The hyper redundancy and flexibility of continuum robots have made them attractive to replace conventional rigid-link robots in many applications. Although the robot body can be bent to different directions as needed, rotation along the backbone is not a feature available in many continuum robot designs. In this article, we propose a pneumatic-driven, dual-segment continuum robot with the capability of providing continuous rotation along the deformed backbone (CRADB) at a particular point. A slip ring was employed to enable rapid coupling/decoupling between the air supply and different chambers of the robot, while a stepper motor with adjustable speed was adopted to provide the additional rotational motion. A novel control scheme based on optimization was developed to compute the required shape configuration under different modes. Control schemes toward fast deformation motion and CRADB motion have been, respectively, built. Experiments were conducted to compare the performance in positioning the robot’s tip with and without the additional degree of freedom. The ability to enable continuous rotation after positioning at different points was also tested to demonstrate its performance in various potential applications.

In this paper, an intelligent-design method to deal with conceptual optimization is proposed for the decisive impact of the concept on the product-development cycle cost and performance. On the basis of matter-element analysis, an effective functional-structure combination model satisfying multiple constraints is first established, which maps the product characteristics obtained by expert research and customer-requirements analysis of the function and structure domain. Then, the Evolutionary Game Algorithm (EGA) was utilized to solve the model, in which a strategy-combination space is mapped to the solution-search space of the conceptual-solution problem, and the game-utility function is mapped to the objective functions of concept evaluation. Constant disturbance and Best-Response Correspondence were applied cross-repeatedly until the optimal equilibrium Pareto state corresponding to the global optimal solution was obtained. Finally, the method was simulated on MATLAB 8.3 and applied to the design for fixed winch hoist, which greatly shortens its design cycle.

Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry.

Blood pressure (BP) can reflect many physiological characteristics, and timely monitoring of it can somehow prevent hypertension, asphyxia and other diseases. With the development of clinical medical technology, the accuracy requirements of the intelligent physiological monitoring equipment for predicting the physiological characteristics of BP are gradually increasing. This paper proposes a BP prediction model based on Long Short Term Memory Networks (LSTM-NN), which makes full use of the efficient processing characteristics of LSTM for time series information and accurately predicts the systolic BP and diastolic BP. The primitive photoplethysmographic pulse wave (PPW) signal and actual BP data in different time periods and different states were collected on 6 adult goats simultaneously. The blood flow changes were stimulated by injection of adrenaline to obtain a wide range of raw data to improve the generalization ability of the model. The clinical features of each PPW cycle were introduced into the LSTM model for training and prediction to resolve actual systolic BP and diastolic BP. Comparing the model with the prediction effect of BP neural network (BP-NN) model, the result shows that the prediction accuracy of LSTM model is high and the robustness is strong. The maximum error values for systolic and diastolic pressure prediction are 1.05mmHg and 1.8mmHg, respectively.

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions.