This study discusses the quality analysis of a new tapping machine. By measuring the torque and speed of the tapping machine, the correlation between the quality of the nut and the tapping process is analyzed. Using various regression trees and learning methods, the results show that the magnitude of the tapping torque is an essential indicator of the quality of the thread. In addition, through such methods, the appropriate operation boundary of the tapper torque and tapper speed can be determined. Thread, processed in the two boundaries, can have a high probability of being a Class 1 thread. Furthermore, the tapping torque is also used for quality classification by a one-dimensional convolutional neural network (1-D CNN). The 1-D CNN model performs high classification accuracy and has the ability to localize the important area of the signal. The experimental results show that the regression tree can effectively find the operation boundary, providing information for users to adjust their machine settings, and improve the thread quality. In addition, the off-line classification of thread done by humans can be replaced by the high-accuracy 1-D CNN model predicting the thread quality in real-time. The detection speed can be accelerated, and labor costs can be reduced.

In this paper, the compact reducer which can be used as a rigid drive mechanism with a high-speed reduction ratio is systematically studied for kinematics and dynamics. The speed ratio is determined by the number of cam lobes and engaged rollers. The eccentric rotating conjugate lobe cam profile is synthesized by using the rigid body transformation method. To characterize the input torque based on Newton’s second law, the transmission forces of the resisting and driven multi-roller are proportional to the arm length of each actuating roller found by its geometric vector. In consideration of machining undercutting, roller limit load, and contact stresses, favorable designs can be achieved by adjusting the cam size, turret dimension, eccentricity, and roller size. Together with experimental tests, a prototype of the lobe cam reducer is made to verify the feasibility of the proposed design procedure and to investigate its kinematic and dynamic characteristics for speed reduction ratios, torques, and transmission efficiency.

In this study, the quality of a nut is discussed by considering images of the internal thread, and an analysis is conducted using traditional machine-learning and deep-learning algorithms. Compared to the traditional contact methods, the vision-based method has the advantage of fast computing speed and is not affected by the conditions of tapping speed. The pitch and pitch diameter of the internal thread are the indicators that characterize nut quality classification. For one nut, 36 internal thread images are collected, one image per 10 degrees, by the self-designed laser triangulation measurement platform. Using the laser triangulation method, the information on both indicators can be obtained and analyzed. In the traditional machine-learning methods, the internal thread images undergo several preprocessing procedures to obtain the region of interest and calculate the depth between the crest and root. Subsequently, 33 handcrafted features are used to extract the features from the 36 processed images. Finally, the features are classified by three families of machine-learning algorithms, including support vector machines, k-nearest neighbors, and decision trees. In the deep-learning method, conditional generative adversarial network and convolutional neural network (CNN) are used for data augmentation and nut quality classification, respectively. The experimental results show that the proposed CNN model can achieve a higher classification accuracy rate. Furthermore, the proposed CNN model trained with the generated images is better equipped to detect the nut quality under different decision thresholds.

With the increasing demand for safety and automatically locking nuts, it’s important to guarantee a consistent nut quality. Traditionally, a floating tapping machine has been used for high-speed production, but it has unstable thread quality because of the existing gap between the tap and nuts holder. To overcome this problem, a stable tapping machine must be considered for tapping high precision threaded nuts and the tapping process must be monitored in real-time for the internal threads quality to reduce the inspection time. First, this article used the relative movement between a nut and its tap to establish the dimensionless tapping material removal rate. Furthermore, for creating the tapping torque curve of a specific nut, a few nuts were tapped to obtain the maximum value and variation of the tapping torque at various tapping speeds. Then, based on the differences in the hole sizes, chamfer depths, and material nature, the quality assurance range can be constructed as a real-time monitoring model for high precision thread manufacturing. To demonstrate the feasibility of the proposed procedure, tapping for carbon steel, alloy steel, and titanium alloy nuts was performed and the monitored tapping torques matched the nut quality classification of Japanese Industrial Standards (JIS).