Tool Holder Research Articles

A State-of-the-art Review on the Intelligent Tool Holders in Machining

A State-of-the-art Review on the Intelligent Tool Holders in Machining

An assessment of monitoring signal quality acquired from an intelligent tool holder

Recently, intelligent monitoring systems have been developed and greatly expanded across the industry through the utilization of machine tools internal data. However, efficiency of these monitoring systems depends on how this data is obtained and processed. Data from an intelligent tool holder that measures the vibrations closest to the cutting zone can also be eligible for evaluation of a machining processes. Therefore, internal CNC data vs. intelligent tool holder monitoring signals were tested under real cutting conditions, compared, and evaluated. In conclusion, recommendations for the potential of future industry applications and research activities have been provided.

Energy Consumption Modeling and Optimization of a Hobbing Machine Tool Considering Multi-Axis Coupling

Energy consumption of machine tools has always been a hot issue in the manufacturing sector due to the concern about climate change. To decrease the energy consumption of machine tools, this paper investigates the multi-axis coupling mechanism of a hobbing machine tool during machining from the design stage, and develops an energy consumption model under multi-axis coupling of the whole machine. Firstly, the multi-axis coupling mechanism in the actual machining process is studied. The hob speed and the feed speed of Z axis moving component will affect the hobbing force, and the component of hobbing force in each direction will affect the load and energy consumption of each axis. Meanwhile, the tracking error of each axis is reduced by a sliding mode controller. Next, a comprehensive energy consumption optimization model of a hobbing machine tool under multi-axis coupling is constructed. Furthermore, three optimization algorithms are implemented to the energy consumption model. Compared with the initial scheme, the simulation experiment results of the case-study indicate that the hobbing machine tool energy consumption is reduced by 1.19%, the steady-state error of each axis is reduced by 35.27%, 21.70%, and 36.34%, respectively, and the maximum deformation of the tool holder is reduced by 10.91%. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is dedicated to dealing with the issue of a hobbing machine tool energy consumption. Based on our previous work, the multi-axis coupling mechanism of the gear hobbing machine tool is investigated, and a comprehensive energy consumption model of the gear hobbing machine tool is proposed. The design variables of the energy consumption optimization model include the structural parameters of the Z axis moving component and the sliding mode controller parameters of the three axes (B, C, and Z axis). Three optimization algorithms are used to drive the optimization model. The simulation results verify the feasibility of the method. The control performance of the system is improved, and the mass of moving component is reduced.

Polishing performance of 3D-printed multi-material photopolymer tools on glass

Due to the increasing demands and complexity of optical glass components to be processed in the future, advanced technologies and precision machining methods will be required in order to produce high-quality optics economically. The polyjet process enables the production of 3D printed gradient index (GRIN) polishing tools with hardness gradients by combining different polymer materials for e.g. synchrospeed polishing. In terms of their geometric design, these tools are significantly more flexible than conventional ones, where the polishing cloth must be glued to metal tool holders. The composition of the polishing pad material directly influences the process efficiency and quality at which the glass is polished. In a laboratory test the influence of the mechanical properties of the multi-material pads on process efficiency, i.e. their long-term stability and the polishing rate of the glass workpiece is demonstrated.

Precision tailoring of laser beam propagation in a single crystalline diamond tool for in situ laser-assisted diamond turning.

The characteristics of laser beam propagation within a diamond tool critically influence the applied thermal softening capability of in situ laser-assisted diamond turning (In-LAT). In the present work, we perform optical geometric analysis, optical simulation and experimental validation to propose a novel diamond tool configuration for precisely tailoring laser beam propagation in In-LAT. First, the characteristics of laser beam propagation in the current In-LAT diamond tool are theoretically and experimentally explored. Second, according to the issues discovered in the current In-LAT diamond tool, an improved tool configuration based on the total internal reflection of a laser beam within the diamond tool is proposed, aiming for promoting refraction of the laser beam from the rake face of the diamond tool as well as eliminating the reflection of laser beam to tool holder. Finally, the optimization of laser beam incident position is carried out for achieving the superior profile and intensity of the emitted laser spot. Current work provides rational laser beam propagation for improving the thermal-softening capability of an In-LAT diamond tool.

INVESTIGATION OF CORRELATION BETWEEN PROCESS PARAMETERS AND VIBRATION WITH USE OF WASTE CBN INSERTS IN DEEP ROLLING

Vibration is a mechanical phenomenon in which oscillations occur around an equilibrium point. In addition, the used and waste-cutting inserts create a great problem for the industry; therefore, these inserts constitute a problem to be solved in terms of both recycling and waste costs. The aim of this study is to analyze the correlation between vibration process parameters, formed during deep rolling, and surface roughness, micro-hardness with the using of the waste CBN inserts. For this, in total, 27 experiments in total were conducted with the help of a specially designed tool holder after parameters had been selected. The surface roughness, micro-hardness and vibration values of the specimens that were applied deep rolling were measured and the correlation between these values was investigated. When deep rolling was applied to the workpieces with used and waste CBN inserts, it was seen that the waste CBN inserts could be used in deep rolling and that the surface roughness improved and the surface hardness increased. In addition, it was seen that with the increase of the number of passes and rolling forces, the micro-hardness increased, and that feed rate, the number of passes and rolling forces had the biggest effect on vibration, respectively.

Determination of the actual deflection of the cutting tool ın turning by the Castigliano theorem

The turning method is a widely utilized technique in machining. The effect of machining parameters on the machining quality in turning is quite high. A longer tool overhang reduces tool rigidity, leading to increased tool deflection and subsequently, a decrease in machining quality. The tool overhang should be as short as possible in order to reduce tool deflection and eliminate the negative effects of vibration. Another critical factor affecting machining quality, as much as the tool overhang length is the distance between the main cutting edge and the tool holder axis. In the turning process, the alteration of the main cutting-edge position significantly influences the amount of tool deflection. When the main cutting edge is positioned farther from the tool holder axis, it generates a greater torsional moment effect, which negatively impacts machining quality. However, although the main cutting edge position of the turning tool has a great effect on the machining quality, it has not been adequately explored in the existing literature.This study examines how deflection values vary concerning the deviation of the main cutting edge from the tool holder axis using various methods and compares the results. In contrast to the commonly used deviation equation, this research applies the Castigliano theorem, which provides results much closer to actual tool deflection values. The outcomes derived from the Castigliano theorem are then compared with finite element analysis (FEM) and experimental studies. The proposed theoretical approach aligns well with the experimental results, highlighting the significance of considering torsional moments alongside bending moments as they both play a substantial role in tool deflection.

Study on tool wear state recognition algorithm based on spindle vibration signals collected by homemade tool condition monitoring ring

With a view to further realising the intelligence of tool condition monitoring (TCM) and to address the high cost and low stiffness problems of existing smart tool holders, this study firstly proposed a tool condition monitoring ring, which can be mounted on the tool shank and is easily disassembled and reused. This ring acquires the triaxial vibration signals of the spindle. This study also firstly applied manifold learning to TCM model for data dimensionality reduction and proposed a weighted random forest based on K-nearest neighbour (KWRF). A tool wear state recognition algorithm based on wavelet threshold de-noising (WTD), variational mode decomposition (VMD), manifold learning and KWRF was proposed. Four sets of full life cycle milling experiments were carried out on 45 steel with cemented carbide milling inserts. The results showed that the recognition accuracy of the proposed model reached 98.74%, which verified the effectiveness of the proposed model.

The effect of friction spot stir welding in double rivet lap joint installation of aluminum 2024-T3 on the strength of shear tests

The process of joining materials is very necessary to facilitate the manufacture of industrial products. Among these connections are rivets, bolts, nails, glue, and welding which is mostly done on metal materials. The aircraft skin is usually connected with rivets through a drilling process. Joining metal materials, especially aluminum, uses many welding techniques. The welding method currently being developed is Friction Stir Welding (FSW). FSW developed the Friction Stir Spot Welding (FSSW) process in joining metals, especially in this study using 2024-T3 aluminum. The purpose of combining the rivet method with FSSW is to see the material’s mechanical characteristics when applied to aluminum material. The FSSW method uses a 2500 RPM engine speed milling machine using a blunt tool holder with a pin dimension of 2.5 mm. It is carried out using a pneumatic drill using a drill bit with a dimension of 2.5 mm. Aluminum 2024-T3 has of 200×20×2 mm dimensions, a hole spacing of 15 mm with double rivet lap joint installation. The results showed that the FSSW variation had a higher tensile shear strength of 2.8% than the drilling variation. However, the hardness value in the drilling variation is 56.3% higher than the FSSW variation in the Heat Affected Zone (HAZ). Microstructural observations also indicate differences in the HAZ region, where the FSSW is reduced in size and longer. This is due to the heat treatment process due to friction between the pin tool and the aluminum, thus changing the structure.

Impact of Chatter Amplitude on Surface Roughness of Carbide Insert Tool Using Laminates among Tool and Tool Holder

&#x0D; Chatter seems throughout the metallic reducing method as a result of resonance resulting from the interaction of the prominent natural frequencies of the gadget with the frequency of chip serration. Chatter leaves a lousy floor finish on the part and negatively affects dimensional tolerances, decreased productiveness, immoderate device wear and damaged device-device components. This paper provides an progressive approach to chatter suppression throughout turning manner by way of offering extremely-thin steel rubber laminates between carbide insert device and tool holder via growing the stiffness of the device. The dynamic behavior of the workpiece and device with laminates are simulated the usage of ANSYS and investigated experimentally. it's miles obvious that with the introduction of rubber brass laminates among tool and device holder the amplitude of vibration of tool decreased and thereby floor finish of workpiece advanced.

Impact of Chatter Amplitude on Surface Roughness of Carbide Insert Tool Using Laminates Among Tool and Tool Holder

Chatter seems throughout the metallic reducing method as a result of resonance resulting from the interaction of the prominent natural frequencies of the gadget with the frequency of chip serration. Chatter leaves a lousy floor finish on the part and negatively affects dimensional tolerances, decreased productiveness, immoderate device wear and damaged device-device components. This paper provides an progressive approach to chatter suppression throughout turning manner by way of offering extremely-thin steel rubber laminates between carbide insert device and tool holder via growing the stiffness of the device. The dynamic behavior of the workpiece and device with laminates are simulated the usage of ANSYS and investigated experimentally. it's miles obvious that with the introduction of rubber brass laminates among tool and device holder the amplitude of vibration of tool decreased and thereby floor finish of workpiece advanced.

Using machine learning for cutting tool condition monitoring and prediction during machining of tungsten

ABSTRACT Machining of single-phase tungsten, used as a plasma facing material in fusion energy reactors, is commonly associated with rapid tool wear and short tool life. Conventional methods of monitoring tool wear or changing cutting tools after a predetermined period are inefficient and can lead to unnecessary tool change or risk damaging the workpiece. Tool wear can adversely affect the surface finish and dimensional tolerances of machined parts. Predicting its onset can avoid this critical damage whilst ensuring maximum tool life is utilised. In this paper, firstly the tool life results in end milling single-phase tungsten using different cutting tool geometries and cutting speeds are provided for the first time. A novel method is proposed by combining sensor signal prediction and classification machine learning models. It works by forecasting the cutting tool bending moment signal which is then used for predicting future cutting tool condition in end milling of pure dense tungsten. A series of machining experiments, covering the whole life of a cutting tool, were performed to collect the sensor signals. The current time series signal from the sensory tool holder is employed to forecast the future signal by training a 1D convolutional neural network (1D CNN) and an artificial neural network (ANN). The forecasted signal is then used to predict the state of the cutting tool in the future. Machine learning classifiers namely, random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) supervised learning models were trained and validated on actual sensor signals to correlate the tool conditions with specific sensor signal features. The investigations revealed that the 1D CNN performed best in forecasting the time series sensor signal whilst achieving a mean absolute error of 3.37. In addition, the RF, when trained on Wavelet Scattering features, resulted in the most accurate classification of sensor signals for tool condition detection. The analysis showed that the combination of 1D CNN signal forecasting, feature extraction through statistical analyses and RF classifier performs best in predicting the state of a cutting tool in near future. Using this method allows for decision making for changing the tool whilst ensuring that the maximum useful life of a cutting tool is utilised. It also enables preventing undesired damage to the machined surface due to late detection of tool wear or delays in taking appropriate actions. The application of this method can reliably reduce the manufacturing costs and resource consumption associated with cutting tools for machining tungsten and minimise tool wear induced damage to the workpiece.

A new approach to failure mode and effect analysis under linguistic Z-number: A case study of CNC tool holders

Failure Mode and Effects Analysis (FMEA), as the most important component of risk management, has been widely used in the engineering field. Although the method is widely used, the traditional FMEA approach does not take into account the issue of decision maker's mental preference, which seriously affects the accuracy of risk ranking results. To improve these shortcomings, we fuse the language set and Z-number to propose the language Z-number to express the decision information. Meanwhile, we propose a new linguistic Z-number weight Muirhead mean (LZNWMM) operator and use it to aggregate the information. Finally, we propose a new FMEA framework, which uses the proposed operator to aggregate information and calculate attribute weights using the fuzzy best-worst method. Using this method applied to a computer numerical control (CNC) tool holder manufacturer, its usability and validity are verified by comparing it with the traditional FMEA method, LZNWA method and LZNBM method.

Machining Characteristics of High-gradient Inside Surface Using Long Tool Holder

In order to meet the requirements of space reachability, the high-gradient inside surfaces such as infrared optical domes need to be machined with a long tool holder. Under the action of lateral cutting force, the long tool holder is easy to chatter, and the machined surface will roughen. The directionality of cutting force and its influence on chatter marks are studied in the machining process of high-gradient inside surface, and the dynamic model on tool holder is built in this paper. The natural frequency increases to 2820Hz for long tool holder by optimizing its structure and the clamping method, and the dynamic characteristics is greatly improved. The surface roughness is better than Ra 3.3nm for thermocompressed polycrystalline MgF2 parts which are machined in different directions, there aren’t chatter marks on the surface, and the machining requirements are satisfied for infrared optical parts.

Pre-Compensation of Thermal Error for Laser-Assisted Diamond Turning.

The laser-assisted diamond turning (LADT) method can effectively improve the machinability of hard and brittle materials based on the laser heating effect, resulting in prolonged diamond tool life and better surface integrity. However, due to the incomplete absorption of laser beam energy within the workpiece cutting zone, simultaneous heating of the tool holder occurs, resulting in a structural thermal expansion that affects the workpiece form accuracy. In this article, the form accuracy of a LADT-machined workpiece was systematically studied. Accurate calculations of the tool shank and tool holder thermal fields and thermal expansion were performed using thermodynamic coupled finite element analysis. In addition, the LADT tool path was precisely pre-compensated by taking into account the structure expansion. The experimental results demonstrate that the form accuracy can be significantly improved with a pre-compensated tool path, which provides crucial technical support for achieving a high-precision finish on optical elements using the LADT method.

Identification and effect of chip shear band on chatter vibration in the turning of Nickel Alloy 718

Shear bands occur during the machining of high-strength materials such as Nickel and Titanium alloys. Shear bands lead to segmented chips, which in turn causes fluctuation in the force that excites vibrations. In some cutting conditions, it is difficult to distinguish regenerative chatter from the shear bands because both originate from chip thickness oscillations. This paper presents the relationship between the chip segmentation and regenerative chatter in turning Alloy 718. The tests are conducted with tool holders having different flexibilities, hence natural frequencies. The chatter stability diagrams with process damping effect are predicted first, followed by experiments in stable and unstable zones. It is observed that, at low-speed machining of Alloy 718, the chip segmentation is caused by chatter. At high speeds, thermo-mechanical induced adiabatic shear dominates the chip segmentation but may become coupled with the relative vibrations between the tool and workpiece. While chatter occurs close to a dominant natural frequency of the structure, shear banding frequency is dependent on the cutting speed and chip thickness and may occur at a distinct frequency.

Vibration-Damped Tool Holders in Industrial Robotic Machining Systems

Since the introduction of automation systems has begun to be applied to most industries with the development of technology, the utilization of industrial robots is increasing every year. However, the processing field a such as milling and Rough Cutting processing process occurs vibration from large processing load. Since industrial robots basically have low stiffness, they are vulnerable to vibration generated from processing loads when processing. In this paper, multi-axis active spindle holder of high stiffness was developed to improve precision and quality deterioration due to vibration generated from a processing load. If an active spindle holder of high stiffness is applied, it can increase the dynamic stiffness of the robot by blocking vibrations in the low-frequency range at the end effector before they are transmitted from the spindle to the robot, and as a result, it is expected to improve precision and quality. The performance evaluation of the active spindle holder manufactured based on the design was proceeded, and it was verified that the active spindle holder developed to compensate for the shortcomings of industrial robots was made with moving range and high stiffness of 200 Hz more. Furthermore, machining experiments confirmed an improvement in quality of more than 10%.

Analyzing the effect of tool thickness on surface roughness and straightness in CNC parting operation of AA6063

Surface roughness and straightness of AA6063 Aluminium alloy while performing experimental studies during the parting operation is discussed in this paper. The material and its standard dimensions were selected based on easy availability in the market and its application in ship and airplane industries. The selected material has diameter of 25 mm. Parting tool holder and inserts were selected with regards to their compatibility with the CNC machine. High-Speed Steel (HSS) square parting tool holder mounted by uncoated cemented carbide inserts of two different thicknesses, 3 mm and 4 mm were used. Parting operations were carried out on Gedee Weiler Eco Turn ET25 CNC machine using HSS square tool.Process parameters such as Tool thickness (A) (3, 4 mm), Speed (B) (1250, 1500, 1750 rpm), Feed rate (C) (3, 4, 5 mm/min) were varied to determine the results of the experimental study. It was concluded that better surface finish and degree of straightness were obtained at tool thickness of 3 mm, cutting speed of 1750 rpm and feed rate of 4 mm/min. Surface roughness testing was conducted using a Mitutoyo-SJ 210 Stylus-R5 profilometer. Surface straightness testing was conducted on Daisy 564 Cimtrix CMM.The findings indicated that the feed rate, followed by tool thickness and cutting speed, has the greatest influence on the surface roughness (Ra) characteristics of AA6063 material. While comparing the speed and feed in both tool thicknesses, it is observed that as we increase the tool thickness, there is an increase in both surface roughness and in the degree of straightness. The most favorable outcome obtained from the experiment shows that tool thickness 3 mm can be used for better surface finish and flatness.

Stability prediction via parameter estimation from milling time series

Machine tool vibrations impose severe limitations on industry. Recent progress in solving for the stability behavior of delay differential equations and in modeling milling operations with time delay differential equations has provided the potential to significantly reduce the aforementioned limitations. However, industry has yet to widely adopt the current academic knowledge due to the cost barriers in implementing this knowledge. Some of these cost prohibitive tasks include time-consuming experimental cutting tests used to calibrate model force parameters and experimental modal tests for every combination of tool, tool holder, tool length, spindle, and machine. This paper introduces an alternative approach whereby the vibration behavior of a milling tool during cutting is used to obtain the necessary model parameters for the common delay differential equation models of milling.

Analysis of trajectory and motion parameters of an industrial robot cooperating with a numerically controlled machine tools

The tendency to improve manufacturing processes also applies to robotic and automated processes. Optimization of the trajectory and motion parameters of an industrial robot is one of the most important aspects of effective production. Robots are often not independent mechanical units, but interact with other work machines. The effective cooperation of mechanical devices is a significant technological challenge. In this paper, the types of positioning, trajectories and motion parameters of an industrial robot cooperating with numerically controlled machine tools were analyzed. The considered example is the process of machining the riveting tool holder. The tasks of offline programming of numerically controlled machine tools and robots were combined. For this purpose, SinuTrain and RobotStudio software were used. The main simulation analyzes indicated the relationship between the programmable parameters of the robot's motion and the actual values of the speed and acceleration of the tool center point. In the paper, the influence of speed and positioning accuracy on the duration of the process were also indicated. The results of the analyzes allowed to optimize the working time of the robot while maintaining the appropriate quality of the process.