Search

Selective laser melting (SLM) is a powder-based additive manufacturing technology that can be used to fabricate high-density components with complex geometry. Several studies have investigated the process parameters that affect surface quality. However, most researchers have ignored the importance of the scanning strategy. In this study, the Taguchi method was used to investigate the relation between warpage and fundamental parameters (laser power, scanning speed, overlap, and scanning angle) to fabricate stable and undistorted specimens. Moreover, several scanning strategies (offset scanning, line scanning, meander scanning, meander scanning with hatch vector, and lightning scanning) were applied to explore the influences on surface quality. The results revealed that meander scanning and lightning scanning generated consistent specimens without large deformation. The process parameters, such as an increased 45° scanning direction and 30% overlap, optimized the surface quality. A lower scanning speed (500 mm/s) could generate lower Ra of specimens, with the exception of lightning scanning with an increased 45° scanning direction. This study may contribute to the growing understanding of the scanning strategy in SLM.

In recent years, system of systems resilience has been widely studied. System of systems has obvious resilience properties when considering dynamic reconfiguration in the following four parts: avoidance, survival, adaption and recovery. System of systems can be downgraded and recovered by reconfiguring resources to keep the performance output enough to satisfy the threshold under internal failure or external shocks. In other words, because of dynamic reconfiguration, system of systems has obvious characteristics of resilience. In this study, first, a new resilience model for systems and system of systems based on the performance threshold is proposed. Second, military system of systems is decomposed hierarchically, including system of systems–level, platform-level and system-level top-down, respectively. Third, a radar network of military system of systems is taken as a typical case. A performance model for a radar network under internal or external shocks is established based on the linear-Gauss-Poisson process in system of systems, and its parameters are discussed in detail. Finally, a typical 5-node radar network of formation air defense military system of systems is taken as an example to demonstrate proposed models and methods. The reliability and resilience loss are achieved by considering internal failure or external shocks, which can serve as a reference for evaluating and improving the effectiveness of system of systems.

Ball screws play a critical role in high-quality precision manufacturing. The use of machine learning and artificial intelligence for the diagnosis of machines’ health status is increasingly pertinent. The processing of big data originating from machine sensors is crucial. However, installing multiple sensors on the object requiring diagnosis may be costly. A sensorless strategy using built-in signals to determine the conditions of a hollow ball screw was deployed. Moreover, we evaluated the most discriminative parameters among fusion sensor signals by using Fisher’ criteria. A support vector machine (SVM) as diagnostic tool was used. In the absence of prominent characteristic features in data, the conventional SVM cannot classify the data well. To address this concern, we constructed a feature engineering for distinguishing features from the raw data to facilitate the SVM classification process well. In addition, we validated the physical phenomenon in realistic ball screw conditions through feature extraction. Experimental results demonstrated the average diagnostic accuracy levels for the ball screw preload, pretension, cooling system, and table payload were 98.91%, 94.08%, 91.69%, and 93.5%, respectively, after feature engineering was applied successfully.

Industry 4.0 accelerates the growth of unmanned technology that reduces the labor cost and creates high automation in manufacturing system. The automated guided vehicle which is capable of transferring materials or executing tasks without human intervention becomes a necessary system for modern unmanned factories. The study explores the guidance and control design to accomplish the common task of path-following control for unmanned ground vehicles (UGV). A complete design method is presented that includes the lateral-directional autopilot, the vector field guidance for path-following, and multi-sensor fusion. The lateral-directional autopilot produces the low-level control action, the higher level guidance indicates the course direction of UGV at every spatial point based on the lateral path error, and the accurate UGV position relies on the estimate obtained by dynamically fusing sensors with extended Kalman filter. The design parameters in every stage are analyzed theoretically first and then fine-tuned in practice. The process is clearly described in this study, and the field test results are discussed in details to verify the performance of the proposed method and demonstrate the superiority over others.

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.

This paper concerns the robust gain-scheduling control issue for autonomous path following systems with stochastic network-induced delay. Firstly, to effectively approximate the highly nonlinear tire dynamics, the linear fractional transformation formulations are employed to describe the tire cornering stiffness with a norm-bounded uncertainty. Secondly, by taking the data dropout and delay encountered in signal computation and transmission into account, a more generalized lumped delay form is proposed to unify the time-varying data dropout and network-induced delay. Moreover, a Markovian process is presented to describe the lumped delay as a stochastic distribution. Thirdly, to address the issue of varying vehicle velocity, a linear parameter varying model is established to capture vehicle lateral behaviors. Based on the stochastic stability theory, a new robust gain-scheduling path following control method is proposed for the autonomous vehicles. Finally, the experimental study is presented to bridge the gap between the theoretical and practical investigations on path following control of autonomous vehicles, and results validate the superior performance of the proposed method compared with existing works.