Proceedings of the Institution of Mechanical Engineers,Part B: Journal of Engineering Manufacture

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.

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.

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.

This paper designed a 7-DOF redundant robot manipulator that can flexibly and efficiently pick-up random objects. The developed 7-DOF machine with an additional redundancy achieved great progress in terms of flexibility and efficiency in the operational space. A robot operating system (ROS) was used to configure the manipulator system’s software modules, supporting convenient system interface, appropriate movement control policy, and powerful hardware device management for better regulation of the manipulator’s motions. A 3D type Point Cloud Library (PCL) was utilized to perform a novel point cloud image pre-processing method that did not only reduce the point cloud number but also maintained the original quality. The results of the experiment showed that the estimation speed in object detection and recognition procedure improved significantly.

The redundant robot manipulator architecture with the two-stage search algorithm was able to find the optimal null space. Suitable parameters in D-H transformation of forward kinematics were selected to efficiently control and position the manipulator in the right posture. Meanwhile, the reverse kinematics estimated all angles of the joints through the known manipulator position, orientation, and redundancy. Finally, motion panning implementation of manipulator rapidly and successfully reached the random object position and automatically drew it up to approximate the desired target.

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.

The surge in competition among companies to acquire a more significant portion of the market as well as respecting customer preferences in high quality and diverse products result in a reduction of product life cycles. Accordingly, companies are under enormous pressure to introduce new high quality and diverse products on time. Assessing new product designs at the primary phases of new product development (NPD) is a necessary and complex activity that can considerably reduce the time and cost of introducing new products to the market. The current methods of evaluating new product conceptual designs, including employing decision-making methods based on subjective opinions of experts, utilizing simulation packages, and following trial-and-error approaches in prototyping, may be inefficient, very time-consuming, and costly. To overcome this issue, this paper develops a quantitative data-driven Multi-Criteria Decision-Making (MCDM) approach founded on the combination of an Artificial Neural Network (ANN) method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assess the new conceptual designs. So that the ANN method is utilized to predict the performance characteristics of new designs based on the related existed data of similar products, and TOPSIS is employed to score and rank different proposed alternatives designs. Finally, a case study of evaluating new product conceptual designs in an automotive research and development company is considered to demonstrate the performance and applicability of the proposed approach.

Quadruped bionic robot has a strong adaptability to the environment, compared with wheeled and tracked robots, it has superior motion performance, and has a wide range of application prospects in rescue and disaster relief, ground mine clearance, mountain transportation, so it has become a research hotspot all over the world. Leg structure is an important embodiment of the superior performance of quadruped robot, and it is also the key and difficult point of design. This article proposes a novel quadruped robot with waist structure, which can complete a variety of gait forms. Based on the theory of linkage mechanism, a novel leg structure is designed with anti-parallelogram mechanism, which improves the strength and stiffness of the robot. Using D-H description method, the kinematics analysis of this quadruped robot single leg is carried out. On this basis, in order to ensure the foot contact with the ground and achieve zero impact, polynomial programming is used to plan the foot trajectory of swing phase and support phase. Based on the static stability margin, the optimal static gait of the quadruped robot is planned. A co-simulation study has been carried out to investigate further the validity and effectiveness of the quadruped robot on gait. The simulation results clearly show the robot can walk steadily and its input and output meet the expected requirements. The solid prototype platform is built, and the trajectory planning experiment of single leg is carried out, and the foot trajectory of single leg is obtained by using laser tracker. The gait planning algorithm is applied to the whole robot, and the results show that the robot can walk according to the scheduled gait, which proves the effectiveness of the proposed algorithm.

Magnetic particle inspection is typically used to detect the magnetic leakage caused by defects. This method is mainly used to detect the surface and subsurface defects of ferromagnetic materials. The conventional detection method involves inspectors performing visual inspection under high-power ultraviolet light. However, the intense ultraviolet light can easily damage the eyes of the inspectors. Furthermore, the aforementioned process is not only time consuming but also susceptible to human errors. Therefore, this study developed an automated optical inspection system to perform magnetic particle inspection. Analysis of several image features revealed that a contour compactness between four and five can be used to distinguish defective and non-defective features effectively. The defect identification ability obtained with several input combinations of image features for neural networks was analyzed. The results revealed that a high identification ability can be achieved for defective features when the input combination of area, mean width, and compactness is used.

Multiple performance objectives in turn-mill multitasking machining are investigated using the Taguchi method combined with the fuzzy theory. Using these two methods, optimized processing parameters can be rapidly identified to obtain optimized dimensional accuracy and geometrical shape angle, thus reducing machining cost and time. Herein, control factors for determining the single objective optimization parameter using the Taguchi robust process L₉(3⁴) orthogonal table were spindle speed (rpm), feed (mm/min), C-axis brake pressure (kg/cm²), axial cutting depth (mm), with dimensional accuracy and geometrical shape angle as objective characteristics. Then, signal-to-noise ratios of different groups were generated by gray correlation according to the experimental sequence to obtain the gray correlation coefficient for the calculation of the multiple performance characteristic index (MPCI). The MPCI results demonstrated that optimized dimensional accuracy was 0.005 mm and optimized geometrical shape angle was 0.004°. The optimized MPCI parameters were A3 (4000 rpm), B3 (250 mm/min), C3 (30 kg/cm²), and D3 (1.5 mm). It can reduce the processing for burr elimination and tool wear reduction by MPCI optimized process parameters.