Gear Hobbing Research Articles

Approach for multiscale modeling the thermomechanical tool load in gear hobbing

In this report, an approach is presented how a geometric penetration calculation can be combined with FE simulations to a multiscale model, which allows an efficient determination of the thermomechanical load in gear hobbing. FE simulations of the linear-orthogonal cut are used to derive approximate equations for calculating the cutting force and the rake face temperature. The hobbing process is then simulated with a geometric penetration calculation and uncut chip geometries are determined for each generating position. The uncut chip geometries serve as input variables for the derived equations, which are solved at each point of the cutting edge for each generating position. The cutting force is scaled according to the established procedure of discrete addition of the forces along the cutting edge over all individual cross-section elements. For the calculation of the temperature, an approach is presented how to consider a variable chip thickness profile. Based on this, the temperature distribution on the rake face is calculated. The model is verified on the one hand by cutting force measurements in machining trials and on the other hand by an FE simulation of a full engagement of a hob tooth.

A method for predicting hobbing tool wear based on CNC real-time monitoring data and deep learning

Intelligent monitoring and diagnosis of tool status are of great significance for improving the manufacturing efficiency and accuracy of the workpiece. It is difficult to quickly and accurately predict the wear state of worm gear hob under different working conditions. This paper proposes a novel approach to predict hob wear status based on CNC real-time monitoring data. Based on the open platform communication unified architecture (OPC UA) technology and orthogonal test, the machine data of motor power, current, etc. related to tool wear are collected online in the worm gear machining process. And then, an improved deep belief network (DBN) is used to generate a tool wear model by training data. A growing DBN with transfer learning is introduced to automatically decide its best model structure, which can accelerate its learning process, improve training efficiency and model performance. The experiment results show that the proposed method can effectively predict hob wear status under multi-cutting conditions. To show the advantages of the proposed approach, the performance of the DBN is compared with the traditional back propagation neural network (BP) method in terms of the mean-squared error (MSE). The compared results show that this tool wear prediction method has better prediction accuracy than the traditional BP method during worm gear hobbing.

Research of the influence of axial displacement on the wear of hob milling tools

The need to increase productivity and cost-effectiveness of gear cutting of cylindrical gears with oblique teeth creates favorable conditions for the development of research in the field of tribology and the increasing application of tribological knowledge in industrial practice. Improving the gear cutting process by hob milling is important and useful for both gear manufacturers and hob milling tools manufacturers. Increasing productivity and increasing of the quality of serration are the basic directions of improving the process of gear cutting by hob milling. Technological parameters significantly affect the wear of cutters and taking into account the parameters for defining the economy of the process affect the achievement of optimal parameters of the cutting regimes. Based on experimental research, the paper presents an analysis of the influence of axial displacement, as one of the technological parameters, on the wear of hob milling tools in the gear cutting of cylindrical gears with oblique teeth.

New design of the evolvent worm mill with carbide turning rack

For high-speed cutting of teeth of cylindrical wheels made of structural and difficult-to-machine steels and alloys, cast iron, non-ferrous metals and various non-metallic materials with increased abrasive ability in conditions of mass and large-scale production, carbide hob cutters are used. A promising direction for hob cutters is the use of rotary racks as cutting elements. However, when they are rotated by 180°, errors in the tool profile are transferred to the workpiece. Therefore, the existing designs of such cutters are used only for roughing or semi-finishing, reaching accuracy classes B, C and D according to GOST 9324-2015. In order to improve their accuracy, the article proposes a new design of the assembled hob cutter with carbide rails, which allows maintaining the required tooth profile after turning the cutting elements. Milling cutters with flat front surfaces and back surfaces are adopted, the generatrix of which is perpendicular to the cutting edge. From the point of view of the working conditions of the cutting tool, studies of the kinematic angles were carried out. The calculation was carried out according to the formulas derived according to the scheme for the general option of cutting the wheel with a worm cutter. In this case, the working angles were calculated at points on the tooth profile at different moments of rotation of the cutter and along the entire length of the cutter. The analysis of the influence of the main parameters of the cutter on the cutting angles is presented. The possibility of using the design of a prefabricated worm cutter with flat front surfaces and back surfaces, the generatrix of which is perpendicular to the cutting edge, has been confirmed. Recommendations for achieving optimal working angles in the process of gear hobbing for milling cutters of the adopted design are proposed.

Modelling and experiment of gear hob tooth profile error for relief grinding

Gear hob is an important tool that is most used in gear processing. Hob accuracy directly exerts an overwhelming influence on the quality of the processed gear. Generally, the hob tooth profile accuracy is mainly determined by relief grinding process. Studies on tooth profile errors of gear hobs caused by severe friction and cutting with the high-speed rotation of the wheel during the form grinding machining of hobs are limited. Thus, a theoretical model of the tooth profile error prediction under different machining parameters was established based on the analysis of coupling influence of high temperature and high strain rate on gear hobs in the relief grinding process. The model was completed on the basis of the dynamic explicit integral finite element method of thermo-mechanical coupling. Through the prediction model, the influence of the grinding depth ap, feed speed Vw and grinding speed Vs on the tooth profile error can be analysed. In addition, an algorithm for accurately calculate the grinding wheel axial profile by combining instantaneous envelope theory and hob normal tooth profile was proposed. The hob relief grinding experiments were carried out using the proposed grinding wheel profile algorithm. The relative error of the prediction obtained by comparing the calculation results of the prediction model with the experimental results is within 10%. Results prove the validity of the prediction model. This finding is greatly important for optimising the accuracy of hob relief grinding.

Research on Geometric Error Prediction of High Speed Dry Cutting Gear Hobbing Based on MPGA and BPNN

The geometric error of high-speed dry cutting gear hobbing is affected by many factors, such as process parameters, gear hobbing machine tools and hobs, and it is difficult to predict. This paper uses the vibration of the hob spindle to predict the geometric error, and builds a geometric error prediction model for high-speed dry cutting gear hobbing based on multiple population genetic algorithms and BP neural network. This model comprehensively considers the influence of the hob spindle speed, feed rate and hob spindle vibration on the geometric errors of high-speed dry cutting gear hobbing, which can provide a useful reference for the improvement of gear machining geometric accuracy. Through the experiment, it is concluded that adding the hob spindle vibration during the machining process as the model input can obtain more accurate prediction results of the geometric error of the hobbing machining. Compared with the traditional BP neural network prediction model, this model has better prediction ability.

Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process

Dry hobbing has received extensive attention for its environmentally friendly processing pattern. Due to the absence of lubricants, hobbing process is highly dependent on process parameters combination since using unreasonable parameters tends to affect the machining performance. Besides, the consideration of tool life is frequently ignored in gear hobbing. Thus, to settle the above issues, a multi-objective parameters decision approach considering tool life is developed. Firstly, detailed quantitative analysis between process parameters and hobbing performance, i.e., machining time, production cost and tool life is introduced. Secondly, a multi-objective parameters decision-making model is constructed in search for optimum cutting parameters (cutting velocity v, axial feed rate $$f_{{\text{a}}}$$ ) and hob parameters (hob diameter d0, threads z0). Thirdly, a novel algorithm named multi-objective multi-verse optimizer (MOMVO) is utilized to solve the presented model. A case study is exhibited to show the feasibility and reliability of the proposed approach. The results reveal that (i) a balance can be achieved among machining time, production cost and tool life via appropriate process parameters determination; (ii) optimizing cutting parameters and hob parameters simultaneously contributes to optimal objectives; (iii) considering tool life provides usage precautions support and process parameters guidance for practical machining.

Multi-objective Optimization and Decision-making Method of High Speed Dry Gear Hobbing Processing Parameters

Multi-objective Optimization and Decision-making Method of High Speed Dry Gear Hobbing Processing Parameters

Productivity Improvement Through Cycle Time Reduction in A Gear Manufacturing Industry

In this work, Statistical process control (SPC) and Design of experiments (DOE) was used to reduce the cycle time of gear hobbing process. The roller lever shaft and gear shaft occupies about 35% production capacity of gear hobbing machine in a month. Statistical Process Control (SPC) analysis was done to identify how the process is performing against its specification limit. In order to increase the production without compromising the quality, the process parameters is should be enhanced to improve the quality. In this process, Hob tool, Feed rate, RPM and Hob material were considered as the most influencing input parameters. Cycle time was considered as a response variable. For experimentation Hob tool, Feed rate and RPM are used to found the best process parameter to get optimized cycle time. And finally it is observed that the feed rate is the dominating parameter to reduce the cycle time. The optimal parameter leads to decrease in processing time of gear hobbing in roller lever shaft by 26.29%.

Tool wear in dry gear hobbing of 20MnCr5 case-hardening steel, 42CrMo4 tempered steel and EN-GJS-700-2 cast iron

The demand for higher power densities in gear applications leads to the use of high-strength materials. This poses a challenge for manufacturing technologies with regard to the machinability of these materials. Even though gear hobbing is one of the most common technologies for gear machining, only limited investigations on the machinability of high-strength materials have been carried out. In this paper, the machinability of different workpiece materials by gear hobbing under dry cutting conditions is investigated. The tool life and wear behavior of powder metallurgical high-speed steel PM-HSS S390 and sintered tungsten carbide – cobalt WC-Co K30 tools with an AlCrN coating were analyzed. 20MnCr5 case-hardening steel, 42CrMo4 tempered steel and EN-GJS-700-2 cast iron were machined using the fly-cutting trial as an analogy process for gear hobbing. To identify a respective target tool life of LT,10 = 10 m, the cutting speed was varied while the feed rate was defined based on the respective tool concept and cutting substrate. The experiments showed a good machinability of the soft state material 20MnCr5 with PM-HSS S390 tools up to a cutting speed of vc = 350 m/min. A machining of the high-strength materials 42CrMo4 and EN-GJS-700-2 resulted in high tool wear or catastrophic tool failure. Even at low cutting speeds of vc = 50 m/min, the target tool life LT,10 could not be reached. When using WC-Co K30 tools, tool lives of LT > 6 m and cutting speeds up to vc = 400 m/min were feasible when machining 42CrMo4 tempered steel. For the machining of EN-GJS-700-2 cast iron, high abrasive tool wear was observed, which led to short tool lives of LT < 4 m at cutting speeds above vc ≥ 200 m/min. By reducing the cutting speed to vc = 100 m/min, a tool life of LT > 15 m could be achieved.

Data-driven based optimization for high-speed dry cutting gear hobbing processing parameters

The formulation of gear hobbing processing parameters relies on extensive professional knowledge and artificial experience, and inappropriate processing parameters will lead to low machining precision and high manufacturing costs. In order to obtain suitable processing parameters in high speed dry hobbing gear process, Gradient boosting regression (GBR) and Generalized Regression Neural Network (GRNN) are adopted to establish a process parameter optimization model targeting gear machining accuracy and machining energy consumption. And then a method based on Differential Evolution (DE) algorithm, parameter adaptive multi-object differential evolution (AMDE), for hobbing processing parameter optimization is proposed. Machining experiment verification shows that the established gear hobbing accuracy prediction model and energy consumption estimation model have high prediction accuracy, and AMDE has strong optimization capabilities and robustness.

Development of thermal error mapping model for the dry gear hobbing machine based on CNN-DAE integrated structure and its application

The dry cutting gear hobbing machine is a specialized manufacturing equipment for small-module gears. During the continuous large-volume gear cutting process, the thermal error plays a major negative role in the quality of gear making. In this paper, based on the convolutional neural network and the denoising auto-encoder algorithm, we developed a new mapping model between features of characteristic signals and the thermal-induced deviation of the spacing between the hob and the workpiece. In the modeling, the temperature change of key thermal points, the hob spindle current signal, and the corresponding thermal-induced deviation trend of spacing between the hob and the worktable in a continuous gear cutting experiment were collected and sampled. Those samples were fed into the TensorFlow framework to train the integrated mathematical structure. Finally, the mapping model was obtained, which was capable to accurately and quickly quantify the thermal error of the YDE-type dry gear hobbing machine. Accordingly, a thermal error compensation system was developed as well. To prove the accountabilities of this newly proposed mapping model as well as the corresponding thermal error compensation system, another continuous gear cutting verification experiment was carried out on the dry gear hobbing machine equipped with this new thermal error compensation system. The experiment results showed that with the help of the newly proposed thermal error model and compensation system, the M values of machined gears, which directly reflected the effect of the thermal error on the gear hobbing accuracy, remained in a practically acceptable range (± 0.02 mm) in the continuous gear cutting, which proved the reliability of our proposed new mapping model.

Multiscale feature extraction from the perspective of graph for hob fault diagnosis using spectral graph wavelet transform combined with improved random forest

The hob forms key component in gear hobbing machine and its health condition directly affects the reliability and safety of entire machine. This paper proposes a multiscale feature extraction scheme based on spectral graph wavelet transform combined with improved random forest, forming a novel hob fault diagnosis technique and realizing multi-scale analysis of vibration signals from the perspective of graph. Firstly, the vibration signal samples are transformed into path graphs, which contain the vertices information and similarity information between connected vertices, enriching the input information. Then, the path graphs are preprocessed by spectral graph wavelet transform at five-level decomposition for feature extraction. Finally, the random forest improved by adaptive beetle antennae search is utilized to identify the hob fault. Two groups of experimental results indicate that the proposed method has high effectiveness and robustness, achieving all the identification accuracy greater than 90% under multiple operating conditions and various environmental noises.

Analysis method for factors influencing gear hobbing quality based on density peak clustering and improved multi-objective differential evolution algorithm

ABSTRACT For addressing the problem that the quality indicators of gear hobbing are complicated and the influencing factors are unknown, a characteristic processing method combining improved multi-objective differential evolution (IMODE) and clustering based on peak density (DPCA) is proposed. This method can extract the characteristic parameters that strongly influence gear hobbing quality for multi-process parameters and multi-quality indicators, and quantify their importance to the comprehensive quality indicators. First, based on correlation analysis of the quality inspection parameters by DPCA, a set of relatively independent gear hobbing quality inspection indicators is obtained, and the dimensions of the quality inspection parameters are reduced for more effectively reflecting the hobbing processing quality. Next, multi-threshold Birch (IBirch) clusters are obtained for different gear hobbing quality inspection data under different process parameters to obtain cluster labels. Finally, Rough Sets theory and IMODE are used to reduce the gear hobbing process parameters and design parameters. Feature parameters that significantly affect the hobbing process quality are extracted from the process parameters and their importance is quantified. The validity and practicability of the method are verified by processing experiments, and the advantages of the proposed method are proved.

Integrated optimization method for helical gear hobbing parameters considering machining efficiency, cost and precision

The formulation of dry hobbing processing parameters depends heavily on mass experiments and the experiences of skilled technicians, and the use of unsuitable parameters will lead to high cost, low efficiency and severe precision defects. To resolve this issue, a helical gear processing parameter optimization method (PPOM-HG) is proposed in this paper. First, an efficiency-cost-accuracy triple-target optimization model is established. A manufacturing efficiency model is established through a detailed analysis of the geometric trajectory of a hob. A helical gear manufacturing cost model is established by analyzing the power curve of the hobbing machine and the hob’s lifetimes under various processing parameters. A modified correlation analysis random forest (CARF) model is designed for predicting gear machining precision, which replaces the traditional empirical precision function. Then, for searching for the optimal solution of the established efficiency-cost-precision triple-target model, an adaptive multiobjective fusion evolutionary algorithm (AMFEA) with adaptive evolution parameters is proposed. Finally, via many helical gear machining experiments, the validity and advantages of the proposed CARF and AMFEA methods are demonstrated, and the selection strategy of the Pareto front solution under various conditions is discussed.

Chip geometry and cutting force prediction in gear hobbing

This paper presents a tri-dexel geometric engine integrated simulation model for the gear hobbing operation. The process kinematics are modeled and validated using CNC signals from a Liebherr LC500 hobbing machine. Workpiece geometry updating and cutter-workpiece engagement (CWE) calculations are efficiently realized in the tri-dexel engine. 3D force contributions at discretized nodes along the hob's cutting edges are computed considering the localized principal cutting directions, and rake and inclination angles. To measure cutting forces, a rotary dynamometer is successfully adapted and used alongside a Kalman filter to compensate for structural dynamics. The predicted forces agree well with their experimental counterparts.

Calculation of the maximum chip thickness for a radial-axial infeed in gear hobbing

A numerical penetration calculation was used to simulate the radial-axial infeed in gear hobbing for different tool/workpiece combinations. Taking into account 20,000 simulations, a nonlinear regression analysis was performed to establish a mathematical equation, describing the maximum chip thickness as a function of the infeed angle and relevant gear, tool and process parameters. The equation was validated using a new set of simulation results. The calculated values are in good agreement with the simulated values. The developed equation allows the design of the radial-axial infeed according to the maximum chip thickness without the use of a simulation program.

New method of tool wear testing on gear hobbing

New method of tool wear testing on gear hobbing

Examination for post-sharpening adjustment of cutting edge of a worm gear hob with circle arched profile in axial section

A necessary condition of intelligent manufacturing is the continuous development of tool geometry. One relevant element of tool geometry is the analysis of the relationship between edge geometry and manufactured surface quality. During the production of worm gear, in order to achieve the required quality, the worm gear hob must be continuously tested for wear and the cutting edge must be re-sharpened, which results in a decrease in the diameter of the hob. Changes in geometric conditions resulted from the increase in distance between the re-sharped hob axis and the gear axis. The subject of this paper is the analysis of manufacturing with a reduced distance between the worm gear and the worm gear hob due to re-sharpening of the cutting edge of the tool and the fit of the drive.

Development of hybrid composite materials for machine tool structures

Several types of machine tools are used in industries to produce a variety of components. As the speed of these machine tools is increased, is developed. The vibrations developed by these machines should be controlled as it would affect the quality of the parts produced and the life of the machine tools. Hence, in this paper a hybrid composite material is proposed to manufacture a gear hobbing machine column with the aim of improving the dynamic characteristics of the machine. Cast iron is made as outer casing and glass fibre reinforced epoxy is made as inner core. A finite element model of the gear hobbing machine column is developed to carry out the investigations. The proposed model is validated by conducting experiments. The results show an improvement in natural frequency of the hybrid column by 40%.