Search

Tool condition monitoring is vital to maintain the quality of workpieces during machining. Recently, data-driven methods based on multi-sensory data have been applied to tool condition monitoring. The quality of extracted features is a key to realizing a successful data-driven tool condition monitoring. However, the extracted features in the previous study are focused on the multi-collinearity of multi-sensory data which is incapable of identifying the informative and discriminative information in the long time period aspect. This paper proposed a novel method for tool condition monitoring using deep spatial-temporal feature extraction and lightweight feature fusion techniques. A key to the proposed method is the extraction of multi-collinearity as spatial features, and the capture of long-range dependencies and nonlinear dynamics as temporal features, to fully characterize tool wear change using multi-sensory data. Then, a lightweight feature fusion method is used to fuse spatial features, temporal features and statistical features for further removing redundant information employing the Kernel- Principal Component Analysis (KPCA). Finally, support vector machines (SVM) is used to predict the tool conditions using the fusion feature. Experiments on a milling machine and a gear hobbing machine are carried out to verify the effectiveness and generalization of the proposed method respectively.

Conventional Semi-supervised learning (SSL) approaches are limited by the reliance on assumptions of samples (manifold assumption, cluster assumption and smoothness assumption) and may fail to reach their full potential when it comes to industrial scenarios with complicated data characteristics. In this paper, a semi-supervised process quality evaluation framework is proposed based on conditional variational auto-encoder. Label inference process is utilized to handle unlabeled samples during training stage and to directly predict the label during the evaluation stage. Real NVP (real-valued non-volume preserving) is introduced to optimize the posterior distribution in order to increase flexibility. The proposed framework does not rely on assumptions of data distributions and it learns the hidden distributions from samples. Gear hobbing simulation and experiment investigations are conducted to verify the effectiveness of the proposed framework. The results indicate that the proposed method can achieve remarkable classification performance compared with other state-of-the-art approaches.

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.

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.

A method of machining the circular teeth for a cylindrical gear pair with localized tooth contact is described. Formulas are presented for the parameters of the hobbing tool.

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.

Hobbing is one of the crucial gear manufacturing processes with high precision and high efficiency. In order to improve the accuracy of hobbing and hob relief grinding, more precise grinding wheels are demanded for relief grinding in the production of gear hobs. In this paper, a new and accurate mathematical method for calculating the axial profile of grinding wheel based on the corresponding gear hob to be ground is proposed. Considering that most researches have been conducted to focus on the optimization of hob geometric feature and only can be applied to a specific type of hand of helix and rake angle of gear hobs. The method in this paper can be served as a general algorithm for generating axial profile of grinding wheels on the basis of the spatial envelope theory, the principle of coordinate system transformation, and studies on normal profile of single-tooth. The accuracy of grinding hobs is based on applying the approach in practical manufacturing. Taking two typical different kinds of pre-grinding gear hobs as examples for calculation and experiment, the results of tooth profile errors strengthen the position that the validity and feasibility of this method, it can be employed for gear hob design and grinding processing.

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.

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...

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.

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.

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%.

Conventional gear hobbing includes production and quality inspection. The inspection procedure is generally carried out using precision measuring centers and it is time-consuming. In order to improve efficiency, in this paper a gear hobbing quality in-situ evaluation framework is proposed based on vibration monitoring with dependencies attention. Firstly, the gear hobbing vibration sequence is divided into groups of vibration segments. Features are extracted and the dependencies pattern between gear deviations and vibration segments is assessed by utilizing sequentially connected NN (neural network) cells. The pattern can then be applied on in-situ gear deviation evaluation for unlabeled gear hobbing vibration sequence. Gear hobbing experiment is conducted using a high-speed dry cutting gear hobbing machine YE3120CNC to validate the effectiveness of the proposed framework. The results indicate that the proposed method is effective in the evaluation of the gear profile deviation and the gear lead deviation. The influence of feature selection and vibration direction have also been 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.

Gear hobbing is one of the most common soft-machining processes for pre-toothing. The process kinematics result in a characteristic tooth-flank topography, which is mainly determined by so-called feed marks. For an economical finishing process by gear grinding in automotive applications, the feed-mark depths should not exceed a maximum value of 35 μm. In the present study the validity of this limit has been investigated in view of the development of increasingly powerful grinding machines and grinding wheels. For this purpose, gears with feed-mark depths δx below and above 35 μm were machined and ground by means of discontinuous profile gear grinding afterwards. The influence of the feed marks on the grinding process with roughing parameters was systematically evaluated on the basis of various process variables such as the increase in spindle power Ps or the degree of grinding-wheel clogging Zs, while the resulting gear quality was mainly analyzed by various parameters to describe macro- and micro-geometry deviations of the ground tooth flanks. With increasing feed-mark depth, an increase in spindle power was found due to the additional machined volume. An influence of increasing feed-mark depths on the clogging degree, the grinding-wheel wear and the gear quality could not be proven. Therefore, economical finishing of gears by gear grinding is also possible with feed-mark depths of more than 35 μm. A new definition of this limit should be sought.

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.

Gears are highly loaded components in many different application areas such as automotive, aircraft turbines, ships or wind energy systems. The demand on gears can be summarized by ever rising load capacity on the one hand side and significant noise reduction on the other hand. Both demands can only be fulfilled by adapted gear finishing processes generating the best possible macro- and micro geometry as well as the desired surface integrity state. The paper presents research work on analyzing the thermal load on gears during gear hobbing, surface integrity states by different hard gear finishing operations and possibilities to avoid thermal damage in gear manufacturing by adapted process monitoring and fast non-destructive analyzing techniques.

Purpose The purpose of this study is to optimize high-strength gears produced by powder metallurgical process and to provide a material model to predict the tooth root bending fatigue strength. Powder metal (PM) technology offers great opportunities for the reduction of the carbon footprint and improvement of the cost efficiency of gear production. PM gears can achieve flank load-carrying capacities comparable to wrought steel gears if the loaded volume is fully densified. Still, the tooth root strength is of particular importance. Design/methodology/approach The tooth root stresses can be minimized by optimizing the tooth root geometry. This usually leads to a target conflict, as fully optimized tooth root geometries cannot be manufactured by generating processes such as hobbing, generating-grinding or rolling. To use the increase in tooth root load-carrying capacity of fully optimized root geometry on PM gears, a non-generating method for surface densifying is needed. The shot-peening process is used as an alternative densification process for PM gears. The properties of both shot peened and cold-rolled PM gears are analyzed and compared. To quantify the effect of both manufacturing processes, the tooth root bending fatigue strength will be evaluated and compared to wrought gears. Findings From the fatigue strength perspective, a material model is developed, which is able to predict local endurable stress amplitudes. The model is gained through regression varying carbon content, density and size effect on bending specimens. Originality/value It is transferable to PM gears of the same material using a load transfer coefficient.

This article analyses the process of occurrence of the surface roughness during the ultrasonic hobbing of the fine pitch gears from the viewpoint of the theory of elastic-plastic contact, and suggests analytical solutions and regression equations to estimate the roughness parameter Ra in both conventional and ultrasonic machining.

This article studies dry hobbing of external cylindrical wheels by worm wheel hobs and reasons why some gear manufactures use hobbing without cutting fluids. Cutting fluids reduce frictional wear, provides temperature cooling of the tool or workpiece and helps flush away the chips from the cutting zone. But uneven cooling and different cutting conditions on the engaging and disengaging sides of a gear mesh provoke intensive wear of the teeth of the worm wheel hob, thus decreasing the life of worm hobs and increasing both the consumption of cutting tools and expenses for them. In this context, it becomes rather difficult to achieve efficiency and stability for the hobbing process. In the recent times, the cost of coolant disposal has been raised; in some cases, it accounts for 15-20% in terms of shop costs. Research was carried out under the following experimental conditions: a hobbing machine equipped with an automation system of high efficiency and with basic units of high static and dynamic stiffness; a high-accuracy worm hob from powdered metal wear-resistant high-speed steel of grade Р6М5К5; a workholding device with an elastic bush, and various cutting modes. Recommendations are given on using the multi-cycle hob-shifting strategy rather than the strategy of single-cycle shifting; and advantages gained by this technique are observed. Best cutting conditions and precision attained by dry gear-hobbing are described.

In modern machine tool design, precision is an important index to characterize machine tool performance. and precision allocation has become a key task. Since middle 20th century, the precision allocation method using optimization technology to balance manufacturing cost and quality has gradually developed. But most methods mainly take the cost minimization as the goal to optimize the precision allocation. As the precision and manufacturing cost are a pair of factors to be comprehensively considered, balance between them is needed to meet different design requirements. This paper proposes a comprehensive optimization method to trade-off between precision and cost. A multi-object precision allocation optimization model aiming at minimizing fuzzy manufacturing cost and comprehensive precision of machine tool is constructed. A multi-object optimization algorithm to solve the model is designed, combining the multi-objective grey wolf optimization algorithm with multi-objective decision analysis method TOPSIS. A case study based on a large-scale hobbing machine shows that the comprehensive optimization of manufacturing cost and machining precision is realized by using the proposed multi-object precision allocation optimization method.

This paper presents the influence of three lubrication environments, namely hobbing with minimum quantity lubrication (HWMQL), hobbing with flood lubrication (HWFL), and hobbing without any lubrication (HWAL), on the wear characteristics, microhardness, functional performance parameters, generation of noise and vibrations, flank surface roughness, and microgeometry deviation parameters of spur gears. Convective heat transfer coefficients in HWMQL and HWFL are evaluated to study the cooling mechanism involved and their heat dissipation capabilities during spur gear manufacturing. It is found that HWMQL-manufactured spur gears exhibited higher microhardness and smaller values of microgeometry deviations, flank surface roughness, functional performance parameters, wear rate, wear volume, and noise and vibrations than the spur gears manufactured by HWFL and HWAL. HWMQL facilitated a significantly higher convective heat transfer coefficient than HWFL, indicating its superior hobbing performance. An examination of the worn flank surfaces of HWMQL-manufactured gears revealed a wear track that resulted in less abrasive wear, wear debris, and subsurface damage, whereas the worn flank surfaces of HWFL-manufactured gears showed deep grooves, feed marks, and surface defects. This study proves that HWMQL is capable of manufacturing gears with better accuracy, enhanced wear resistance, smoother and quieter operational performance, and longer service life due to its better cooling and lubrication action. The results of this study will be very helpful for the manufacturers and users of spur gears.

Made available in DSpace on 2022-04-28T19:47:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-03-01 The life cycle inventory (LCI) data of a gear hobbing was obtained by means of the methodology unit process life cycle inventory (UPLCI), to conduct a comparative life cycle assessment (LCA) between hobbing assisted by flood lubrication (FL) and minimum quantity lubrication (MQL). The results pointed out 4 among 11 normalized environmental impact categories totalized more than 80% of the accumulated impacts: Fossil Depletion (43%), Climate Changes (19%), Terrestrial Acidification (11%), and Freshwater Consumption (8%). The identified hotspot in the case study was the input flow of raw material for the system “Hobbing Machine,” which was linked to more than 75% of the total amount of normalized potential environmental impacts. Once, changes on raw material depends on the gear design, the research focused on the environmental aspects of energy and cutting fluid consumption, which depends directly on the hobbing process parameters. The introduction of MQL provided reduction of 70.77% on the total amount of normalized potential impacts, while the strategies to reduce electric energy consumption by the machine tool accounted only for 3.74%. Nevertheless, when raw material flow is considered in the LCA, it turns into the process hotspot, due to high energy demanded in the steel-making process, forging, and turning operations to shape the semi-finished gear. The relevance of the key environmental aspects, electric energy, cutting fluids, and raw material, can vary significantly according to the gear size itself. The performed case study was considered a pilot project for the hosting company and can be scaled up to a whole gear manufacturing plant to identify manufacturing cells, which are eligible to optimization in the use of cutting fluids and electric energy by the machine tools. Department of Production Engineering Research Group On Sustainability Engineering Center for Science and Technology Management Federal University of São Carlos, SP Department of Production Engineering Research Group On Engineering and Product Life Cycle Management Federal University of São Carlos, SP Center for Advanced and Sustainable Technologies São Paulo State University, SP Center for Advanced and Sustainable Technologies São Paulo State University, SP

Gears are an important design component of many machines and mechanisms. The most important characteristics of machines and mechanisms, namely their accuracy and smoothness of operation, power density, and so forth, strongly depend on gears. These are the main reasons for the requirements to gears, including but not limited to their accuracy, as well as the productivity of their production, which are permanently growing. In production of gears hobs are used most extensively. High productivity of the gear hobbing process along with the economic performance of the gear generators is the main reason for that. Despite of that, further increase in productivity of the gear hobbing and the gear finishing operations is still required.

A numerical method is proposed for the shaping of tools used in machining complex surfaces with a periodical profile, including screw surfaces. A heuristic approach to Mathcad programming is adopted. The mathematical formulation includes a module generating spatial transformations of the coordinate systems in matrix form. On that basis, a numerical model of a hob profile is constructed. This approach permits single-stage shaping, without determining the profile of the associated helical gear.

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 productive worm of a hob for cutting Novikov gearing with two mash lines has a profile variable not only by a curvature radius, but also by its sign. A procedure is developed for relieving side surfaces of teeth of these milling cutters by a disk tool (abrasive disk) function-specific to ensure back angles of a milling cutter and realization quality of an initial contour of a gear with the production worm of a milling cutter.

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.

For today cog-wheels are widespread parts in mechanical engineering. Hobbing cutters are to machine such parts with an efficient gear-cutting tool. For the preliminary machining of cog-wheel teeth of diesel locomotive wheel-pair m=10mm with the allowance for grinding there are used rough hobbing cutters. At that for the improvement of grinding conditions an allowance must be uneven: larger at the tooth top and less at the dedendum. Therefore special milling cutters are needed for machining cogwheels with the allowance for grinding. The existing solutions are not urgent in the field of limited and small-batch production in connection with high cost and large laboriousness in manufacturing such milling cutters. The paper reports the analysis of existing designs of rough hobbing cutters and also there is investigated and developed a design and methodology of rough hobbing cutter profiling for the preliminary teeth machining of a cog-wheel m=10mm of a diesel locomotive wheel pair. The most efficient solution is development of a solid rough double tooth hobbing cutter. This solution has a number of both design and engineering advantages. The modeling of a cutting process allowed offering an efficient circuit of allowance cutting. The analysis carried out has shown that such a design has a number of advantages including economic ones and it may be recommended for practical application.

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.

Gear finish hobbing offers high potentials regarding economic and ecological aspects. Pre- and finish machining can be realized using only one machine and a complete dry process chain can be implemented as the hard finishing process by grinding is neglected. During the finishing cut, a small stock on the gear flanks is machined using high cutting velocities. The combination of high cutting velocities and small chip thicknesses leads to machining conditions in the finishing cut, which differ from those of regular gear hobbing and influence the tool wear behavior. As the finishing cut also determines the gear quality, a focus on tool life and achievable geometry is important. This paper deals with tool wear investigations for the finishing cut in dry gear finish hobbing. Different cutting substrates were investigated and evaluated with respect to the process productivity, tool life and wear behavior. For this purpose, empirical studies were conducted using the established fly-cutting analogy trial for gear hobbing. Based on the results of the wear investigations and theoretical considerations, a tool life equation for the application-oriented design of dry gear finish hobbing was derived. Cutting velocities from vc = 250 m/min up to vc = 2250 m/min were investigated under dry cutting conditions. The derived equation for tool life takes the allowable tool wear width VBmax into account, which allows a process design with respect to the required gear quality. As the productivity of gear finish hobbing is decisively determined by the selection of suitable tool and process parameters, material coefficients were implemented in the equation and determined for the investigated cutting substrates. The paper concludes with a verification of the derived tool life equation. The present work contributes to the identification of potentials and limits of gear finish hobbing. The results will serve manufacturing engineers as basis for their decisions for implementing this technology in series production.

In order to study the vibration characteristics of the workpiece spindle of a gear hobbing machine, this paper established a mathematical model for the workpiece spindle vibration displacement in the x-axis direction. To verify the reliability of the proposed model, a series of experiments were run on the CNC gear hobbing machine. The relative error between the experimental data and the calculated values was lower than 5 %, which proves that our proposed mathematical model is qualified to be a research tool for the investigation of the variation law of the workpiece spindle. According to the analysis results, the workpiece spindle vibration displacement literally decreases with the increasing order, and the hobbing process with high hob revolution speed and small cutting depth is preferable for gear hobbing accuracy.

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.

Background: Various relevant patents and papers which have reported noncircular gears synthesize the advantages of circular gears and cam mechanisms, and are widely used in many types of mechanical instruments. Hobbing is a better method for fabricating noncircular gears. There are 4 linkagemethods to hob noncircular gears. However, which linkage method should be chosen practically has not yet been reported. Objective: The goal of this work is to choose the best linkage method for hobbing noncircular gears. Method: Firstly, the hobbing models of noncircular gears was deduced. Then, based on the model, hobbing linkage methods of noncircular gears were obtained. Thirdly, under different hobbing linkage methods, their aspects (developing regularity of hobbing cutter trace, arc length of program blocks and motion axes of machine tools) were compared. Results: Finally, with the best characteristics of a high density of shaping cutter trace, high uniformity of arc length of program blocks and ease of control, the equal arc-length of gear billet (EALGB) is obtained. It has been proven that EALGB is an excellent linkage method to hob noncircular gears. Conclusion: It has been proven that EALGB is an excellent linkage method to hob noncircular gears.

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.

12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, 18-20 July 2018, Gulf of Naples, Italy / Edited by Roberto Teti, Doriana M. D'Addona 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering, CIRP ICME 2018, Napoli, Italy, 18 May 2018 - 20 May 2018; Amsterdam [u.a.] : Elsevier, Procedia CIRP 79, 68-73 (2019). doi:10.1016/j.procir.2019.02.013, Published by Elsevier, Amsterdam [u.a.]