Nowadays, robots have been widely used in various manufacturing assemblies. However, in the mobile phone flexible printed circuit (FPC) assembly task, robots are still far from trustworthy because the connector and receiver of FPC are very small in size, and detection for them is influenced by the partial occlusion of the robot end manipulator in operation. This makes it difficult to measure the spatial relationship between the connector and receiver in robot picking and buckling. To this end, this work proposes a Digit Twin (DT)-driven measurement method for partial occlusion FPC assembly. The contributions include: (1) we introduce a homography-based strategy to calibrate the FPC placement panel and its virtual placement panel in digit twin. It makes the partial occlusion views in physical space visible in digital twin within tolerable errors. This helps to improve the measurement by eliminating the observation occlusion which originally exits in physical space; (2) we propose a DT-driven reward function measured by the distance and rotation difference between the FPC connector hold by the manipulator and its ideal position in the digital twin. This makes the deep architecture can learn the actions guided by the measurements of the FPC positions in physical space and digital twin simultaneously. The experiments are carried out on a practical assembly platform for the mobile phone Redmi Note 11. With the number of failed trails limited, the proposed network obviously outperforms the pure vision-based measurement and the network without DT support. The proposed method provides a primitive but novel measurement strategy for mobile phone FPC assembly. It can be applied to the real mobile phone assembly lines to reduce the labor burden in the near future.

The traditional Deep Deterministic Policy Gradient (DDPG) algorithm frequently exhibits a notable reduction in success rate when transferred to new environments after being trained in complex simulation settings. To address these issues, this paper adopts a Multi-Environment (Multi-Env) parallel training approach and integrates Multi-Head Attention (MHA) and Prioritized Experience Replay (PER) into the DDPG framework, optimizing the reward function to form the MAP-DDPG algorithm. This approach enhances the algorithm’s generalization capability and execution efficiency. Through comparative training and testing of the DDPG and MAP-DDPG algorithms in both simulation and real-world environments, the experimental results demonstrate that MAP-DDPG significantly improves generalization and execution efficiency over the DDPG algorithm. Specifically, in simulation environment tests, the MAP-DDPG algorithm achieved an average 30% increase in success rate and reduced the average time to reach the target point by 23.7 s compared to the DDPG algorithm. These results indicate that the MAP-DDPG algorithm significantly enhances path planning generalization and execution efficiency, providing a more effective solution for path planning in complex environments.

The effect of Mg alloying on microstructure and mechanical properties at 300 °C of Al–5Cu–1Mn–0.5Ni heat-resistant aluminum alloy was investigated by microstructure observation and tensile test. Alloying with 0.5 wt%Mg addition considerably increases the yield strength at 300 °C of samples at as-cast, as-solutionized and as-aged states. It also greatly enhances the strengthening effects from solution and ageing treatments, however, simultaneously, decreases the elongation greatly. During the solidification process of the alloy with Mg addition, a great amount of whisker-like Al–Cu–Mg compound phase is formed at the eutectic boundary zones in the form of ridge configuration, which is determined as AlCuMg intermetallic compound by TEM. During the solution treatment, this whisker-like AlCuMg phase is completely dissolved into Al matrix that leads to a remarkable rise in the increment of yield strength at 300 °C by solution strengthening. The alloying of Mg has a significant influence on the precipitation behavior during the ageing treatment, for examples, promoting the formation of finer θ′′ particles and a great number of Cu–Mg atomic clusters. During high temperature tensile test, theses clusters evolve into fine cube-like Al₅Cu₂Mg particles with orthorhombic structure. It is the significant microstructure evolution induced by alloying of Mg that leads to the great rise in yield strength at 300 °C. Addition of Mg in Al–Cu–Mn–Ni alloy dose not lead to the formation of meta-stable or stable S phase, but whisker-like AlCuMg and cube-like Al₅Cu₂Mg compounds are determined by TEM.

In the industrial assembly of flexible printed circuits (FPCs), the FPC connectors and receivers are required to connect successfully with the least possible buckling times. However, precise and autonomous positioning of the FPC connectors is still a great challenge, as they are very small and visually blocked during assembly. This article proposes a strategy for the industrial assembly of FPC by: 1) unlike the traditional vision based methods, the task of FPC position identification in this work is considered as a haptic signals alignment problem. We mount a haptic sensor at the top end of the robotic arm manipulator and record various mismatched haptic signals around the ideal FPC connector position; 2) converting both the correct and incorrect haptic signals into images; and 3) we introduce a novel deep Siamese-network (DSN) structure to align the incorrect haptic images to the ground truth. The experiments are carried out on a practical FPC assembly platform for the Redmi Note 11 mobile phone. It is found that the haptic images combined with DSN can significantly improve the FPC location accuracy. When the buckle times are limited to 10, 5, and 1, the proposed method outperforms the state-of-the-art methods in a realistic FPC assembly platform.

The goal of Few Shot Object Detection (FSOD) task is to effectively train and deploy object detection models even in small datasets, in order to achieve high-precision object detection results. Due to the few shot size of the dataset, the model may have difficulty learning sufficiently generalized feature representations, which can lead to overfitting or underfitting issues. Specifically, the model performs well during training, but shows a significant poor performance on test. That's because model overfits the noise and specific samples of the data in the training set, resulting in the inability to generalize to new data. Secondly, in the case of few shots, the target category scale in the dataset is single, making it difficult for the model to distinguish the same target at different scales, which affects detection accuracy. In order to effectively perform FSOD tasks, this paper proposes a meta learning FSOD algorithm based on PRN-FPN. Firstly, a data augmentation module is proposed to address the issue of data scarcity in few shot situations. The module includes three conversion strategies: color, color, and histogram equalization. Combining these three strategies can effectively increase the diversity of scarce data. In order to improve the algorithm's recognition and detection of targets of the same category but different scales, a Predictor head Removing Network with Feature Pyramid Network (PRN FPN) module is proposed. The module adds CARAFE Feature Pyramid Network on the basis of Predictor head Removal Network (RPN), and has the ability to detect few shot objects at multiple scales by fusing features at different depths. Finally, the algorithm was trained and tested on the publicly available dataset Pascal VOC, and the experimental results showed its effectiveness.

At present, the smart assembly of mobile phones is a challenging task. The assembly process will be affected by console vibration, illumination changes, and end self-occlusion, and it is difficult to locate directly through visual methods. In order to solve the above problems, this paper proposes a multi-modal skill learning model for improving the accuracy of mobile phone assembly. By fusing visual and tactile information, the deep learning model is used to guide the intelligent assembly of robots to improve the accuracy and success rate of mobile phone assembly. The method in this paper has carried out the experiment of flexible flat cable assembly on Redmi note 11, and can ensure the assembly error of the flexible flat cable X and Y axes within 0.3mm. This method is expected to be applied in a real assembly environment, improve the success rate of intelligent assembly and reduce the cost of mobile phone assembly.