Robotics and Computer-integrated Manufacturing最新文献

In sheet metal forming, the quality of a formed part is strongly influenced by the local lubrication conditions on the blank. Fluctuations in lubrication distribution can cause failures such as excessive thinning and cracks. Predicting these failures in real-time for the entire part is still a very challenging task. Machine learning (ML) based digital twins and advanced computing power offer new ways to analyze manufacturing processes inline in the shortest possible time. This study presents a digital twin for simulating a deep drawing process that incorporates an advanced ML model and optimization algorithm. Convolutional neural networks with RES-SE-U-Net architecture, were used to capture the full friction conditions on the blank. The ML model was trained with data from a calibrated finite element model. The ML model establishes a correlation between the local friction conditions across the blank and the quality of the drawn part. It accurately predicts the geometry and thinning of the formed part in real-time by assessing the friction conditions on the blank. A particle swarm optimization algorithm incorporates the ML model and provides tailored recommendations for adjusting local friction conditions to promptly correct detected quality deviations with minimal amount of additional lubricant. Experiments show that the ML model deployed on an industrial control system can predict part quality in real-time and recommend adjustments in case of quality deviation in 1.6 s. The error between prediction and ground truth is on average 0.16 mm for geometric accuracy and 0.02 % for thinning.

Facing the new generation intelligent manufacturing, traditional manufacturing models are transitioning towards large-scale customized productions, improving the efficiency and flexibility of complex manufacturing processes. This is crucial for enhancing the stability and core competitiveness of the manufacturing industry, and human-robot collaboration systems are an important means to achieve this goal. At present, mainstream manufacturing human-robot collaboration systems are modeled for specific scenarios and actions, with poor scalability and flexibility, making it difficult to flexibly handle actions beyond the set. Therefore, this article proposes a new human-robot collaboration framework based on action recognition and multi-scale control, designs 27 basic gesture actions for motion control, and constructs a robot control instruction library containing 70 different semantics based on these actions. By integrating static gesture recognition, dynamic action process recognition, and You-Only-Look-Once V5 object recognition and positioning technology, accurate recognition of various control actions has been achieved. The recognition accuracy of 27 types of static control actions has reached 100%, and the dynamic action recognition accuracy of the gearbox assembly process based on lightweight MF-AE-NNOBJ has reached 90%. This provides new ideas for simplifying the complexity of human-robot collaboration problems, improving system accuracy, efficiency, and stability.

In the process of metal cutting, realizing effective monitoring of tool wear is of great significance to ensure the quality of parts machining. To address the tool wear monitoring (TWM) problem, a tool wear monitoring method based on data-driven and physical output is proposed. The method divides two Physical models (PM) into multiple stages according to the tool wear in real machining scenarios, making the coefficients of PM variable. Meanwhile, by analyzing the monitoring capabilities of different PMs at each stage and fusing them, the PM's ability to deal with complex nonlinear relationships, which is difficult to handle, is improved, and the flexibility of the model is improved; The pre-processed signal data features were extracted, and the original features were fused and downscaled using Stacked Sparse Auto-Encoder (SSAE) networker to build a data-driven model (DDM). At the same time, the DDM is used as a guidance layer to guide the fused PM for the prediction of wear amount at each stage of the tool, which enhances the interpretability of the monitoring model. The experimental results show that the proposed method can realize the accurate monitoring of tool wear, which has a certain reference value for the flexible tool change in the actual metal-cutting process.

Advanced metalworking industries need high-performance materials to pursue the sustainable goal of reducing the consumption of hydrocarbons. The ability to work at elevated temperatures and resist environmental corrosion, among many other mechanical and physical properties, is also imperative for the operating conditions. Despite meeting this sector's physical and mechanical demands, some of these materials represent a strong challenge for their manufacturing. Most of those components are oversized, mainly in aeronautics, aerospace, and shipbuilding industries, and large machining centres must be used, which entails high investment costs. Since dimensional accuracy is paramount, and considering the usual characteristics of these alloys, robotics have been considered an economically viable way to carry out manufacturing. Challenges related to Tool-Wear (TW), Surface Roughness (SR), and dimensional accuracy are scrutinised alongside advancements in robot-assisted manufacturing technologies, striving to overcome these obstacles. The overarching objective of this consolidated overview delves into the critical intersection of robotic manufacturing technology, explicitly accentuating the up-to-date bid of compensation methods for robot-assisted manufacturing and high-performance materials for advanced metalworking industries. In the contemporary industrial milieu, robot-assisted manufacturing has emerged as a linchpin for technological progress and operational excellence worldwide. This paper will provide a comprehensive and concise summary tailored to beginners and seasoned practitioners. Moreover, it underscores the global importance of the topic by highlighting the invaluable contributions of experts in the field. In doing so, the paper elucidates the pivotal role played by these advancements in shaping the trajectory of modern manufacturing practices on a global scale.

Accurate tool wear prediction during machining is crucial to manufacturing since it will significantly influence tool life, machining efficiency, and workpiece quality. Although existing data-driven methods can achieve competitive performance in tool wear prediction, their main emphasis is on fixed operating conditions with sufficient training samples, which is impractical in engineering practice. This implies that predicting tool wear values under variable working conditions with insufficient data is still a challenge owing to the difference in data distributions in complex tool wear mechanisms. Besides, having no access to samples in new conditions is another challenge for tool wear prediction in engineering practice. To address these issues, we develop a hybrid information model-agnostic domain generalization (H-MADG) method to provide appropriate initial model parameters that can be fast adaptative to the new conditions after fine-tuning. Additionally, we construct hybrid information as model input by fusing process information with temporal properties derived by neural networks, and the hybrid information can offer more useful prior knowledge about the machining process. Experimental results on NASA milling data show that compared with contrastive techniques, the RMSE of the proposed H-MADG method is reduced by an average of 36.81 %, which can achieve a low average RMSE value of 0.0904 with 15 cases under five different network architectures. We also investigate several crucial impact factors of the H-MADG method and summarize corresponding analysis and suggestions.

Map construction is the initial step of mobile robots for their localization, navigation, and path planning in unknown environments. Considering the human-robot collaboration (HRC) scenarios in modern manufacturing, where the human workers’ capabilities are closely integrated with the efficiency and precision of robots in the same workspace, a map integrating geometric and semantic information is considered as the technical foundation for intelligent interactions between human workers and robots, such as motion planning, reasoning, and context-aware decision-making. Although different map construction methods have been proposed for mobile robots’ perception in the working environment, it is still a challenging task when applied to such human-robot collaborative manufacturing scenarios to achieve the afore-mentioned intelligent interactions between human workers and robots due to the poor integration of semantic information in the constructed map. On the one hand, due to the lack of ability for differentiating the dynamic objects, the mobile robot might sometimes wrongly use the dynamic objects as the spatial references to calculate the pose transformation between the two successive frames, which negatively affects the accuracy of the robot's localization and pose estimation. On the other hand, the map that integrates both the geometric and semantic information can hardly be constructed in real-time, which cannot provide an effective support for the real-time reasoning and decision making during the human-robot collaboration process.

This study proposes a novel map construction approach containing semantic information generation, geometric information generation, and semantic & geometric information fusion modules, which enables the integration of the semantic and geometric information in the constructed map. First, the semantic information generation module analyzes the captured images of the dynamic working environment, eliminates the features of dynamic objects, and generates the semantic information of the static objects. Meanwhile, the geometric information generation module is adopted to generate the accurate geometric information of the robot's motion plane by using the environment data. Finally, a map integrating semantic and geometric information in real-time can be constructed by the semantic & geometric fusion module. The experimental results demonstrate the effectiveness of the proposed semantic map construction approach.

In practice, the process of robot assembly in an unstructured environment faces difficulties due to the presence of unpredictable environmental errors related to vision and pose. Therefore, to minimize the uncertain environmental errors during the robotic assembly process in an unstructured environment, several studies have considered a reinforcement learning (RL)-based approach. However, if assembly parts are changed, it becomes difficult to apply RL-based methods to assemble various parts because additional learning may be required. Especially in the case of connector assembly, fine-tuning is essential because the shape changes depending on the type of connector. In this study, we propose a similar assembly state discriminator that transforms the state information (force, velocity, and RGB image) of reinforcement learning into generalized features to respond various types of connector assembly tasks. This method processes the data to include essential features for assembly regardless of connector type. By learning the RL model with the processed data using this method, the RL model trained for a specific connector can be efficiently applied to other types of connectors without fine-tuning. The assembly success rate for the 7 types of connectors (Harting, HDMI, USB, power, air jack, banana plug and PCIE) using the proposed method was demonstrated to be over 96 %.

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

Manufacturing is transformed towards smart manufacturing, entering a new data-driven era fueled by digital technologies. The resulting Smart Manufacturing Systems (SMS) gather extensive amounts of diverse data, thanks to the growing number of sensors and rapid advances in sensing technologies. Among the various data types available in SMS settings, time-series data plays a pivotal role. Hence, Time-Series Classification (TSC) emerges as a crucial task in this domain. Over the past decade, researchers have introduced numerous methods for TSC, necessitating not only algorithmic development and analysis but also validation and empirical comparison. This dual approach holds substantial value for practitioners by streamlining choices and revealing insights into models’ strengths and weaknesses. The objective of this study is to fill this gap by providing a rigorous experimental evaluation of the state-of-the-art Machine Learning (ML) and Deep Learning (DL) algorithms for TSC tasks in manufacturing and industrial settings. We first explored and compiled a comprehensive list of more than 92 state-of-the-art algorithms from both TSC and manufacturing literature. Following this, we methodologically selected the 36 most representative algorithms from this list. To evaluate their performance across various manufacturing classification tasks, we curated a set of 22 manufacturing datasets, representative of different characteristics that cover diverse manufacturing problems. Subsequently, we implemented and evaluated the algorithms on the manufacturing benchmark datasets, and analyzed the results for each dataset. Based on the results, ResNet, DrCIF, InceptionTime, and ARSENAL emerged as the top-performing algorithms, boasting an average accuracy of over 96.6 % across all 22 manufacturing TSC datasets. These findings underscore the robustness, efficiency, scalability, and effectiveness of convolutional kernels in capturing temporal features in time-series data collected from manufacturing systems for TSC tasks, as three out of the top four performing algorithms leverage these kernels for feature extraction. Additionally, LSTM, BiLSTM, and TS-LSTM algorithms deserve recognition for their effectiveness in capturing features within manufacturing time-series data using RNN-based structures.

Vision-based weld seam tracking has become one of the key technologies to realize intelligent robotic welding, and weld deviation detection is an essential step. However, accurate and robust detection of weld deviations during the microwelding of ultrathin metal foils remains a significant challenge. This challenge can be attributed to the fusion zone at the mesoscopic scale and the complex time-varying interference (pulsed arcs and reflected light from the workpiece surface). In this paper, an intelligent seam tracking approach for foils joining based on spatial–temporal deep learning from molten pool serial images is proposed. More specifically, a microscopic passive vision sensor is designed to capture molten pool and seam trajectory images under pulsed arc lights. A 3D convolutional neural network (3DCNN) and long short-term memory (LSTM)-based welding torch offset prediction network (WTOP-net) is established to implement highly accurate deviation prediction by capturing long-term dependence of spatial–temporal features. Then, expert knowledge is further incorporated into the spatio-temporal features to improve the robustness of the model. In addition, the slime mould algorithm (SMA) is used to prevent local optima and improve accuracy, efficiency of WTOP-net. The experimental results indicate that the maximum error detected by our method fluctuates within $\pm$ 0.08 mm and the average error is within $\pm$ 0.011 mm when joining two 0.12 mm thickness stainless steel diaphragms. The proposed approach provides a basis for automated robotic seam tracking and intelligent precision manufacturing of ultrathin sheets welded components in aerospace and other fields.