Aiming at the problems of low machining accuracy and more serious tool wear in the traditional diamond grinding machining (DGM) microstructure of hard and brittle materials, this paper proposes high-speed rotary ultrasonic machining (HRUM) technology and develops a HRUM machine tool. The hardware part of the machine tool mainly includes the spindle module, micro-motion system module, ultrasonic machining tank module, and data acquisition (DAQ) system module. The LabView-based controlled machining control system, including motion selection, initialization, coarse tool setting, constant force tool setting, control machining, and coordinate display module, is developed. Comparative experimental research of the HRUM and DGM of small holes in Al₂O₃ ceramics is carried out in the developed HRUM machine tool. The results demonstrate that HRUM effectively reduces axial cutting forces, reduces binder adhesion, and suppresses slippage while improving tool-cutting ability and extending tool life compared to DGM under the same machining parameters. This technology has essential research significance for the high-precision and efficient machining of microstructures in hard and brittle materials.

The removal of pineapple eyes is a crucial step in pineapple processing. However, their irregularly distributed spiral arrangement presents a dual challenge for positioning accuracy and automated removal by the end-effector. In order to solve this problem, a pineapple eye removal device based on machine vision was designed. The device comprises a clamping mechanism, an eye removal end-effector, an XZ two-axis sliding table, a depth camera, and a control system. Taking the eye removal time and rotational angular velocity as variables, the relationship between the rod length of the prime mover and the contact force and gear torque during the eye removal process was simulated and analyzed using ADAMS (2020) software. Based on these simulations, the optimal length of the prime mover for the end-effector was determined to be 23.00 mm. The performance of various YOLOv5 models was compared in terms of accuracy, recall rate, mean detection error, and detection time. The YOLOv5s model was chosen for real-time pineapple eye detection, and the eye’s position was determined through coordinate transformation. The control system then actuated the XZ two-axis sliding table to position the eye removal end-effector for effective removal. The results indicated an average complete removal rate of 88.5%, an incomplete removal rate of 6.6%, a missed detection rate of 4.9%, and an average removal time of 156.7 s per pineapple. Compared with existing solutions, this study optimized the end-effector design for pineapple eye removal. Depth information was captured with a depth camera, and machine vision was combined with three-dimensional localization. These steps improved removal accuracy and increased production efficiency.

Engineering mechanics laboratory teaching has long been constrained by high equipment costs, limited hands-on opportunities for students, and a lack of intuitive data visualization. To address these challenges, this paper presents a practical and low-cost experimental design method based on the Phyphox software and smartphone sensors. Two representative experimental modules were developed as examples: the measurement of the rigid body moment of inertia and the determination of Young’s modulus via the bending method. The experimental principles, apparatus construction, and data acquisition and processing methods were systematically analyzed. The results demonstrate that the proposed method achieves relative errors of 3.8% for the moment of inertia and 6.83% for Young’s modulus, confirming its feasibility and close agreement with theoretical values in engineering mechanics education. On this basis, the method was integrated into a structured 5E instructional framework, and a comprehensive instructional design was established, covering teaching objectives, key points and difficulties, implementation steps, and assessment methods. This study proposes a novel teaching method that transforms traditional engineering laboratory experiments into a low-threshold, data-and-analysis-integrated learning model. Experimental validation shows that the proposed method can reduce costs and enhance data visualization, thereby promising to improve students’ engagement in engineering mechanics education.