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The purpose of this study is to develop an evaluation method for assessing the relative relationship between gear tooth shape deviations on every gear tooth using network theory. Our previous study introduced a method for representing the phase difference between each helix deviation as a network, demonstrating that it is possible to identify the relative relationship between gear tooth deviations. However, there has been no in-depth analysis of the impact of injection molding on the gear’s phase difference network. In this paper, we begin by measuring the gear tooth shape deviation, calculating the correlation coefficient, and expressing it as an adjacency matrix. When the adjacency matrix was visualized and displayed as a pixel plot, a periodic pattern was formed. The relationship between the position of the gate used to inject molten resin material into the injection mold and the pixel plot were then investigated in detail, and it was confirmed that the helix deviation network of a gear manufactured with the injection molding process is useful as a new indicator for the manufacturing error of injection-molded plastic gear.

Abstract A wireless smart gear system has been being developed as a gear health monitoring system. This system works based on the magnetic coupling between its two major elements consisting of a monitoring antenna and a smart gear. In operation, the smart gear works as a working gear in the power transmission system while the monitoring antenna monitors this gear continuously. Specifically, since the monitoring antenna is fixed and kept connecting to a network analyzer during the gear operation, it allows the magnetic coupling to occur and remain thoroughly. As a result, the return loss signal of the monitoring antenna becomes a resonant return loss data of the smart gear system. In this paper, the authors propose a method to use this resonant return loss signal for identifying two parameters called the $$Q$$ Q value and the angular frequency of the smart gear side antenna. There are three cases of magnetic coupling that have been considered. These cases are called Open, Short, and Loaded by a 50 [Ω] resistor where the characteristics of the antenna on the smart gear side are changed via its connector by leaving Open or connecting to a Short and a 50 [Ω] resistor modules, respectively. The results indicate that the method is effective for parameter identification of the $$Q$$ Q value and the angular frequency of the smart gear side antenna for all three cases. Moreover, the analysis of the validated $$Q$$ Q value and the angular frequency also indicates the relation between these two characteristic values and the practical conditions of the smart gear. This finding consolidates the effectiveness of our developing wireless smart gear system. Article Highlights This article proposes a parameter identification method for a developing smart gear health monitoring system. By only using the resonant return loss data obtained from the monitoring antenna, there are two characteristic values of the smart gear have been achieved. This article also introduces a completed return loss equation of a magnetically coupled system. This return loss equation is helpful for the computation of characteristic parameters of the magnetically coupled system such as a wireless power transfer system or a coupled antenna system. The results figure out that the identified characteristic parameters and the physical condition of the smart gear side’s antenna circuit are related to each other. This finding affirms the effectiveness of not only the proposed parameter identification method but also the developing smart gear system.

The present paper describes an inspection method for gear nicks at the final assembly process for automotive transmissions. This method applies multi-scale time synchronous averaging and optimal filtering to ensure the quality of transmission gears against abnormal noise due to gear nicks. At present, a highly reliable method has been developed for measuring gear noise in the final transmission assembly inspection process. However, the inspection system has realized not to evaluate nick-induced noise quantitatively. Therefore, the nick noise evaluation still relies on skilled workers who can estimate it subjectively. Eventually, it may reveal nick-induced noise on rare occasions after mounting the transmission on a vehicle. To avoid that requires the mechanism of nicks-induced vibration to be apparent. This paper proposes a method that can detect nick-induced vibration with high reliability. It also enables itself to identify a nicked gear even among several gears on a shaft in a transmission. The proposed method was implemented in the test equipment for the final assembly inspection and used to measure the noise of CVT units built with nicked gears. The results confirmed that this method allows itself to detect nicks that were hard to discern in subjective estimations and leads to making it possible to identify nicked gears. This method secures quality assurance against abnormal noise induced by transmission gear nicks.

Various factors in hobbing processes affect profile and helix deviations of hobbed gears, which prevent problems on the hobbing machine from being analyzed using them. Identifying problems requires a system that allows itself to evaluate motion errors in a hobbing machine from them. The proposed one in this study has artificial intelligence for image analysis. Thus, the developed hobbing simulation provided the data on profile and helix deviations of hobbed gears derived from the motion errors in the hobbing process, which determined correlation coefficients to express the similarities of the simulated micro-geometries between tooth flanks of a hobbed gear. These correlation coefficients are color-coded according to their magnitude and converted into images to constitute training data for artificial intelligence. However, different motion errors could lead to similar ones in tests with helical gears. The present paper describes the comparisons between images obtained for spur and helical gears computed by hobbing simulation and the comparisons between ones produced from the simulated and actual micro-geometries. As a result, the motion error that causes similar images depends on gear geometries, i.e., whether spur or helical. Furthermore, actual micro-geometries depend on hob accuracies, which require their compensation. That yields equivalent images to those produced from simulated data.

A hobbed gear has pitch, profile, and helix deviations caused by various factors. However it is hard for even experts to specify which factors cause individual deviations from the measured ones. Therefore, a hobbing-machine-diagnosis system could play a key role in identifying the factors from hobbed-gear deviations. Artificial intelligence for image recognition, which is comparatively easy to use, could be appropriate for the system. Generally, a larger number of training data for artificial intelligence could improve the system accuracy, however, it must take a longer time for learning. Besides, efficient learning requires small image data. Typical gear-deviation diagrams are large images that include unnecessary information for the diagnosis system. Therefore, the image data for training have only information on the correlation coefficients between the deviations of two teeth selected at a time. The present paper describes a method for providing image data for learning. The image is the arrangement of correlation coefficients in rows and columns as a grayscale. This paper also describes the relationship between the image and the hobbing problems; e.g., radial or face runouts. As a result, the image data showed clear characteristics for the difference in the periodicity of the hobbing problem, however, did not show clear characteristics for the difference in the directionality.

The present paper describes the geometries of honing wheels used in the final tooth-flank-finishing process of automotive transmission gears. For micro geometries of honed gears to keep their required gear accuracy, improper geometries of a gear-honing wheel cause the dressing interval to be short. That is because stock removals are not uniform over honed tooth flank, then the wear of honing wheel is also not uniform. As a result, micro geometries of honed gears are out of their tolerances. The amount of the stock removal could depend on sliding velocities between tooth flanks of a honed gear and a honing wheel so that uniform sliding velocities could yield uniform stock removals. In this paper, a geometrical approach is proposed to determine geometries of gear-honing wheels for minimizing the sliding-velocity variations. Gear-honing wheels were designed with the proposed method and were tested through honing experiments. The results showed micro geometries of honed gears keep their tolerances within a longer dress interval. Therefore, the proposed method for the determination of gear-honing-wheel geometries could extend the tool lives of honing wheels in mass production.

A smart gear sensor system has been developed for the condition monitoring of gear. This system includes a smart gear—the operation gear and a monitoring antenna. The analysis of the return loss of the monitoring antenna magnetically coupled with the smart gear gives the health condition of the gear. This research considers the effects of the distance and phase fluctuations between two components on the magnetic resonant return loss. The impacts of phase fluctuations include both static and high-speed conditions. Two experimental rigs have been built for the two cases. The coupling distance and static phase fluctuation are conducted via the first experimental rig. The second experimental rig performs both the coupling distance and phase fluctuation effect simultaneously while the smart gear rotates at high speed. During each test, the monitoring antenna return loss is captured thanks to a network analyzer. Analysis of the return loss data demonstrates that both the coupling distance and the change of the phase angle at the static condition and high speed have influenced the resonant return loss of the monitoring antenna. These findings are meaningful to the authors for evaluating and improving the accuracy of this gear health monitoring technique.

The present paper describes a method for determining geometries of gear-honing wheels used in the final process for manufacturing automotive transmission gears. Inappropriate geometries of gear-honing wheels could cause large undulations on finished gear-tooth flanks, and the finished gear would be out of the required accuracy. In such cases, the geometries of gear-honing wheels are required to be modified iteratively until the finished gears have sufficient accuracy. The change in meshing stiffness of a gear-honing wheel and a finished gear significantly affects the rotational synchronization. The poor rotation synchronization could cause the large undulation on a finished gear-tooth flank, to be different from the target micro geometries. This paper presented a geometrical approach that was proposed for a determination method of gear-honing-wheel geometries. The method allows the meshing stiffness to be balanced. Gear-honing wheels designed with the proposed method induced the desired micro geometries of finished gear-tooth flanks in gear-honing experiments. Therefore, the proposed method with the geometrical approach could be useful for the determination of gear-honing-wheel geometries.

In a hobbing process, many factors bring pitch, profile, and helix deviations to hobbed gears, and it is generally difficult to identify which factors cause the deviations. Therefore, a system that allows causal factors to be determined is required especially in the after-sales service of the hobbing machine manufactures. In the present study, a hobbing-machine-diagnosis system is being developed. Artificial intelligence will be employed in the system to easily determine the cause of deviations that occur in hobbed gears. The development of the system requires a lot of learning data for artificial intelligence. The data should be obtained in various conditions. However, collecting data from real cutting is almost impossible because of cost and time, so that hobbing simulation was carried out. The hobbing simulation has already been developed by many researchers. But those simulations are not enough for manufacturers of hobbing machines to evaluate problems in the machines. In addition, the conventional tooth profile/helix deviations diagrams are not appropriate for images used in artificial-intelligence systems, because they include redundant data. In the present paper, therefore, a network graph representing the correlation between every two teeth were discussed to examine whether the graph can be used as data for artificial-intelligence systems. As a result, an image of the network graph could be suitable for the system and could enable its data size to be reduced.

This paper evaluates the sensitivity of a proposed crack detection method of POM (Polyoxymethylene) gears using a deep convolutional neural network. The vibration signal was collected from an automatic data acquisition system for endurance tests of gears. The fast Fourier transform (FFT) of the measured vibration signals generated grayscale images for training input. A high-speed camera captured cracks at the tooth root, and the length of cracks was computed as a damage index for training labels. A convolutional neural network (CNN), called VGG16 ConvNet, which has 1000 classes in output, firstly was pre-learned from image data of ImageNet and then the weights of two layers, which were close to the output layer, was relearned from the created images of meshing vibration data with the transfer learning technique. The output layer was modified to fit two classifications problem related to the cracked or non-cracked situation of gears. The accuracy rate for the recognition of the gear fault reached 100%. However, the remained problem is whether the performance of the developed system is susceptible to the change of the working condition of gear, such as high rotational speed and torque, or not. Hence, the robustness of the crack detection performance of the developed system was investigated. The endurance tests of gears under some test conditions, such as high-low rotational speed and/or torque, were carried out to collect the different vibration signals. The accuracy rate of gear failure classification under various working condition was judged and the factors affected on the performance of the developed system under working condition changing was discussed. The results showed that the developed system learned from one testing condition incapably perform in varied testing conditions. In other words, the developed system must be learned from diversity data for a superior effectuation. In this case, our interest is to uncover how many experiments and images for re-learning are required in each experiment for better performance. The investigation of the re-learning in this paper showed the required number of images was 200 and a single endurance test under each condition was enough if an appropriate number of images were obtained.

The purpose of this study is to generate training data for failure detection by Neural Network, which estimates extent of damage from vibration data of meshing plastic gear pairs. Generally, measurement of meshing vibration with accelerometers installed on a gear system is not difficult. Therefore, one can automatically acquire vibration data under operation and obtain a large amount of vibration data. However, it is not easy to obtain a large amount of evaluation data of the damage extent. Evaluation of the damage extent is possible if the operation has stopped. It means that the procedure of this data acquisition requires additional work, and there is a need for acquiring data of the damage extent, automatically. This research addresses this need by developing an automatic data acquisition system of the meshing vibration and the extent of damage. In the developed system, an accelerometer installed on the housing of bearings is used for acquisition of vibration data, and a high-speed camera observes cracks occurring at the root of the target tooth. The measurement condition was discussed, and the effectiveness of the system was confirmed through experiments. Additionally, a damage index of the crack at the root was proposed, and the computed index was used as labels for training data. Finally, the measured meshing vibration data was visualized for generating training data of Convolutional Neural Network.

The present paper describes edge contact analyses for power transmission gears using tooth-flank-film elements, which have been proposed for finite element analyses of tooth stresses. For edge contact analyses, the tooth-flank-film elements are placed not only on usable flanks but also over tooth edges. Finer elements on edge neighboring areas and on the parts of usable tooth flanks where tend to contact with mating edges enable the edge contact to be analyzed. Comparisons of calculated distributions of tooth contact stress with observed tooth flanks after a running test showed the validity of the proposed method for edge contact analyses.

The high productivity of gear skiving has caused the process to attract a lot of attention in recent years and taper shaped skiving cutters are generally used in current gear skiving. A cutter axis in gear skiving is inclined to a gear axis and a taper shaped skiving cutter could cause tooth profile deviations of skived gears after re-sharpening of tool faces. Adjusting cutter position and/or re-profiling of cutter flanks are applied to enable tooth profile deviations of skived gears to remain within acceptable ranges when the adjustment is necessary. Adjusting cutter position is preferred to re-profiling of cutter flanks due to desire for low production cost. However, the universality of the current methods of calculating cutter positions is still not confirmed. The present study explains that the current methods for obtaining cutter positions could require individual calculating formulas according to cutter specifications for each particular gear. A universal method which avoids such complexity of making various formulas could contribute to permit gear skiving to become more useful in the gear machining field. Then such a desirable method is proposed and the reasonability of the method is confirmed by experiments. In addition, this study discusses the existence of scope in which the method can be applied and a method for checking collisions between cutter flanks and gear tooth flanks being cut after adjustment of cutter position.

This study proposes a rotary encoder-less time synchronous average using a non-linear filter for vibration analysis of gears in operation. This non-linear filter has smaller phase lag than that of conventional linear filters and a property synchronized with the meshing vibration. Therefore, the proposed method can estimate accurate rotational phase of gears. The accurate estimation of rotational phase of gears enable us to average the vibration responses including meshing frequency without any speed sensors and eliminate noises incoherent with the revolution periods of the gears of interest. In this paper, we explicate the ability and utility of this method are experimentally verified by using an ideal gear vibration model and an actual gear vibration response. As a result, the non-linear oscillator has an advantage compared with the linear filter for the highlighting the meshing frequency in the gear vibration, because of its demodulation property.

In this study, a measurement method of surface texture of tooth flanks with a CNC gear measuring instrument is validated. Especially, in order to inspect effect of probe profiles on the evaluation of surface texture, this paper proposes a numerical calculation method that simulates displacement of the probe contacting with tooth flanks of interest. The proposed method uncovers properties of profile curves of tooth flanks measured with different probes, which are installed on the gear measuring or surface roughness instruments. In the simulation, scanned data of a roughness standard test piece is used as tooth flanks, and ideal geometrical models of the different probes are used to calculate the probe motion. Finally, the statistical characteristic quantities of the simulated profile curves are evaluated to show the effect of the probe profile difference on the measurement results.

This article intends to discuss the possibility of how to quickly improve the functions of in-situ sensing gear conditions. This study proposes a new manufacturing method of the sensors by using a laser beam machine sinters conductive inks sprayed on gear surfaces as a crack sensor. For this purpose, we have developed a three-axis laser-printing machine. The developed three-axis laser printer was based on a three-axis CNC machine, and a 1.6W laser module (445nm wave length) was mounted on the principal axis of the CNC machine. However, the laser power was not enough for sintering the conductive ink sprayed on a 5-micrometer-thick polyimide as an insulated layer on steel plates. Thus, the 1.6W laser module is replaced with a 3.5W laser module, which has the same wavelength. This paper shows laser sintering conditions for the new laser module. In addition, a simple sensor, which is able to detect cracks at roots of gear teeth, is proposed. The sensor is formed of an involute spur gear. The electrical property, such as resistance, of the proposed crack sensors was investigated in this paper, and it was concluded that the relation between the resistance and module of gears was linear, when the number of teeth was fixed.

Taper shaped skiving cutters are commonly used in gear skiving because they provide clearance angles under a simple gear arrangement and they could have lower cutting resistance than cylindrical skiving cutters. Their tooth flanks are generally ground by the same method of generation grinding with a trapezoidal wheel as ones for pinion cutter tooth flanks for gear shaping. In gear skiving, however, the process of tool-face re-grinding could increase profile deviations of cut gears due to the lack of an appropriate change in cutter profiles along the facewidth. In the present paper, computer programs were developed to simulate the generation grinding and the skiving with the cutting edges ground by the method. Skiving with a cutter ground by the method were carried out and skived tooth forms were compared with those calculated by the developed program. As a result, the comparisons could guarantee the reliability of the program. Furthermore, the program has revealed that the increase in a helix angle of a cutter yields the increase in tooth profile deviations of skived gears after regrinding the tool faces. Therefore, taper shaped cutters should be applied to internal gear skiving with extra caution.

Improving quality of hobbed gears must yield high accuracy of ground gears. This can be achieved by measuring hobbed gears accurately. Pitch deviations are most crucial in grinding processes with a worm type grinder. In the preset study, a new measuring method is proposed for single pitch deviations. In this method, measured helix trace data are conditioned with Gaussian Filter to be divided into short- and long-wavelength components. Single pitch deviations are calculated from the long-wavelength components around the center of facewidth. The calculated deviations showed 62.6 % lower variation in iterative measurements.

This paper proposes a design method of a PD controller with consideration of acceleration responses of an auxiliary mass for active mass dampers (AMDs) operated by a combination system of a neural oscillator and position controller. In the former proposed controller, a desired value of the auxiliary mass was calculated by the output of the oscillator synchronized with the structure's vibration response, and the auxiliary mass was operated to the desired value by the position controller, then the resulting motion of the auxiliary mass mitigated the vibration of the structure. This system can easily solve the stroke limitation problem of the AMD by saturating the desired value of the auxiliary mass within the limit, however the ratio of the control force to the mitigation of the structural acceleration, called as cost-performance, is worse than the system designed by the ordinary linear control method. Therefore, the purpose of this paper is the improvement in the cost-performance of the system by reconsidering the design method of position controller's gains. For this purpose, the gains of the position controller are designed with consideration of the acceleration response of the auxiliary mass, that is tried to approach asymptotically to the ideal sine wave. Numerical simulations with several types of earthquakes indicate that the modified controller has better cost-performance when compared with the former controller.

In the present paper, tribological properties of DLC films deposited by UBM sputtering were investigated by rolling contact friction tests under approximately 2.86GPa of high contact pressure. DLC films were deposited by UBM sputtering under substrate bias voltages such as 0 to -300V. Friction coefficients of DLC films deposited under low bias voltage were unstable. And it was found that this unstable friction coefficient occurred when the counterpart ball began to contact with sliding. From observation of the cross section of DLC films after occurring unstable friction coefficients using SEM, the films were fractured in the a-C:H or Cr/C gradient interlayer. Moreover, these fractures had already occurred before sliding. DLC films were thought to be damaged by rolling with slightly sliding contact with high pressure in very short time. It was clear that DLC film deposited under substrate bias voltage -300V has the best performance for high contact pressure. And the change in Young's modulus of Cr/C gradient interlayer at the interface continuously seemed to be important to prevent the fracture of DLC film due to high pressure.

This paper proposes a new control system for active mass dampers. The control system is a feedback system, which combines a single neural oscillator and a position controller. The output of the neural oscillator synchronized with the structure response is a rhythmic command which decides the driving direction of the auxiliary mass, and a special map plotted by periodic solutions of the oscillator determines the travel distance of the auxiliary mass in comparison with the current state of the neural oscillator affected by the structure response. In addition, in order to move the auxiliary mass to the desire value, the position control system consisting of a PD controller is used. As a result of numerical simulation, when the structure was quaked by the several types of the earthquake, the structural vibration was reduced by the effect of the auxiliary mass' motion, and the results showed that the proposed system was verified as the controller for the active mass dampers and was able to be used to solve the stroke limitation problem of the active mass dampers.

This paper proposes a feedforward controller for active suppression of contact force fluctuation between a pantograph and overhead contact line of a high-speed railway vehicle, assuming the use of a pneumatic actuator with low response speed. The proposed controller, designed to exploit the periodicity of the disturbance as a priori knowledge, consists of a phase-locked-loop-based synchronized harmonic generator and adaptive compensators to extract the periodic structure of the disturbance and to generate the appropriate input for disturbance cancellation. The synchronization condition of the phase-locked loop is clarified through a theoretical study, and a design of the controller that can cancel the sinusoidal disturbance with the frequency from 1 Hz to 10 Hz is presented. Numerical studies are carried out to examine the validity of the theoretical study and the performance of the controller. Experimental studies using an actual pantograph equipment show that the proposed controller can suppress the contact force fluctuation to 10 % of that without control at the frequency from 1 Hz to 10 Hz.