Product assembly involves extensive production data that is characterized by high dimensionality, multiple samples, and data imbalance. The article proposes an edge computing-based framework for monitoring product assembly quality in industrial Internet of Things. Edge computing technology relieves the pressure of aggregating enormous amounts of data to cloud center for processing. To address the problem of data imbalance, we compared five sampling methods: Borderline SMOTE, Random Downsampling, Random Upsampling, SMOTE, and ADASYN. Finally, the quality monitoring model SMOTE-XGBoost is proposed, and the hyperparameters of the model are optimized by using the Grid Search method. The proposed framework and quality control methodology were applied to an assembly line of IGBT modules for the traction system, and the validity of the model was experimentally verified.

Adaptive compliance control is critical for rehabilitation robots to cope with the varying rehabilitation needs and enhance training safety. This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot (WLLRR), which includes a high-level trajectory planner and a low-level position controller. Dynamic motion primitives (DMPs) and a trajectory deformation algorithm (TDA) are integrated into the high-level trajectory planner, generating multi-joint synchronized desired trajectories through physical human-robot interaction (pHRI). In particular, the amplitude modulation factor of DMPs and the deformation factor of TDA are adapted by a multi-modal adaptive regulator, achieving smooth switching of human-dominant mode, robot-dominant mode, and soft-stop mode. Besides, a linear active disturbance rejection controller is designed as the low-level position controller. Four healthy participants and two stroke survivors are recruited to conduct robot-assisted walking experiments using the TD-MACCS. The results show that the TD-MACCS can smoothly switch three control modes while guaranteeing trajectory tracking accuracy. Moreover, we find that appropriately increasing the upper bound of the deformation factor can enhance the average walking speed (AWS) and root mean square of trajectory deviation (RMSTD).

With the emergence of intelligent manufacturing, new-generation information technologies such as big data and artificial intelligence are rapidly integrating with the manufacturing industry. One of the primary applications is to assist manufacturing plants in predicting product quality. Traditional predictive models primarily focus on establishing high-precision classification or regression models, with less emphasis on imbalanced data. This is a specific but common scenario in practical industrial environments concerning quality prediction. A SMOTE-XGboost quality prediction active control method based on joint optimization hyperparameters is proposed to address the problem of imbalanced data classification in product quality prediction. In addition, edge computing technology is introduced to address issues in industrial manufacturing, such as the large bandwidth load and resource limitations associated with traditional cloud computing models. Finally, the practicality and effectiveness of the proposed method are validated through a case study of the brake disc production line. Experimental results indicate that the proposed method outperforms other classification methods in brake disc quality prediction.

As the energy cost accounts for about one-third of the total manufacturing cost, there is great significance in evaluating and managing energy consumption in manufacturing processes. The energy consumption during multi-axis end milling, which represents a large part of the industrial energy costs, is usually extraordinarily large, especially for complex free-form surfaces requiring multi-finish-machining. To obtain the most efficient tool path, the tool orientation is adjusted to obtain the largest cutting stripe width at each cutter contact point. However, the use of excessive driving energy consumption and cutting energy to obtain the largest cutting stripe width may reduce the energy efficiency of the tool path. To solve this problem, the geometry features of the tool path are analyzed firstly, and the global energy consumption analysis, which includes a cutting energy analysis and driving energy analysis, is conducted. The discrete energy consumption path model is constructed to find the most energy-efficient tool orientation sequence for a tool path. Finally, contrast experiments are carried out to validate the proposed method.

The energy consumption optimization of edge devices in the mobile edge computing environment is mainly based on computational offload strategy. Most of the current common computing offload strategies only consider a single computing resource and do not comprehensively consider different kinds of computing resources in mobile edge computing environments, which cannot fully reduce the energy consumption of edge devices under the condition of ensuring response time constraints. To solve this problem, a multi-resource computing unloading energy consumption model is proposed in the mobile edge computing environment, and a new fitness calculation method for evaluating the energy consumption of edge devices is designed. Combined with the workflow management system, a multi-resource computing offloading particle swarm optimization task scheduling algorithm for energy consumption optimization in mobile edge computing is proposed. The algorithm can fully reduce the energy consumption of mobile terminals under the condition of considering the response time constraint. Experiments show that, compared with the existing four algorithms, the task scheduling algorithm corresponding to the new strategy has stable convergence and optimal fitness. Under the constraint of user response time, the energy consumption of edge devices in the task scheduling scheme is better than the other four unloading strategies.

Compliant physical human-robot interaction (pHRI), as well as the accuracy and robustness of trajectory tracking, are crucial for rehabilitation robots. In this paper, a new sitting/lying lower limb rehabilitation robot, SUT-SLLRR, has been designed for patients with lower extremity motor dysfunction. A dynamic movement primitives modulation-based compliance control strategy (DMPM-CCS) has been proposed for the SUT-SLLRR. The high-level trajectory planner consists of the trajectory generator based on dynamic movement primitives (DMPs), acceleration layer modulation generator, and velocity layer modulation generator, which can reshape the reference trajectory to generate desired trajectory within a constrained joint space through pHRI. Besides, the linear active disturbance rejection controller (LADRC) is adopted as the low-level position controller to ensure that each joint can accurately and robustness track the desired trajectory under internal and external disturbances. Simulation and experimental results indicate that the proposed strategy can provide the compliant pHRI within the constrained joint space and ensure the accuracy and robustness of trajectory tracking.