysis of Feed Rate with Velocity Control in Cutting Mono Crystalline Silicon Using a Diamond Wire Saw.

Fixed-diamond abrasive wire saw cutting is one of the most common methods for cutting hard and brittle materials. This process has unique advantages including a narrow kerf and the ability to use a relatively small cutting force. In the cutting process, controlling the main process parameters can improve the processing efficiency, obtaining a better processing surface roughness. This work designs the PI controller (Proportional-Integral controller) based on the reciprocating wire saw cutting process. The control objects are the workpiece feed rate and wire saw velocity, and the control objective is the normal cutting force. For the control trials, several reference values of various normal cutting forces were chosen. The effects of feed rate and saw velocity on the cutting surface finish and cutting time were investigated in this work using wire saw cutting analysis on a square monocrystalline silicon specimen. The results of this study showed that under a constant applied force of 2.5 N, the optimal feed rate of the diamond wire through the specimen could reduce cutting time by 42% while achieving a 60% improvement in the measured surface finish. Likewise, optimal control of the wire saw velocity could reduce cycle time by 18% with a 45% improvement in the surface finish. Consequently, the feed speed control is more effective than the wire saw velocity.

Development of a rock-bit interaction analytical model by considering the in-situ stresses for a bottom-hole element.

PDC drill bits are an important part of drilling engineering, but improper selection or design can lead to decreased performance and increased costs. Then, accurate modeling of rock-bit interaction for Oil/gas well drilling is critical. Although several mathematical models are presented for this purpose, they have not been able to present a comprehensive model for the rock-bit interaction. In-situ stresses in real drilling conditions affect the force required for rock failure. However, the models proposed so far either have not considered the effects of in-situ stresses or have assumed that the rock failure angle in the downhole conditions is equal to the one calculated in the atmospheric conditions. In this work, after reviewing the background of studies conducted on the rock and bit interaction, with an analytical method, stresses applied to the bottom hole element are examined, including stresses resulting from bit and in-situ stresses. Based on the principle of superposition, the total stress imposed on the bottom hole element is calculated to determine the angle and force of rock cutting. Finally, a novel mathematical model of rock-bit interaction in vertical and deviated oil/gas wells drilling by Considering In-Situ Stresses is presented. Also, the study compares the current model to the Nishimatsu and Xin Ling models using data from a southwest field in Iran. The results show that the simplifying assumption made by previous models leads to a significant underestimation of the failure angle and the amount of force required to the rock failure, with reductions of up to 21% and 48%, respectively, in the case of a vertical well. In an inclined well, the current model predicts cutting force at about 0.14 of that predicted by the previous model.

Experimental studies on the characteristics of chisel picks in coal cutting for bucket wheel excavators.

Chisel pick is a basic and important rock cutting tool, and the performance of chisel pick directly affects rock mining. In this paper, a rock cutting device was developed for chisel picks cutting experiments. The influence of the depth of cutting, width of chisel pick, rake angle, back clearance angle and tip fillet radius on the cutting performance such as cutting force, normal force, and specific energy has been comprehensively studied. In addition to the general conclusions, the experimental results show that the back clearance angle has an influence range on the cutting, and the ratio of the normal force to the cutting force decreases with the increase of the rake angle; the tip fillet radius greatly improve the mean cutting force and specific energy. The experimental results will provide data support for the design of chisel picks on rock excavation machinery and a more reasonable chisel pick cutting rock mechanics model.

Tool Run-Out in Micro-Milling: Development of an Analytical Model Based on Cutting Force Signal Analysis.

Micro-machining is a widespread finishing process for fabricating accurate parts as biomedical devices. The continuous effort in reducing the gap between the micro- and macro-domains is connected to the transition from conventional to micro-scale machining. This process generates several undesired issues, which complicate the process's optimization, and tool run-out is one of the most difficult phenomena to experimentally investigate. This work focuses on its analytical description; in particular, a new method to calibrate the model parameters based on cutting force signal elaboration is described. Today, run-out prevision requires time-consuming geometrical measurements, and the main aim of our innovative model is to make the analysis completely free from dimensional measurements. The procedure was tested on data extrapolated from the micro-machining of additively manufactured AlSi10Mg specimens. The strategy appears promising because it is built on a strong mathematical basis, and it may be developed in further studies.

Analysis of Face Milling of Hard Steel 55NiCrMoV7 by Studying Rough and Semi-Finished Machining and the Influence of Cutting Parameters on Macroscopic Chip Dimensions.

Hard milling is being increasingly used as an alternative to EDM due to its high productivity. The present paper presents the results of theoretical-experimental research on the face milling of hard steel 55NiCrMoV7. A comprehensive analysis of cutting temperatures and forces during single-tooth milling and a morphological examination of the resulting chips are conducted for roughing and semi-finishing operations. The temperature is analyzed in the chip formation area, and the detached chips and the cutting force are analyzed through their tangential, radial, and penetration components, depending on the contact angle of the cutter tooth with the workpiece. The analysis of chip morphology is carried out based on the dimensional and angular parameters of chip segmentation and their degree of segmentation. Based on the central composite design and the response surface method, it is shown that it is possible to mathematically model the dependence of the macroscopic dimensions of the detached chips on the cutting parameters. The determined process functions, the maximum chip curling diameter, and the maximum chip height allow for establishing the influence of the cutting parameters' values on the chips' macroscopic dimensions and, thus, guiding the cutting process in the desired direction.