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The spur face gear is a novel drive that is widely applied in national defense. To improve the production efficiency and meshing properties of spur face gears, a hobbing method for spur face gears with a bidirectional modification is proposed. To obtain the tooth surface of the gear by hobbing, the basic worm of the spherical hob and the tooth surface equation by hobbing were established. To obtain a convex surface, modification along the contact trace and contact line was proposed. Then, a hobbing method for face gears with bidirectional modification was studied based on current machine tools. Therefore, a prototype was produced, which illustrates the feasibility of manufacturing the design. Detection methods revealed that the maximum error of an unmodified tooth surface is −11.2 μm, and the maximum deviation errors along the contact trace and contact line are −2.7 μm and −2.1 μm, respectively. The results show that hobbing is an effective method for bidirectional modification of gear teeth.

Optimum process parameters play an important role in improving manufacturing process which have a vital influence on the energy consumption and production cost. Considering the fact that hobbing process is sensitive to process parameters, an integrated multi-objective process parameters optimization method for gear hobbing is proposed to reduce energy consumption and production cost. Thus, this paper firstly analyzes the hobbing process parameters and establishes a description of hobbing process parameters problem. Then a multi-objective optimization model of hobbing process parameters is introduced, with energy consumption and production cost to be optimized. An improved multi-objective ant lion optimizer (IMOALO) is designed to solve multi-objective optimization problem. Finally, a case study is presented in detail to verify the optimization model. The results show that energy consumption and production cost can be optimized simultaneously by determining appropriate process parameters based on proposed method. It has potential in providing favorable support and assistance for technical operators in the practical parametric decision.

Forschung im Ingenieurwesen (2021). doi:10.1007/s10010-021-00531-5, Published by Springer, Berlin

Made available in DSpace on 2022-04-29T08:31:52Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 The grinding of interrupted geometries, such as keyways, holes, lubrication channels and crankshafts, bearings, helical drills, sharpening, and gear hobbing process for pre-toothing, presents technical difficulties such as decrease in surface quality and an increase in costs, when compared to continuous grinding process. This work aims to study further findings about interrupted cylindrical grinding process of AISI 4340 steel, with the application of resinoid aluminum oxide grinding wheel and conventional application of MWF. In a novel and innovative way, this work analyzed the wheel wear cost at 3 different feed rates (0.25, 0.50, and 0.75 mm/min) for continuous workpieces and interrupted workpieces with 2, 6, and 12 grooves. In addition, surface roughness, roundness deviation, grinding power, tangential force, microstructural analysis, diametrical wear, and G ratio were also evaluated. In conclusion to the results obtained, the average values of surface roughness Ra and Rz increased with the severity of the process (feed rate and number of grooves). For feed rates varying from 0.25 to 0.75 mm/min, mean values of 0.51 to 1.10 μm (Ra) and 4.52 to 10.99 μm (Rz) were obtained for the condition with 12 interruptions, indicating an increase in the order of 75 to 114% compared to without interrupted workpiece. The quick wear of the wheel, obtained for an average diametrical wheel wear of 5.79 μm for workpiece with 12 interruptions in 0.75 mm/min, produced higher surface roughness values. Variations of more than 80% in roundness deviation were obtained during grinding at feed rates of 0.75 mm/min due to mechanical shocks during material removal process, being observed in force variations from 50 to 700 N. The cost analysis of grinding wheel wear showed the waste of abrasive material for grinding process with interrupted geometries. For all tests, the tool cost for low feed rate (0.25 mm/min) did not suffer great variations. For feed rates of 0.50 and 0.75 mm/min, the cost of tool wear was higher. The condition with the highest percentage increase in cost was recorded for grinding process with workpiece of 12 grooves and feed rate of 0.75 mm/min, generating an increase of up to 1720% in the tool cost. Converting into monetary values, the wheel cost is of $ 12.62 per 1000-piece batch. Department of Control and Industrial Process Federal Institute of Paraná Jacarezinho Campus Department of Mechanical Engineering São Paulo State University “Júlio de Mesquita Filho” Bauru Campus Department of Mechanical Engineering São Paulo State University “Júlio de Mesquita Filho” Bauru Campus

Greenhouse gas emissions have become one of the most prominent global concerns of sustainable development. To reduce product life cycle carbon footprint, planning should begin at embodiment design phase. The accurate assessment of carbon footprint is the foundation of carbon footprint reduction. However, existing carbon footprint models cannot be applied to embodiment design phase due to incomplete and limited design information. With this in mind, this paper proposes a carbon footprint model for embodiment design based on macro-micro design features. First, a Function-Structure-Feature (FSF) model for embodiment design is established to convey the design information. The concept of design features is introduced (at both macro and micro levels). The macro design feature denotes the different operational states of the product and the constraint relationships between parts. The micro design feature denotes the specific properties of parts. Then, a model of product carbon footprint based on design features is presented through the analysis of the relationships between macro-micro design features and product carbon footprint. The feasibility of the proposed method is demonstrated through a gear hobbing machine. The product carbon footprint model allows quantitative evaluation of product carbon footprint during embodiment design phase, and the amount of carbon footprint from each type of design feature is predicted. Based on evaluation result, the design features can be improved to reduce product carbon footprint. Case study results show that the carbon footprint is decreased by 10.96% after improving design features.

The hob wear and tear of hob adversely affect the stability of dry gear hobbing accuracy. Quick and accurate online recognition of current hob wear state is crucial to curb this type of machining error during the continuous gear hobbing process. For most of CNC machine tools, an in-depth analysis of the spindle power consumption signal is the common way to monitor the tool wear condition. However, the variation of the spindle current signal is not only related to tool wear state but also the thermal-induced error and the different machining allowances of workpieces to be cut. Therefore, it is barely possible to find one or two individual signal features to precisely characterize the change of hob wear state. In this study, to find the features with strong characterization abilities, we proposed a new tool wear experiment strategy to collect the power consumption data and the thermal-induced error in the entire thermal deformation developing period as well as the representative value of different machining allowances of workpieces. Based on collected spindle power consumption data, we indiscriminately extracted both the time-domain and the frequency-domain features and formed a high-dimensional 47*100 feature dataset. Subsequently, some extracted features were excluded by conducting a correlation analysis with the help of collected thermal error data and machining allowance data, and the rest was further compressed to nine new synthetic features with a dimension-reduced treatment by using the deep autoencoder algorithm. Finally, a mapping model between the synthetic features and different hob wear states was developed by adopting the back propagation neural network classification algorithm. To prove the practicability of this mapping model, another gear cutting experiment was conducted and the results showed the accuracy of this hob wear state online monitoring approach can reached to over 90%.

Purpose Machine tools are the equipment used for the cutting and shaping of materials, like metals, which generate greenhouse gas (GHG) emissions across their life cycles due to material use and energy consumption. The life cycle emissions of machine tools are distributed over time and may vary with technology advancement. This paper aims to incorporate these temporal factors into the global warming impact (GWI) assessment of machine tools and further reveal their influences on the results. Method Incorporating emission time into the GWI assessment of machine tools is based on the following dynamic life cycle assessment (LCA) framework. First, compute temporally differentiated GHGs of machine tools based on the activity-based modeling. And then, use time-dependent characterization factors (CFs), which are developed based on the radiative forcing concept, to assess their GWI. By using this framework, a dynamic life cycle GWI assessment of machine tool is conducted using two gear hobbing machines. Both the emission time and the potential changes of life cycle emissions due to the improvement of electricity mix and the variation of machine tool use modes are considered. Results and discussion The results demonstrated that when the emission time was considered, both machines offered 3% of reduction of GWI, compared with their static results, respectively. Further reductions were found for the two machines, when the electricity improvement and the changes of the machine tool use modes were considered. All the differences between the static and the dynamic environmental impact results become smaller with the extension of the time horizons (THs) that accounted for the evaluation. Conclusions and recommendations The conventional static LCA has the potential to overestimate the real GWI of machine tools. It is more important to account for the emission time in GWI assessment at shorter THs or for a longer lifetime of machine tools. This work offers a method to dynamically assess the real GWI of machine tools. The proposed method helps to make robust decision-making for environmentally friendly machine tool selection and support sustainable production.

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.

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.

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.

Manufacturing industry has paid more attention to the carbon footprint in the manufacturing process with an increasing focus on ecological environment. Also, optimum machining parameters are usually considered as an efficient solution for minimizing carbon footprint and processing time owing to their great role in process control. To make a better process parameter set, a novel multi-objective parameter decision approach called multi-objective grey wolf optimizer (MOGWO) is adopted to realize the decision process in gear hobbing. First, the problem of gear production is elaborated in detail and the characteristics of carbon footprint in light of hobbing process are synthetically analyzed; the carbon footprint model and processing time model are established subsequently. Second, a parameter decision approach for multi-objectives is presented followed by thorough optimization approach. Finally, a case study is put into practice for verifying the presented parameter decision-making scheme. The results demonstrate good hobbing process parameter solutions under the proposed decision approach, and it reveals a certain functional relationship between carbon footprint and processing time in view of the graphic display.

A boring process based on electrostatic cooling is proposed, and the effectiveness of the cooling process is verified by simulation and experiment. First, the action mode of active gas in the cutting area is analyzed on the basis of the electrostatic cooling mechanism and metal cutting law. Second, a corona discharge device with an external gas path is designed, and the attenuation law of the length and bending degree of the external gas path on the concentration of active gas in the transmission process is analyzed as well. Third, the precision boring cutter is modified into an internal cooling precision boring cutter with an external gas path. The gas concentration attenuation value at the outlet of the boring head is also obtained. Finally, finite element simulation and experimental analyses are conducted. Results show that the error of main cutting force between simulated and theoretical calculation is less than 2.65%, which verifies the correctness of the simulation. Electrostatic cooling can obviously reduce the feed force and cutting temperature, improve the wear life of tools, and achieve the effect of wet boring compared with dry cutting. The results of this study can be applied to the cooling process modification of cylindrical holes, V-shaped grooves, end faces, and threads during boring. In addition, it can also provide theoretical support for the cooling effect of gear hobbing experiments, welding, and other processes.

Improving the machining accuracy of the gear hobbing machine is effective to enhance the processing efficiency of gear production. Thermal error is the main source of the machining error of the gear hobbing machine, and establishing accurate thermal error prediction model is necessary to reduce the thermal error of gear hobbing machine. In this paper, an accurate thermal error model was proposed based on the improved gray wolf optimizer (IGWO) and adaptive neuro-fuzzy inference system (ANFIS). What’s more, the K-means clustering and gray model GM (0, N) were used to conduct the selection of temperature-sensitivity points to guarantee the robustness and accuracy of the prediction model. ANFIS was used to predict the thermal error on different working condition based on the data collected in the thermal characteristic experiments. IGWO was applied to optimize the parameters of ANFIS. And the combined IGWO-ANFIS was applied to predict the thermal expansion of the hobbing shaft of the gear hobbing machine. By comparing the prediction performance of IGWO-ANFIS and other existing algorithms, it can be concluded that it has better generalization performance than other algorithms.

This paper focuses on parametric analysis, modelling, and parametric optimization of minimum quantity lubrication–assisted hobbing (MQLAH) using an environment-friendly lubricant for manufacturing superior quality spur gears. Influences of hob cutter speed, axial feed, lubricant flow rate, air pressure, and nozzle angle on the deviations in total profile, total lead, total pitch and radial runout, and flank surface roughness parameters were studied by conducting 46 experiments using the Box-Behnken method of response surface methodology. Results revealed that the effect of air pressure is negligible but other parameters have a significant impact on the considered responses. A back propagation neural network (BPNN) model was developed to predict microgeometry deviations and flank surface roughness values of the MQLAH-manufactured spur gears. The BPNN-predicted results are found to be very closely agreeing with the corresponding experimental results with a mean square error as 0.0063. A real-coded genetic algorithm (RCGA) was used for parametric optimization of MQLAH process for simultaneous minimization of microgeometry deviations and flank surface roughness. Standardized values of the optimized parameters were used to conduct confirmation experiments whose results had very good closeness with RCGA-computed and BPNN-predicted values and produced spur gears of superior quality. This study proves MQLAH to be a potential sustainable replacement of conventional flood lubrication–assisted hobbing for manufacturing cylindrical gears of better quality.

It is an increasingly urgent to improve the machining accuracy of the gear hobbing machine. Thermal error is the main source of the machining error of the hobbing machine, and reducing thermal error is necessary to improve the machining accuracy of hobbing machine. In this paper, a novel thermal error prediction model for the hobbing machine was proposed based on the improved gray wolf optimizer (IGWO) and generalized regression neural network (GRNN). The fuzzy cluster grouping and mean impact value (MIV) were firstly combined to select the typical temperature variables and reduce the coupling between temperature variables, so the robustness of the thermal error model can be guaranteed. Then GRNN was used to establish the mapping relationship between temperature variables and thermal error. The IGWO considering the proportion of local optimization and global optimization was applied to optimize the smoothing parameter of GRNN. Finally, the proposed IGWO-GRNN was used to predict the thermal drift of the workpiece shaft of the dry cutting hobbing machine, and its predictive accuracy and generalization performance were compared with four existing algorithms. The results indicate that the prediction accuracy of IGWO-GRNN is at least 5.1% higher than other algorithms and its generalization performance is also promoted.

In order to improve the processing precision and shorten the hob manufacturing cycle of the face gear, a precision generating hobbing method for face gear with the assembly spherical hob is proposed. Firstly, the evolution of the cylindrical gear to spherical hob basic worm is analyzed, then the spherical hob basic worm is designed, thus the basic worm and spiral angle equation of spherical hob are obtained. Secondly, based on the design method of the existing hob, the development method of the assembly spherical hob is analyzed, the cutter tooth and the cutter substrate of the assembly hob are designed, and the whole assembly is finished. Thirdly, based on the need of face gear hobbing, a numerical control machine for gear hobbing is developed, and the equation of the face gear is obtained. Fourth, for reducing the face gear processing errors induced by equivalent installation errors, the error analysis model is established and the impacts of each error on the gear tooth surface are analyzed. Finally, the assembly spherical hob is manufactured and the gear hobbing test is completed. According to the measurement results, the processing parameters of face gear hobbing are modified, and the deviation of tooth surface is significantly reduced.

A novel method for online estimate and compensation of the gear machining error based on the flexible electronic gearbox (EGB) is introduced. EGB is a multi-axis controller, which is used to realize the gear generation process. Gear processing quality is directly dependent on the control precision of EGB. The present work focuses on the estimation, analysis, and compensation of the gear machining error during the gear hobbing process. First, an efficient method for the online evaluation of the helical gear tooth profile deviation, pitch error, and helix deviation is presented, from the perspective of the combination of the multi-axis linkage control structure model and the gear processing technology. Second, the online compensation architecture for the five-axis gear hobbing linkage is proposed, which is composed of an asymmetric cross-coupling controller and a master-slave EGB controller. Simulation results show that the proposed online compensation architecture has better control precision, in comparison to the conventional EGB. Finally, experiments are performed on a six-axis gear hobbing machine, where higher accuracies are obtained for the helical gear, processed by the proposed method.

High-speed dry hobbing (HSDH) has been regarded as an environmentally benign gear machining technique. Current research on energy efficiency of machine tool focuses on the energy efficiency for workpiece material removal but rarely involves the energy consumption required to control thermal stability. Due to the fact that thermal effect dominates the machining accuracy and accuracy consistency, especially in the dry machining process, the energy consumption for thermal stability control should be taken as useful energy consumption. In view of this, by taking the motorized spindle system (MSS) as the study objective, which is a core subsystem of machine tool enabling HSDH and characterizes intensive and inefficient energy consumption, an exergy-based method is proposed to evaluate the comprehensive energy efficiency of MSS. Furthermore, an exergy efficiency optimization model is proposed to maximize total exergy efficiency and minimize average temperature of MSS. A solution method integrating Pareto Dominant-based Multi-objective Simulated Annealing (PDMOSA) and Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS) is proposed to search the best solution of the optimization model. A case study is introduced to validate the proposed model and a final optimal solution is obtained at the total exergy efficiency of 53.5% with balanced temperature of 35.4 °C. The cooling water in MSS is identified to be a dominant factor that affects total exergy destruction. The presented model can give a reference to select appropriate process parameters of MSS for green and precision machining.

The involute gears are widely used in the gearbox for wind turbine. To satisfy the requirements of reduction of noise and increase of the endurance of the gear drive, the new type of involute-helix gears, which have the advantages of involute gears and circular arc gears, had been proposed. In this paper, the hobbing cutters of involute-helix gears are designed utilizing the normal section of convex and concave tooth profiles, respectively. Mathematical models of hobbing cutters are established and general products are also manufactured. Using the developed hobbing cutters, the proposed gear pair is manufactured based on the numerical control technology. Combining with the measure method of gear measuring center and the characteristic of the investigated gears, the method of error measurement of special tooth profiles is provided. The further development of special detection software for the gears will be carried out. The results will lay the foundations for the large-scale industrial practice and production applications of gear drive.

Modern automobiles are equipped with a large number of gear transmissions. In the powertrain, for example, they are used as multi-stage manual transmissions and in the interior for mirror and seat adjustment. Machining manufacturing processes—like gear hobbing and shaping—represent the state of the art in gear manufacturing due to high manufacturing accuracy and flexible machining possibilities. With regard to economic, ecological and technical aspects, cold forging offers a promising production technology. In conventional transverse and forward extrusion processes, the ejection process is a challenge due to high forces and a negative influence on the gearing accuracy. One variant of forward extrusion is the so-called “Samanta” process, whereby an additional ejection step is avoidable by sequential forming of the gears. For the establishment of this process in industry, the increase of the achievable gearing accuracy and the achievable tool life is of major relevance. Thus, a deeper process understanding as well as knowledge about the influences on the process results is beneficial. In this context, the aim of this research work is to produce spur and helical gears by the “Samanta”-process. In addition to the resulting component properties, the characteristics of the process are determined. On this basis, commonalities and differences in cold forging of spur and helical gears are investigated to gain a principle process understanding. These findings are also used to derive possible approaches for improving component and process properties in future research work. The results reveal that cold forging of spur and helical gears by the “Samanta”-process results in characteristic properties and specific challenges within the gear types. Furthermore, a geometric adaptation in the gearing area of the used die promises enormous potential for a positive influence on the process results for both types of gearing.

A force-thermal coupling error model was proposed to investigate the coupling effect of thermal error and force-induced error on the machining accuracy. Based on the developed model, numerical simulations were conducted to unveil the temperature distributions of both the assembly and the worktable, as well as corresponding displacement errors. Besides, based on the two-degrees-of-freedom hobbing locus motion, an equation to characterize the meshing relation between the hob and the workpiece was deduced with the consideration of the force-thermal coupling errors. By using this equation, an interface, which can visualize the gear hobbing process and map the force-thermal coupling error to the resultant gear profile, was developed in the MATLAB. Further comparison between experimental data and simulation results prove the credibility of our proposed method.

This paper reports on study of laser texturing assisted abrasive flow finishing (LT-AFF) process of the hobbed spur gear (HSG) to further improve their microgeometry, surface finish, micohardness, microstructure, wear resistance, and material removal rate (MRR) as compared to AFF process. Preliminary experiments were conducted to identify optimum values of power and focal length of the continuous fiber laser of 1064-nm wavelength, and number of passes for laser texturing of the HSG made of 20MnCr5 alloy steel. Identified optimum values were used to produce homothetic texture on flank surfaces of HSG in a direction perpendicular to the lay profile generated by hobbing. Influence of laser texturing was studied by comparing deviations in microgeometry, average and maximum surface roughness values, microhardness, wear resistance, and MRR of HSG directly finished by AFF and laser-textured hobbed spur gears (LTHSG) finished by AFF. Previously optimized value of viscosity of AFF medium was used during finishing of HSG and LTHSG. Deviations in total profile, total lead and total pitch, and radial runout were used to indicate deviation in microgeometry of the spur gears. Results reveal that AFF of LTHSG reduced surface roughness and errors in microgeometry and improved microhardness, microstructure, wear resistance, and MRR than direct of AFF of HSG. Improved microgeometry and surface quality of spur gears will lead to their increased operating performance and service life thus reducing their running noise and vibrations and preventing their premature and failures. This research proves that productivity of AFF process can be improved significantly by laser texturing for finishing the spur gears.

The manufacturing of case-hardened gears usually consists of several complex and expensive steps to ensure high load carrying capacity. The load carrying capacity for the main fatigue failure modes pitting and tooth root breakage can be increased significantly by increasing the near surface compressive residual stresses. In earlier publications, different shear cutting techniques, the near-net-shape-blanking processes (NNSBP’s), were investigated regarding a favorable residual stress state. The influence of the process parameters on the amount of clean cut, surface roughness, hardness and residual stresses was investigated. Furthermore, fatigue bending tests were carried out using C-shaped specimens. This paper reports about involute gears that are manufactured by fineblanking. This NNSBP was identified as suitable based on the previous research, because it led to a high amount of clean cut and favorable residual stresses. For the fineblanked gears of S355MC (1.0976), the die edge radii were varied and the effects on the cut surface geometry, hardness distribution, surface roughness and residual stresses are investigated. The accuracy of blanking the gear geometry is measured, and the tooth root bending strength is determined in a pulsating test rig according to standardized testing methods. It is shown that it is possible to manufacture gears by fineblanking with a high precision comparable to gear hobbing. Additionally, the cut surface properties lead to an increased tooth root bending strength.

The design flexibility and transmission stability of the non-circular gears can be improved using the helical tooth scheme. Herein, a linkage model was derived for hobbing the helical non-circular gears based on the influence of the axial feed motion of the hob on the motion of the projecting rack on the gear-blank end face. This axial feed motion produces additional motion effects on the rotary axis of the gear-blank and the moving axis of the hob. Further, the accuracy of the linkage model was verified by kinematic simulations. The global convergence characteristics of the transcendental equation used for obtaining the polar angle of the pitch curve were ascertained to derive the interpolation calculation process for the linkage model-based electronic gearbox. The cause of cumulative error during the multi-turn hobbing process of the gear blank was analyzed. The error accumulation was effectively controlled by optimizing the interpolation algorithm. The hobbing experiments and meshing transmission test were conducted using the self-developed non-circular gear hobbing system to verify the effectiveness of the linkage model and interpolation algorithm.

Dry hobbing machine is a kind of specialized machine tool to manufacture high-quality small-module gears. With the development of shipbuilding, automobile, and wind power industries, higher request on the dry gear hobbing accuracy is called. The thermal-induced error, which accounts for 50–70% of machining error, needs to be reduced more efficiently. Hence, in-depth investigation on the thermal-structural characteristics of the dry hobbing machine is indispensable. In this paper, thermal-structural coupling numerical simulations were conducted to investigate the heat transfer process as well as thermal-induced deformation of the worktable of a YDE3120CNC gear hobbing machine in the large-volume gear production. New equations were used here to describe the convection heat transfer happened on the worktable assembly. Verifying test results proved the feasibility of the newly proposed numerical method. Following numerical simulation, results indicated that during the gear hobbing process, the highest surface temperature of the worktable assembly appeared at the top surface of the fixture, and corresponding thermal-induced deformation reached the maximum of 49.9 μm. Besides, in a large-volume production, the worktable assembly would attain thermal equilibrium in 15,000 s. Hence, the assumption widely used in previous studies on gear hobbing machines that the worktable assembly could be treated as an ideal solid without any thermal deformation is wrong. The thermal-induced deformation of the worktable assembly should be considered in the subsequent development of the thermal error compensation system.

This article presents the results of research on the influence of the condition of surface layers obtained after grinding a hob cutter tool face using the hybrid combined minimum quantity lubrication (MQL) and compressed cold air (CCA) (MQL-CCA) method of delivering the coolant, on its wear. In the MQL-CCA method, the minimum quantity of the lubricating (MQL) and the cooling agents, in the form of cooled compressed (CCA), were introduced into the grinding zone, simultaneously. For comparison, the tests were also conducted by separately applying the coolant with the WET method with MQL, as well as CCA. In the scope of the described grinding conditions, applying the MQL-CCA method does not cause considerable microhardness of the surface layer as compared to “wet” machining (WET). In addition, the surface roughness obtained through the application of the MQL-CCA method and expressed with 2D and 3D parameters is comparable to that obtained in grinding with the WET method. SEM images for the MQL-CCA and WET methods did not reveal any typical defects in the form of burrs or chippings, occurring on the edges of the ground surfaces. The wear curves corresponding to hobs ground with the application of the coolant using the WET method and using the MQL-CCA method show similar courses, which is indicative of a lack of influence of the surface layer condition resulting from providing coolant during grinding onto intensity of the tool wear process in tangential hobbing. This conclusion is also confirmed by the similar number of teeth affected by measurable wear signs. Therefore, using the hybrid MQL-CCA method, which decreases the amount of coolant provided during the grinding of hob cutters, may constitute, in the described scope of the grinding conditions, an alternative to grinding processes carried out most often with the WET method which entails considerable expenditures for purchasing, maintaining, and utilization of the coolant.

Based on the homogeneous coordinate transformation method and finite element simulation method of a whole hobbing machine, this paper proposes a highly applicable force-induced error model for a high-speed dry hobbing machine. The number of parameters needed in the error estimation can be effectively reduced by using this model, which has great value in industry application. According to the engagement features of the conjugating surface and the principle of two-degrees-of-freedom hobbing, the meshing equation was deduced and the force-induced errors of the system were mapped to the normal errors of gear surface. In this way, the mapping relationship between different kinds of force-induced errors and hobbing precision can be obtained. Then, the theoretical tooth surface and the real tooth surface under the influence of force-induced errors were respectively calculated by using a numerical simulation method. Based on numerical simulation results, the mathematical relationship between the tooth profile root-mean-square error (RMSE) and the compensation variables was obtained, as well as the optimal value of the compensation variables. The following experimental results validate the feasibility and accuracy of this error compensation method. This study has an important guiding role in the improvement of dry gear hobbing precision.

Power-skiving is an efficient method for the manufacturing of high accuracy gears, particularly internal gears. However, dedicated power-skiving machine tools are normally very expensive. Accordingly, the present study proposes a simple methodology for automatically and systematically generating the NC code required to manufacture gears using the power-skiving method on a conventional six-axis CNC turn-mill machining center. The main factors determining the linear cutting speed of the power-skiving tool are analyzed and a modified D-H notation is used to evaluate the error characteristics of the machine tool system. The validity of the proposed methodology is demonstrated by machining an internal gear on a DMG MORI NTX1000 six-axis CNC turn-mill machining center. The results confirm that the proposed approach provides a simple yet effective method for implementing the power-skiving technique on a conventional six-axis CNC turn-mill machining center.

Two main popular cutting methods for bevel gear mass production, face milling and face hobbing, both require dedicated tools and machines available from only a few machine tool companies, which makes production more costly. This paper thus proposes a cheaper, more flexible alternative for producing small or medium batches of large bevel gears, a disk tool cutting method using a five-axis machine. In this method, the machine coordinates are derived based on tooth surfaces. The target’s topographic points of tooth surface are applied to construct a fitted surface which is then used to further refine the cutter-contact points to improve the precision of the gear produced. At the same time, mathematical models are established for both the tool and the machine. A coordinate transformation matrix is then generated by aligning the coordinate system of the tool’s reference point with each of the work gear cutter-contact points. Because different machines employ identical transformation matrices for producing the same workpiece, the machine’s five-axis coordinates can be derived using inverse kinematics. These coordinates are the resource to generate the NC machining data that allows NC verification software to perform cutting simulations. The simulation results verify the correctness of the mathematical models.

Low carbon gear hobbing is an environmentally friendly way to machine massive workpieces. The appropriate process parameter decision-making is of great significance to improve processing quality, reduce the machining time, production cost, and carbon emission in gear manufacturing. This paper first proposes a support vector machine/ant lion optimizer/gear hobbing (SVM/ALO/GH) integrated approach to do the multi-objective optimization of machining parameters for solving small sample problem of batch production. The first population of process parameters is generated by the multi-class SVM method. Pareto improvement and ALO algorithm are employed to obtain the optimal process parameters. Finally, the case study is presented to give a clear picture of the application of the optimization approach. The results uncover that the proposed SVM/ALO/GH method has better performance than the improved back propagation neural network/differential evolution (IBPNN/DE) algorithm over the small sample problem.

Current non-circular gear manufacturing process all use discrete machining method, which generates a large number of small line segments, brings in approximate error, and leads to high-order discontinuity of the whole tool path curve. The tooth surface quality will be decreased significantly due to the vibration of machining tool caused by the discontinuity of machine tool motion trajectory especially in high-speed machining. In addition, the processing speed and efficiency will be limited due to the unsmooth kinematics profile. This paper proposes a non-circular gear completely continuous generating machining interpolation method. The non-circular gear machining electronic gearbox is developed to compute the real-time interpolation point data to guarantee the strict generating motion relationship between the workpiece spindle, feed shaft, and cutter spindle during the gear manufacturing process according to the derived linkage model for non-circular gear hobbing and generate continuous smooth machining trajectory. The proposed interpolation method is implemented on the self-developed CNC system based on the embedded CNC system hardware structure platform and verified experimentally on a gear hobbing machine. A pair of two-order oval non-circular gear is machined, and the roll and application experiments on a kind of gear pumps are done to illustrate the machining performance and provide a high efficiency and precision machining method for non-circular gear.

Cutting tools are extensively used in subtractive manufacturing systems for achieving the desired products. The advancement of cutting tool technology promotes the development of efficient and green machining. However, as one of the auxiliary consumptive materials, cutting tools have a crucial impact on the sustainability of machining systems. Thus, the development of sustainability urgently requires characterizing the impacts of cutting tools. This paper investigates the environmental impacts of cutting tools from the life cycle aspect based on the material and energy consumption during the material extraction, manufacturing, use, and recycling of cutting tools. Carbon emission is applied to measure the impacts of cutting tools on the sustainability of the machining system. Then a life cycle carbon efficiency approach based on the service life of the cutting tool is presented to characterize the life cycle carbon emission characteristics of cutting tools. A gear hobbing system with five kinds of hobs was introduced as a case study to characterize the life cycle carbon emissions and efficiency of hobs. It indicates that substrate and coating material type, manufacturing process, coating method, and regrinding times all influence the carbon emissions and efficiency of hobs to different extents. A high-speed dry-cutting hob exhibits excellent carbon efficiency in addition to carbon emissions.

High-speed dry hobbing is an emerging green technique for gear hobbing owing to its high productivity and environmental friendliness. However, severe friction occurs and heat instantaneously accumulates in the cutting region due to the adoption of high cutting speed and the lack of metalworking fluids, which causes serious hob wear. This investigation is concerned with the thermal performance and heat-loading quantitative method of hob cutter in high-speed dry hobbing. According to the multiple-edge and intermittent cutting behavior, the temporal dynamic characteristics of hob cutter are analyzed. Based on the complex 3D chip geometry produced in high-speed dry hobbing, the calculation method for heat-production (the amount of generated cutting heat) of hob tooth is proposed. Considering the coupling effects of heat conduction, heat convection, and heat radiation, the prediction model for heat-loading quantity (the bearing capacity of cutting heat) of hob tooth is developed. With the help of the developed method, the interrelation between the thermal performance of hob cutter and material removal volume, cutting conditions, coating material, respectively, are investigated. The predictive method developed in this work provides a methodology for process parameter optimization and hob cutter manufacturing of high-speed dry hobbing.

Gear hobbing technology is one of the most widely used forming processes of gear teeth. And the development of dry hobbing technology provides a solution for realizing productive, economical, and ecological gear production. Since there is no cutting oil for cooling and lubrication in dry hobbing process, the hob tool life, thermal deformation errors of machine tool, and quality of workpiece are sensitive to the cutting parameters, especially the cutting speed and tip chip thickness. Considering this situation, a dry hobbing parameters optimization model with the hobbing efficiency as our objective, and the hobbing cost per piece, gear quality, tact time as constraints was established, in which the cutting speed and tip chip thickness were considered as optimal variables and the material of workpiece, coating of hob, and feed rate were considered comprehensively. An iterative test method is proposed to solve this model. And for the application in automated production line, an online adaptive application system was also developed based on SINUMERIK 840D NC system. The parameters of five different kinds of material gear were optimized by applying this model and system, and the result showed the model and the system were practical.

Failure Mode, Effects and Criticality Analysis (FMECA) of remanufactured machine tools is one of the key issues for improving their quality and reliability. According to the characteristics of remanufactured machine tools and the basic principle of FMECA, the method of failure mode analysis for remanufactured machine tools in service is proposed, with the FMECA procedure being expounded in detail. In addition, failure criticality of each failure mode for remanufactured machine tools is analyzed by risk priority number (RPN) method. Finally, the method is demonstrated in a case study of remanufactured YX3120 gear hobbing machine. The results from the case study are discussed and conclusions are drawn.

High-speed dry hobbing is the dominating green technique for gear hobbing owing to its high productivity and environmental friendliness. However, a large amount of cutting heat is generated during the machining process due to the absence of metalworking fluids and the adoption of high cutting speed. A better understanding of chip heat-carrying capacity for high-speed dry hobbing is quite necessary when aiming to reduce the influence of cutting heat on machining precision. In this paper, an analytical model is established to quantitatively determine the chip heat-carrying capacity of high-speed dry hobbing. According to the progressive heat transfer characteristic of high-speed dry hobbing, cutting heat generation and transmission are analyzed. 3D chip geometry is numerically calculated by modeling the complex hob geometry and the interrelated kinematic relations of high-speed dry hobbing. Based on the 3D chip geometry and the specific cutting energy, chip heat-carrying capacity model is developed considering three heat sources. In this model, chip heat partition is experimentally determined by calorimetric method. With the help of the developed model, chip heat-carrying quantity and chip heat-carrying efficiency are discussed by investigating their influence factors (hob rotation speed, axial feed, feed method, chip removal time and hob geometry).

TC4 thin-walled parts with curved surface are widely used in industrial applications, and the surface quality is a basic requirement to ensure the functional performance. Because of the low rigidity of thin-walled parts, the cutting vibration problem is commonly encountered which results in a bad surface quality. Meanwhile, the rigidity of the workpiece is continuously changing along with the cutting process which is sensitive to the thin-walled parts and induces a more complex cutting vibration. With the extensive usage of high-speed milling which has the advantage of small milling force, the cutting vibration becomes more severe due to the high frequency excitation for the thin-walled parts. To avoid the serious cutting vibration as well as improve the machining quality for high-speed milling of TC4 thin-walled parts with curved surface, the appropriate cutting parameters which influence cutting vibration directly are needed to be determined. Taking high-speed flank milling of TC4 arc-shaped thin-walled parts as an example, the stable domain is worked out on considering the rigidity variation of thin-walled parts. Meanwhile, the limit stable axial cutting depth is obtained based on the stability domain which is established by considering the rigidity variation of workpiece along with the machining process. The results show that the limit stable axial cutting depth is 9.69 mm, and the stable cutting process as well as the high-efficiency machining can be guaranteed simultaneously when selecting the axial cutting depth that is slightly smaller than the limit value. This research puts forward an effective approach for recognizing the stable axial cutting depth range for high-speed milling of thin-walled parts with curved surface. In addition, it provides guidance for superior-quality and high-efficiency machining process.

This paper presents two new methods and associated algorithms for numerically modeling chip geometry and calculating cutting force for parts with free-form surface during 5-axis CNC machining using a flat-end mill. Extending the computational geometry-based Alpha shape method that can only predict cutting chip geometry, a parallel slice local volume modeling approach has been added to predict cutting forces as well. To demonstrate the validity and capability of these new methods, simulation of the cutting and chip forming process of 5-axis CNC machining on a free-form surface has been carried out. Physical validation experiment in controlled conditions has been carried out on a 3-axis micro CNC machine with the two cutter rotation angles set to be zero. The predicted and measured cutting forces are in reasonably good agreement both in trend and magnitude. The presented chip volume and cutting force method can be used to perform cutting force estimation for generating optimal tool path and orientation during 5-axis milling. The method requires longer computational time than traditional analytical methods, but it supports the ultimate goal of chip volume modeling and calculation—accurate dynamics cutting force prediction.

Strict position and speed synchronization constraint relationship of the gear machine tools axes make gear machining different from the traditional multiaxis machining process. This paper proposes a reconfigurable embedded CNC system platform used for gear machine tools based on the “ARM + DSP” hardware structure. The parametric automatic programming module is introduced to generate NC machining program according to the gear parameters, cutting tool parameters, and machining process parameters automatically. The gear machining process-database module is used to support the previous function modules to optimize the cutting process cycle parameters through intelligent expert knowledge base. In order to meet the requirements of multiaxis synchronous movement, a new type of software electronic gearbox is put forward, and the implementation method of it in the CNC is also studied. To reconfigure the electronic gearbox (EGB) function and simple parameters setting, the embedded gear machining CNC system can be applied to different gear cutting machine tools, such as gear milling, shaping, hobbing, scudding, grinding, and honing machine. Finally, the proposed embedded CNC system is verified experimentally on a six-axis gear hobbing machine. All of the tracking error, contour error, and final measured results show that the proposed CNC system which has these core gear machining function modules can work effectively.

In the recent years, utilization of non-standard gears have increased due to the improved load carrying capacity and improved durability. However, these non standard gears are manufactured by non-conventional manufacturing process including wire cut electric discharging machining (WEDM). Standard gears are manufactured by conventional machining process, hobbing. This work reported the performance of injection molded polymer gear paired with a steel gear manufactured by conventional hobbing and wire cut electric discharge machining process. WEDM steel gear surface exhibited poor surface topology and higher hardness when compared to that of conventional machined steel gear. Measured net surface temperature of polymer gear at various loads (2-3 Nm) confirmed a temperature rise of 10-15°C when paired with WEDM gear compared to that of hobbed and ground gear. Polymer gear paired with WEDM exhibited inferior fatigue performance when compared to the polymer gear paired with hobbed and ground gear.

As the machining process is remarkably influenced by the cutting force, its prediction is of great significance. For most of the commonly used cutting force prediction models, they are no longer applicable once the workpiece material is altered. Consequently, a new ball-end milling force prediction method with the consideration of the workpiece material properties is presented in this study for difficult-to-machine materials in high-speed milling. Based on differential and oblique cutting mechanisms, the metal cutting process in this method is taken as the linear superposition of a series of differential oblique cutting processes. With laboring the force-loading status of rake face, the yield strength, the heat conductivity, and the plasticity of the material which are the most important factors to influence cutting force are involved as the input elements. Furthermore, the interaction between material properties and machining conditions is also introduced to broadened the scope of applications of this method. In accordance with specific needs, an inverse method using the average cutting force of a single disk after cutting edge discretization is proposed to obtain the specific coefficients, and the cutter engagement is determined by Z-map method. Finally, the comparison between the simulated result and the experimental result confirms the effectiveness of the presented method for different difficult-to-machine materials in high-speed milling based on slot milling and curved surface milling.

Typical processes of producing transmission gears involve hobbing, milling, or shaping of a forged stock to obtain the desired gear geometries. Among all these machining processes, gear hobbing is an efficient method of manufacturing high-quality gears. In this paper, a three-dimensional (3D) finite element model is presented to simulate the gear hobbing processes. The model is used to simulate the complicated kinematic motion between the hobbing tools and the gear workpieces and to perform a coupled thermo-mechanical analysis on the tools and the workpieces during the chip removal process. Cutting forces, torques, and temperature and stress distributions of the hobbing tools and workpieces are predicted using the proposed model. The tool wear progression in gear hobbing is analyzed in terms of tool geometry by a combined experimental-analytical method. The model considers the complex geometry of hob tooth profiles with multiple teeth engagement and can provide more insights into complicated gear hobbing processes. Based on the simulations with various tool geometries, a new tool geometry is arrived, which reduces spindle torque and shows a significant tool wear reduction. The modeling results are further validated through a direct comparison between the predicted and measured chip shape, torque, and tool wear rate.

Dry hobbing is a new gear tooth forming process, which would replace the traditional wet hobbing process due to its high efficiency and environmental friendliness. However, due to the absence of metal cutting fluid in dry hobbing, temperature of hobbed workpiece is relatively high, which will lead to the increase of dimension errors, especially tooth thickness errors. In this paper, the features of workpiece thermal deformation errors are identified and an error compensation model is developed to compensate both machine tool thermal errors and tooth thickness errors which is induced by workpiece thermal deformation. In this model, the representative temperature variables of a dry hobbing machine tool are obtained based on experimental data with fuzzy clustering method. A compensation model is proposed to map the relationship between representative temperature variables and compensation value. In a series of experiments, the tooth thickness errors ranged from −22 to −59 μm. After implementing compensation of machine tool thermal errors, the tooth thickness errors ranged from −18 to −28 μm, which demonstrates that workpiece thermal deformation has a great influence on tooth thickness errors in dry hobbing. Furtherly, the tooth thickness errors ranged from −4 to 8 μm, after implementing compensation of both machine tool thermal errors and workpiece thermal deformation errors, which demonstrate that the compensation model is effective.

High-speed gear hobbing, especially dry hobbing, is an economical and environmentally friendly way to machine massive workpieces. Considering the fact that high-speed gear hobber are more expensive than the conventional machine tool and the same is true of the corresponding cutters, it is requisite that process parameters are decided appropriately so as to obtain the ideal comprehensive machining effect. What is more, the classical decision-making method of process parameters scarcely consider parameters’ adjustment in the hobbing process of a batch of gears. To resolve the problems, this work presents a hybrid improved back propagation neural network/differential evolution (IBPNN/DE) approach to do a continuous optimization decision making of process parameters in high-speed gear hobbing. Gear hobbing effect evaluation model is first structured to assess the machining effect. The optimization population of the process parameters, which is the input of the DE module, is generated by the IBPNN module. Using the DE algorithm, the best of best process parameters are obtained during the course of working. Finally, a test case is presented to give a clear picture of the application of the optimization approach. The results reveal that the proposed IBPNN/DE method has better performance than the general decision-making method under the comprehensive evaluation criterion over a batch of workpieces.

A new model of the gear hobbing process is presented in this paper to determine the micro tooth surface topography in spur and helical gears. The model is based on the generation of tooth surfaces by successive cuts of the finite number of hob teeth, as they come into interaction with the gear tooth. For every relative position of the hob and the gear, an analysis is carried out to find the active cutting edge points which generate the gear tooth surface. Repeating this procedure for all the selected gear tooth surface points, the micro topography of the gear tooth surface is determined. Therefore, the developed method determines the deviations of the manufactured tooth surface from the theoretical tooth surface of spur and helical gears. The method is applied to improve the micro tooth surface topography of spur and helical gears, namely to reduce the deviations of the hobbed tooth surface. Spur and helical gear examples are used to demonstrate the effectiveness of the method.

The high-speed milling technology for difficult-to-machine material has come to be a hotspot in the industry field. Regarding the high-speed milling of Inconel 718 parts with curved surface, the variation of the geometric features of the curved surface will lead to the sharp fluctuation of the cutting force as well as the vibration of machine tool. In this way, it not only makes a severe impact on the surface machining quality and the tool life but also greatly affects the efficiency of the high-speed milling. For the milling force is an important physical concept to comprehensively reflect the milling process and the cutting speed is one of the main factors that affect the cutting force when the cutting volume is small in the high-speed milling of Inconel 718 parts with curved surface, a new cutting force modeling method is proposed based on the geometric characteristics of the curved surface and the spindle speed, which can be used to predict the cutting force fluctuation under different cutting speed. The experimental results show that the proposed cutting force model can effectively forecast the amplitude and the change trend of the cutting force. The achievements of this study will provide the theoretical basis for the optimization of machining parameters in high-speed milling of curved surface with difficult-to-machine material and enrich the processing and manufacturing theory for parts with curved surface.

In this research, mathematical model of an accurate cutting system for the face milling of hypoid gear is developed. For the machining of hypoid gear, a conventional cutter system is considered, which contains the groups of inside and outside blades. These blade models are called accurate due to the consideration of rake, hook, relief and pressure angles in the design. Mathematical representation of the tooth cutting edges of the blades is developed. By assembling the blade models in the cutting system and revolving it around the cutter rotation axis, swept surface and profiles of inside and outside blades are generated. Using the local synthesis method, determination of cutting system geometry is performed for the given parameters of the gear. In cutting system parameters, average cutter radius and pressure angles of the tooth cutting edges are the variables in this algorithm, whereas the hook, rake and relief angles are assumed to be given. Sensitivity analysis is performed, to analyse the sensitivity of the cutting surface, with respect the blade’s rake and hook angle. Finally, an application is presented for a standard gear geometry. A cutter system is determined for the gear and it is virtually face milled, and the geometric parameters are validated against the given parameters of the gear.

A force model is presented to predict cutting forces in end milling. The helical end mill geometry model is established containing the cutting velocities and the cutting forces during the cutting process, which considers the pitch angle and the helix angle of each flute. The helical flutes are decomposed into a set of infinitesimal oblique cutting edges. At every oblique cutting element, the geometric angles and the cutting velocities are expressed in the three-dimensional plane and the differential cutting forces are determined by cutting energy in the effective cutting plane. By integrating the differential cutting forces along each cutting edge, which considers the effect of the feed velocity, the milling forces can be predicted. The proposed model has been verified with various cutting tests using regular end mill and variable helix end mill. The predicted and measured cutting forces are compared to illustrate the viability of the proposed cutting force model in end milling.

A calculation method for cutting force in gear slicing is proposed based on energy method. In view of the particularity of gear slicing, the cutting edge on a cutter tooth is divided into three sections: cut-in edge, top edge, and cut-out edge. Then, the concept of micro-section edge is proposed for the three curved edges. The cutting force on micro-section edge is calculated according to oblique cutting theory. The vector sum of cutting forces on micro-section edges make up the cutting force on one section of the cutting edge. Then, the cutting force on single cutter tooth is obtained by the vector sum of cutting forces on the three sections of the cutting edge. For the calculation of whole cutting force acting upon the cutter, the number of cutter teeth participating in cutting is determined based on coincidence degree. As a result, the whole cutting force is obtained by the vector sum of cutting forces on the cutter teeth participating in cutting. The feasibility of this cutting force calculation method is proved by a calculation example and the effectiveness of this method is verified by finite element simulation.

Compared with the conventional machining (CM), rotary ultrasonic machining (RUM) is generally believed to have many better cutting features, such as low cutting force, high material removal rate, and long tool life. But this paper presents an interesting observation on ultrasonic vibration-assisted drilling and milling with spiral cutting tools (SCT). Due to the special spiral structure of tools, ultrasonic vibration at the end of tool has more complex characters which may generate a negative effect on machining. In this paper, several RUM experiments were conducted, and we found that the cutting force is not always reduced when using SCT. Through the finite element method (FEM), we found that two kinds of vibrational direction are shown at the end of the SCT when the vibrational frequency changes. One is along the first and third quadrants, and the other is along the second and fourth quadrants. Since the vibrational direction affects the actual cutting angle and interference may occur, this change will greatly affect the cutting force and the surface quality. Therefore, a kinematics model of the vibration on the cutting edge will be proposed to understand the cutting mechanism of RUM deeply.