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According to the requirements of servicing and deorbiting the failure satellites, especially the tumbling ones on geosynchronous orbit, this paper aims to design a docking mechanism to capture these tumbling satellites in orbit, to analyze the dynamics of the docking system and to develop a new collision force-limited control method in various docking speeds.

The mechanism includes a cone-rod mechanism which captures the apogee engine with a full consideration of despinning and damping characteristics and a locking and releasing mechanism which rigidly connects the international standard interface ring (Marman rings, such as 937B, 1194 and 1194A mechanical interface). The docking mechanism was designed under-actuated, aimed to greatly reduce the difficulty of control and ensure the continuity, synchronization and force uniformity under the process of repeatedly capturing, despinning, locking and releasing the tumbling satellite. The dynamic model of docking mechanism was established, and the impact force was analyzed in the docking process. Furthermore, a collision detection and compliance control method is proposed by using the active force-limited Cartesian impedance control and passive damping mechanism design.

A variety of conditions were set for the docking kinematics and dynamics simulation. The simulation and low-speed docking experiment results showed that the force translation in the docking phase was stable, the mechanism design scheme was reasonable and feasible and the proposed force-limited Cartesian impedance control could detect the collision and keep the external force within the desired value.

The paper presents a universal docking mechanism and force-limited Cartesian impedance control approach to capture the tumbling non-cooperative satellite. The docking mechanism was designed under-actuated to greatly reduce the difficulty of control and ensure the continuity, synchronization and force uniformity. The dynamic model of docking mechanism was established. The impact force was controlled within desired value by using a combination of active force-limited control approach and passive damping mechanism.

The purpose of this paper is to investigate the effects of parameters on the liquid phase migration (LPM) during the freeze-form extrusion fabrication (FEF) process.

To carry out this study, three factors were systematically investigated using orthogonal design of experiments. These three parameters are the extrusion velocity, the extrusion interval time and the extrusion head length. An orthogonal array with nine test units was selected for the experiments. Range analysis and analysis of variance were used to analyze the data obtained by the orthogonal experiments to identify the order of significant factors on LPM.

It was found that the LPM decreased with the increase of extrusion velocity and increased with the lengthening of extrusion interval time and the length of the extrusion nozzle. The order of significant factors for the LPM were found to be extrusion velocity > extrusion nozzle length > extrusion interval time.

Using an orthogonal design of experiments and a statistical analysis method, the liquid content of extrudate can be predicted and appropriate process parameter values can be selected. This leads to the minimization of LPM during the FEF process. Also, this analysis method could be used to study the LPM in other paste extrusion processes.

This paper suggests that the factors have significant impact on LPM during FEF process. The following analysis in this paper is useful for FEF users when prediction of LPM is needed. This methodology could be easily applied to different materials and initial conditions for optimization of other FEF-type processes. The research can also help to get better understanding of LPM during the FEF process.

This paper aims to analyze a failure case of a P110 tube in a CO₂ flooding well.

The chemical composition, microstructure and mechanical properties of the failed P110 tubing steel were tested, and met the API Spec 5CT standard. The fractures were investigated by scanning electron microscopy and energy dispersive spectroscopy.

Fracture was induced by stress corrosion cracking (SCC) and that the stress concentration caused by the mechanical damage played an important role in the failure. The failure case is a SCC failure affected by mechanical damage and galvanic corrosion.

The effect of the infiltration of groundwater was studied in the failure case. The stress concentration caused by the mechanical damage played an important role in the failure.

This study attempts to demonstrate how a tourism attraction (i.e., museum) could establish its brand equity. It involves a case study on one of the most famous museums in Taiwan which involves an in-depth interview. The results show that the museum under investigation has established a clear brand identification and its brand communications but has a limited interpretation of its brand assets. Recommendations include strengthening its experiential propaganda, organizing large-scale intercity festivals, coordinating with other vendors to sell cultural products, increasing the number of professional exhibitions, and establishing a self-evaluation mechanism.

Offline reinforcement learning (RL) acquires effective policies by using prior collected large-scale data, while, in some scenarios, collecting data may be hard because it is time-consuming, expensive and dangerous, i.e. health care, autonomous driving, seeking a more efficient offline RL method. The purpose of the study is to introduce an algorithm, which attempts to sample the high-value transitions in the prioritized buffer, and uniformly sample from the normal experience buffer, improving sample efficiency of offline reinforcement learning, as well as alleviating the “extrapolation error” commonly arising in offline RL.

The authors propose a new structure of experience replay architecture, which consists of double experience replies, a prioritized experience replay and a normal experience replay, supplying samples for policy updates in different training phases. At the first training stage, the authors sample from prioritized experience replay according to the calculated priority of each transitions. At the second training stage, the authors sample from the normal experience replay uniformly. The combination of the two experience replies is initialized by the same offline data set.

The proposed method eliminates out-of-distribution problem in an offline RL regime, and promotes training by leveraging a new efficient experience replay. The authors evaluate their method on D4RL benchmark, and the results reveal that the algorithm can achieve superior performance over the state-of-the-art offline RL algorithm. The ablation study proves that the authors’ experience replay architecture plays an important role in terms of improving final performance, data-efficiency and training stability.

Because of the extra addition of prioritized experience replay, the proposed method increases the computational burden and has the risk of changing data distribution due to the combined sample strategy. Therefore, researchers are encouraged to use the experience replay block effectively and efficiently further.

Offline RL is susceptible to the quality and coverage of pre-collected data, which may be not easy to be collected from specific environment, demanding practitioners to handcraft behavior policy to interact with environment for gathering data.

The proposed approach focuses on the experience replay architecture for offline RL, and empirically demonstrates the superiority of the algorithm on data efficiency and final performance over conservative Q-learning across diverse D4RL tasks. In particular, the authors compare different variants of their experience replay block, and the experiments show that the stages, when to sample from the priority buffer, play an important role in the algorithm. The algorithm is easy to implement and can be combined with any Q-value approximation-based offline RL methods by minor adjustment.

The purpose of this paper is to study the functionality of additively manufactured (AM) parts, mainly depending on their dimensional accuracy and surface finish. However, the products manufactured using AM usually suffer from defects like roughness or uneven surfaces. This paper discusses the various surface quality improvement techniques, including how to reduce surface defects, surface roughness and dimensional accuracy of AM parts.

There are many different types of popular AM methods. Unfortunately, these AM methods are susceptible to different kinds of surface defects in the product. As a result, pre- and postprocessing efforts and control of various AM process parameters are needed to improve the surface quality and reduce surface roughness.

In this paper, the various surface quality improvement methods are categorized based on the type of materials, working principles of AM and types of finishing processes. They have been divided into chemical, thermal, mechanical and hybrid-based categories.

The review has evaluated the possibility of various surface finishing methods for enhancing the surface quality of AM parts. It has also discussed the research perspective of these methods for surface finishing of AM parts at micro- to nanolevel surface roughness and better dimensional accuracy.

This paper represents a comprehensive review of surface quality improvement methods for both metals and polymer-based AM parts.