The concept of digital twinning has become a hot topic in the manufacturing industry in recent years. The emerging digital twin technology is an intelligent technology that makes full use of multimodels, big data, and interdisciplinary knowledge, which provides some new approaches for the field of the intelligent manufacturing industry. The job shop scheduling problem has been an important research field in the discrete manufacturing industry. Digital twin technology is adopted to solve the problem of job shop scheduling, which provides the possibility for the intelligent development of workshops. Based on digital twin technology and combined with the actual problem of production line scheduling, we propose a new intelligent scheduling platform to solve the shop scheduling problems above. Meanwhile, based on the prediction and diagnosis of multisource dynamic interference in the workshop production process by big data analysis technology, the corresponding interference strategy is formulated in advance by the scheduling cloud platform. The model simulation experiment of intelligent dispatching cloud platform was carried out, and some enterprises in intelligent manufacturing workshop were taken as examples to verify the superiority of the dispatching cloud platform. Finally, we look forward to the future research direction of intelligent manufacturing based on digital twin technology.

Ultrasonic transducers with broad bandwidth are considered to have high axial resolution and good ultrasound scanning flexibility for the clinical applications. The limitations of spatial resolution due to bandwidth are of great concern in ultrasound medical imaging. The method of acoustic impedance matching between the piezoelectric element and medium is commonly used to obtain broad bandwidth and high resolution. In this study, an optimized backing layer design was proposed to broaden the bandwidth by adding a tunable acoustic impedance matching layer of backing (AIMLB) between the backing layer and the piezoelectric ceramic element. The Mason equivalent circuit method was used to analyze the effect of the backing material composition and its structure on the bandwidth of the transducer. The optimized transducer was simulated using the finite-element method with the PZFlex software. Based on the PZFlex simulations, a 20-MHz ultrasonic transducer using the AIMLB with a bandwidth of approximately 92.29% was fabricated. The experimental results were in good agreement with the simulations. The ultrasonic imaging indicated that the designed ultrasonic transducer with an additional AIMLB had high performance with good imaging capability.

Motor skills are essential for robots to accomplish complicated and dexterous manipulation tasks, which are difficult to be mastered through traditional controller designs. Currently, robots learning from demonstrations enable them to learn control policies automatically from human motor demonstrations. However, the nonlinearity and instantaneousness of the demonstrated forces prohibit robots from fully mastering the motor skill features by simply exploiting force examples. Therefore, a self-improvement learning scheme is required to refine the control policy further until satisfactory motor skills are acquired. Hence, this paper combines learning from demonstrations and reinforcement learning to learn a controller for complex motor skills. The proposed method is validated on an IIWA KUKA robot, performing a specified nonprehensile manipulation task. Note to Practitioners—The motivation of this paper originates from the requirement to develop an efficient and fast learning algorithm that improves the robot skill learning efficiency. Specifically, our research focuses on the nonprehensile manipulation task, easily subject to environmental changes. Therefore, the robot must continuously interact with the environment to master the skill. To accelerate the skill learning process, learning from demonstrations initializes the control policies, and then the robot starts to practice the demonstrated skill. After each practice round, the robot receives a reward from the environment, and based on the reinforcement learning algorithm limited up to 100 trials, the robot masters the nonprehensile manipulation skill.

In order to improve the fabrication efficiency and performance of an ultrasonic transducer (UT), a particle swarm optimization (PSO) algorithm-based design method was established and combined with an electrically equivalent circuit model. The relationship between the design and performance parameters of the UT is described by an electrically equivalent circuit model. Optimality criteria were established according to the desired performance; then, the design parameters were iteratively optimized using a PSO algorithm. The Pb(Zr_xTi_1−x)O₃ (PZT) ceramic UT was designed by the proposed method to verify its effectiveness. A center frequency of 6 MHz and a bandwidth of −6 dB (70%) were the desired performance characteristics. The optimized thicknesses of the piezoelectric and matching layers were 255 μm and 102 μm. The experimental results agree with those determined by the equivalent circuit model, and the center frequency and −6 dB bandwidth of the fabricated UT were 6.3 MHz and 68.25%, respectively, which verifies the effectiveness of the developed optimization design method.