Hob Wear Research Articles

Intelligent decision-making for TBM tunnelling control parameters using multi-objective optimization

In tunnel construction, tunnel boring machine (TBM) tunnelling typically relies on manual experience with sub-optimal control parameters, which can easily lead to inefficiency and high costs. This study proposed an intelligent decision-making method for TBM tunnelling control parameters based on multi-objective optimization (MOO). First, the effective TBM operation dataset is obtained through data preprocessing of the Songhua River (YS) tunnel project in China. Next, the proposed method begins with developing machine learning models for predicting TBM tunnelling performance parameters (i.e. total thrust and cutterhead torque), rock mass classification, and hazard risks (i.e. tunnel collapse and shield jamming). Then, considering three optimal objectives, (i.e., penetration rate, rock-breaking energy consumption, and cutterhead hob wear), the MOO framework and corresponding mathematical expression are established. The Pareto optimal front is solved using DE-NSGA-II algorithm. Finally, the optimal control parameters (i.e., advance rate and cutterhead rotation speed) are obtained by the satisfactory solution determination criterion, which can balance construction safety and efficiency with satisfaction. Furthermore, the proposed method is validated through 50 cases of TBM tunnelling, showing promising potential of application.

Wear prediction study considering TBM hob slip

Aiming at the problem of serious tool wear and untimely tool change affecting the construction efficiency when TBM is digging in microfractured tuff strata, the hob wear prediction study is carried out based on engineering examples. By analysing the rock-breaking mechanism of hob wear, a linear wear rate model and a life prediction model considering hob slip are derived based on the CSM model and the abrasive wear theory; the wear rate and the wear amount at the tool change point are further calculated by the prediction model in comparison with the measured value, and the furthest digging distance of the hob is calculated by the life prediction model. The results show that: the relative error between the measured average wear rate and the calculated linear wear rate considering the hob slip is 5.3%; the life prediction model predicts that the longest digging distance of the positive hob is 857 rings and the shortest distance is 198 rings. The results of the study are of guiding significance for the prediction of tool wear and the selection of the timing of opening and changing the hob in TBM tunneling.

A two-stage feature selection method for hob state recognition

In mechanical field, tool state seriously affects the production quality and efficiency. Recently, machine learning is an important technique to realize tool state recognition. Extracting and selecting features from the raw input data of the machine learning model can effectively eliminate useless information and improve the model accuracy. Therefore, a two-stage feature selection method is proposed in this paper. We first qualitatively propose the non-discreteness and separability of signal features. For these two properties, quantitative calculation methods are then developed in two stages. The proposed two-stage feature selection method is applied to machine learning models for tool state recognition. In engineering application verification, the proposed method achieves an average recognition accuracy of 84.4% in hob wear recognition, and an average recognition accuracy of 99.8% and 94.4% for two data sets in hob fault recognition, which is significantly higher than other feature selection methods. The verification results indicate that the proposed method has good generalization and engineering application value.

Analysis of force and wear mechanism of shield hob under conventional working conditions

In order to study the force and wear of the hob during the excavation of the composite formation, Abaqus simulation is used to simulate the stress state of the hob when breaking the rock and soil. Based on the abrasive wear mechanism, a prediction model of radial wear amount and linear wear rate suitable for a single hob is derived, and verified with actual engineering data. The results show that the normal force plays an important role in the rock breaking of the hob, and the penetration degree and the cutting edge radius are important factors affecting the normal force of the hob. When the penetration degree increases, the normal force and radial wear of the hob will increase, but the increase of radial wear will slow down due to the width of the cutting edge. Working with the “small-large-small” penetration law not only improves the boring efficiency but reduces the loss of the hob. Therefore, the study provided references for the hob wear and optimization research.

Online unsupervised monitoring for hob wear based on multi-domain vibration features extraction and improved Q-statistic control chart

Current mainstream wear diagnosis approaches rely on the entire lifecycle wear-labeled datasets, which is costly, delayed, and low-universality. To achieve the monitoring goal of online, real-time, and strong generalization for hob wear and get rid of the dependence on the wear datasets, an online unsupervised monitoring method is explored based on the multi-domain features extraction and improved Q-statistic control chart, which is progressive. First, the monitoring feature matrix of each hob shifting period is transformed into the principal component subspace, the wear subspace, and the interference subspace based on the principal component analysis (PCA). Then, features in the wear subspace of the previous shifting period are used to get the control limit and those of the next shifting period are used to calculate the Q-statistic. The hob wear status is determined by comparing the control limit and the Q-statistic. The gear hobbing experiment verifies the feasibility of the proposed method.

Hob wear state condition monitoring based on statistical distribution law

Hob wear state condition monitoring based on statistical distribution law

Hob vibration signal denoising and effective features enhancing using improved complete ensemble empirical mode decomposition with adaptive noise and fuzzy rough sets

The acquired hob vibration signals are inevitably contaminated by noise in the industrial environment, which changes the vibration signal frequency distribution and reduces the accuracy of feature extraction and hob wear identification. To solve this problem, a novel hob vibration signal denoising and effective feature enhancement method, CEEMDAN-FRS, is proposed based on the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and fuzzy rough sets (FRS). First, the effective frequency distributions of gear hobbing, particularly the modulation sidebands, were obtained as prior knowledge by analyzing the hob vibration response mechanism. Then, the two key parameters of CEEMDAN (i.e., noise standard deviation and ensemble size) were adaptively determined based on signal characteristics to achieve improved decomposition compared with the use of fixed values. The evaluation and selection of intrinsic mode functions (IMFs) based on a single feature such as kurtosis or root mean square, only focus on the partial characteristics, leading to an identification bias. Thus, 11 features with different sensitivities of IMFs are weighted based on FRS, and fused as a unified feature to conduct a comprehensive IMF evaluation. Finally, a reweighted IMF reconstruction strategy is proposed. The comparisons of the proposed method and related approaches to hob vibration signals show that the proposed method achieves better performance in terms of effective feature enhancement, noise removal, and signal-to-noise ratio improvement

Hob wear prediction based on simulation of friction, heat fluxes, and cutting temperature

Hob wear prediction based on simulation of friction, heat fluxes, and cutting temperature

Influence of Contact Surfaces’ Impact on the Gear Profile during Hobbing Process

This work is the result of research on the dynamic process that occurs during milling machining, namely, the influence of the contact surfaces’ impact on the gear and the hob and the influence of their displacements on the resulting profile of the tooth. An acquisition system was placed on the final elements of the milling machine chain to determine the torque moments and displacements during gear milling. The experimental analysis proves that the displacements are within admissible limits and have no major influence on the quality of the processing surfaces. A dynamic simulation of the hobbing process with the finite element method (ANSYS) was performed for a limited period of time, and the values of deformation, equivalent strain, and stress have been determined; the time at which the chips come off and the corresponding value of the equivalent stress that occurs at their break were determined based on the maximum distortion energy von Misses theory. It is required to simulate the entire hobbing process, even if it can be time-consuming to differentiate the influence of the dynamic behavior of the machine’s kinematic supplementary chains on the hob wear and the tooth profile. A modal analysis will be able to support the comparative study related to the obtained experimental data.

Performance comparison of green lubricants in gear hobbing with minimum quantity lubrication

It presents performance comparison of two green lubricants based on fatty alcohol and synthetic ester in hobbing with minimum quantity lubrication of spur gears using temperature generation at teeth hobbing zone (THZ), heat transfer mechanism, hob wear, gear quality, microhardness, and work hardening capacity. It is found that fatty alcohol-based lubricant yielded smaller THZ temperature, minimum hob wear, microgeometry deviations, and flank surface roughness and higher Prandtl number, microhardness, and work hardening capacity than synthetic ester-based lubricant. Higher Prandl number means friction reduction at THZ by forming a stable protective layer and heat dissipation by convection. Synthetic ester reduced THZ temperature by better cooling action. The outcome will aid in selection of better green lubricant to improve sustainability of hobbing.

Sustainability and performance assessment of gear hobbing under different lubrication environments for manufacturing of 20MnCr5 spur gears

Sustainability of a gear machining process is adversely affected by large amount use of synthetic machining fluids which harm the environmental and human health. This concern has necessitated development and/or exploration of innovative options without compromising quality and productivity of gears. This paper describes assessment of sustainability and performance of gear hobbing for manufacturing of spur gears made of 20MnCr5 under different lubrication environments referred as hobbing without any lubrication (HWAL), hobbing with minimum quantity lubrication (HWMQL), and hobbing with flood lubrication (HWFL). Type of lubricant and its consumption, particulate emissions, their toxicity, fumes generation, energy consumption and hob wear have respectively been used as indicators for environmental, social, and economic aspects of sustainability. Gear quality in terms of microgeometry deviation parameters and flank roughness; and material removal rate have been used for performance assessment of HWMQL, HWFL, and HWAL. This study reveals that HWMQL consumes only about 4–5% of lubricant than HWFL; increases hob cutter life by 1.5 times; improves productivity by 27.2%; and consumes 27.8% less specific energy than HWFL. HWMQL also gives good air quality at the workplace with lesser nontoxic particulate emissions and lower fumes generation. Moreover, spur gears manufactured by HWMQL had minimum microgeometry deviations and better flank finish. This study establishes HWMQL environment as the most promising alternative for sustainable manufacturing of good quality spur gears with high productivity.

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

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

Influence of Cutting Speed on the Hob Wear in Hobbing with the Minimum Quantity Lubrication

Influence of Cutting Speed on the Hob Wear in Hobbing with the Minimum Quantity Lubrication

A 3D chip geometry driven predictive method for heat-loading performance of hob tooth in high-speed dry hobbing

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.

AN EXPERIMENTAL STUDY ON CUTTING PERFORMANCE OF HSS HOB WITH TIN COATING FILM IN DRY HOBBING

This paper presents the result of a systematic dry hobbing experiment to examine the influence of cutting speed on the tool life and machining performance for a TiN-coated HSS hob. The hobbing experiments were performed under different cutting speeds from 100 m/min to 300 m/min. Some important machining items related to the tool life, for example, the influence of instantaneous cutting temperature, the characteristics of hob wear, the chip deformation and the effect of chip crush behavior are discussed in detail. The importance of the cutting speed employed in a dry hobbing process is verified, i.e. an appropriate speed not only leads to the small deformation and better fluidity of the chips and thus reduces the occurrence of chip crushing action, but also effectively control the oxidation progress of coat material. Based on the experiment results, a suitable cutting speed range due to the tested hobbing conditions is confirmed.

WPŁYW POSUWU WZDŁUŻNEGO NA ZUŻYCIE FREZU ŚLIMAKOWEGO I SIŁY SKRAWANIA PODCZAS FREZOWANIA OBWIEDNIOWEGO Z MINIMALNYM WYDATKIEM CIECZY OBRÓBKOWEJ (MQL)

In the paper evaluation of the influence of axial feed on the hob wear in hobbing with minimal quantity lubrication technique has been done. As a work material C45 carbon steel has been investigated. Wear resistance of the hob made from high speed steel HS6-5-2 without coating has been investigated. For comparison tests with conventional fluid supply method have been carried out. Gears have been generated with full depth of cut and with two axial feed with constant cutting speed value. During hobbing cutting forces have been measured by experimental stand. Tool wear has been measured directly as a width of flank wear land of the hob cutter teeth. During investigation any significant wear changes on the rake faces haven’t been detected, so those results haven’t been taken into consideration. A constant length of cut parameter has been established as a criteria value. Results of investigation have been presented in the form of graphs describing changes of wear land width parameter in comparison to the most loaded tooth wear land parameter. Also changes of cutting forces in time are presented too. On the base of obtained results conclusion has been formulated that MQL technique might be used as an alternative solution for supplying cutting fluid into the cutting zone during hobbing process.

Cutting Performance of Coated High Speed Steel Hobs in Dry Hobbing

This paper presents the results of a systematic dry hobbing experiment performed on a general mechanical transmission type of hobbing machine. All four types of coated HSS hobs demonstrate satisfactory cutting performance applicable enough to practical production. Some related details such as the characteristics of hob wear, the effect of chip crush behavior and cutting speed are also discussed based on the experiment results.

Generalized method for calculating the durability of sliding bearings with technological out-of-roundness of details

This article presents the generalized cumulative model of wear kinetics for the sliding bearing elements in the case of single area and single-and-double area contact for different phases of rotating a shaft with a small contour ovality and a bushing with a circular contour. As a result of the performed numerical analysis, the regularities of the change in the initial maximum contact pressure due to shaft rotation and shaft changes due to wear are described, as well as the durability of the bearing and the linear wear of the bushing and shaft depending on the size of ovality are discussed in the work. The contact pressures are considerably higher in the zones of double-area contact than in the zones of single-area contact. The shaft ovality increases the bearing durability and reduces the hob wear. The results are presented graphically by means of charts.

切りくずの切削過程におけるホブ摩耗の分配

切りくずの切削過程におけるホブ摩耗の分配

Development of a new rapid characterization method of hob's wear resistance in gear manufacturing—Application to the evaluation of various cutting edge preparations in high speed dry gear hobbing

Gear hobbing remains a cutting technology mainly dedicated to large-scale productions of gears for the automotive industry. The improvements in hobbing tool design are problematic due to the very long duration of wear tests and due to the application of special machine tools only available in production plants. In order to overcome these limitations and to accelerate the efficiency of the investigations, a new rapid testing method called “flute hobbing” has been developed on a standard five-axes milling machine widely present in research laboratories. This testing method has been associated with a software providing the geometry of each chip in hobbing. The correlation of the chip geometry with the wear of each tooth enables to discriminate the critical teeth of a hob in order to focus the development in this area of the cutting zone. This new methodology has been used to investigate the influence of the cutting edge preparation on the wear resistance of gear hobs made of PM-HSS in the context of dry high speed manufacturing. The application of the AFM technology to generate defined edge preparation has shown its efficiency to improve the tool wear resistance and has confirmed previous results.