The precise measurement of inner dimensions and contour accuracy is required for deep-hole parts, particularly during the manufacturing process, to monitor quality and obtain real-time error parameters. However, on-machine measurement is challenging due to the limited inner space of deep holes. This study proposes an automatic on-machine measuring apparatus for assessing inner diameter, straightness, and roundness errors. Based on the axial-section measurement principle, an integrated measuring module was designed, including a self-centering mechanism, a diameter measuring sensor, and a positioning reference sensor, all embedded within a control system. On this basis, calculations of the inner diameter, and evaluations of the straightness and roundness errors are presented. Experimental verification is conducted on a blind deep hole with a nominal 100 mm inner diameter and 700 mm depth. Compared with measurements performed on a coordinate measuring machine (CMM), which is limited to a maximum hole depth of 300 mm, the proposed apparatus achieved full-depth on-machine measurements. Meanwhile, the measurement results were consistent with the data obtained by the CMM. The straightness error is considered less than 0.05 mm, and the roundness error is considered less than 0.015 mm. Ultimately, without requiring any additional reference platform, the proposed apparatus shows promise for measuring deep-hole parts on various machine tools, with diameters of no less than 80 mm and theoretically unlimited hole depth.

Dimensional and profile measurements of deep-hole parts are key processes both in manufacturing and product lifecycle management. Due to the particularity of the space conditions of deep-hole parts, the existing measurement instruments and methods exhibit some limitations. Based on the multi-axis, highly precise servo drive system, a novel measuring device is developed. The laser displacement sensors are fed by the flux-switching permanent magnet linear motor, and the part is rotated by the servo motor. On this basis, the assessment methods of roundness, straightness, and cylindricity are proposed by employing the least square method (LSM). Additionally, considering the axial center deviation between the sensors and the part, the rotating center coordinate is optimized by the gradient descent algorithm (GDM). Then, the measurement system is constructed and the experiment study is conducted. The results indicate favorable evaluation error of the LSM fitting and GDM iteration. Compared with the coordinate measuring machine (CMM), the measured results show good consistency. In the error analysis, the angle positioning error of measured point is less than 0.01°, and the axial positioning error is less than 0.05 mm. The proposed system and assessment method are regarded as a feasible and promising solution for deep-hole part measurements.

A hybrid-loop servo control system based on sliding-mode control and neural network control is proposed in this article to realize flexible forming process on a double toggle mechanical press. First, kinematics and dynamic analysis of the drive system are conducted to derive the control objective behavior. Then, the structure of the hybrid-loop system is introduced; sliding-mode control is applied to compensate the punch position errors. In the inner loop of the servo motor, complementary sliding-mode control is used to track the motor rotation angle, and a radial basis function neural network estimator is applied to eliminate the considerable load disturbances in the stamping stage. Finally, experimental hardware is constructed and a compound blanking and drawing process is carried out to validate the proposed servo control system in practice. With different materials and drawing depth, high tracking accuracy and robustness are exhibited to realize favorable forming performance.

Breeze energy is a widely distributed renewable energy source in the natural world, but its efficient exploitation is very difficult. The conventional harvester with fixed arm length (HFA) has a relatively high start-up wind speed owing to its high and constant rotational inertia. Therefore, this paper proposes a harvester with a helix s-type vertical axis (HSVA) for achieving random energy capture in the natural breeze environment. The HSVA is constructed with two semi-circular buckets driven by the difference of the drag exerted, and the wind energy is transferred into mechanical energy. Firstly, as the wind speed changes, the HSVA harvester can match the random breeze to obtain highly efficient power. Compared with the HFA harvester, the power coefficient is significantly improved from 0.15 to 0.2 without additional equipment. Furthermore, it has more time for energy attenuation as the wind speeds dropped from strong to moderate. Moreover, the starting torque is also better than that of HFA harvester. Experiments showed that the HSVA harvester can improve power performance on the grounds of the wind speed ranging in 0.8–10.1 m/s, and that the star-up wind speed is 0.8 m/s and output peak power can reach 17.1 mW. In comparison with the HFA harvester, the HSVA harvester can obtain higher efficient power, requires lower startup speed and keeps energy longer under the same time. Additionally, as a distributed energy source, the HSVA harvester can provide a self-generating power supply to electronic sensors for monitoring the surrounding environment.