Robotics and Computer-integrated Manufacturing最新文献

Robotic machining could provide a solution for removing supports from metal additive manufactured workpieces, replacing labor-intensive work. However, the robot’s intrinsic weaknesses of low positioning accuracy and structural rigidity primarily restrict its applications. Improving the accuracy of robotic machining remains an unresolved issue. A mixed solution is proposed, in which a portable CNC machine with the capability of visual feature recognition is equipped with a universal industrial robot. The robot implements positioning motions in a large space, while the portable CNC fulfills accurate machining motions on a local feature of the workpiece. A sizeable weight of the portable CNC exerts a moderate load on the industrial robot’s joints, increasing joint stiffness. The mixed machining system exhibits high accuracy and stiffness when milling a steel/titanium alloy workpiece, achieving tolerances up to ±0.04 mm on a 60×80 mm U-shaped path without exciting any structural vibration modes. When the dimension of the workpiece exceeds the machining range of the portable CNC, a combined algorithm of coarse-fine registration based visual identification and robot error compensation is designed to align the spatial coordinates of the machining motion with that of the positioning motion, thereby extending the machining range with high accuracy. Through the proposed mixed robot machining method, experiments of doubling the machining range have been done to verify that the mixed machining robotic system is able to slot a 550 mm-long path with accuracy of ±0.1 mm. Furthermore, the mixed robotic machining system is applied to recognize and remove multiple supports of lattices, grids and ribs from a titanium-alloy additive manufactured thin-wall workpiece with high accuracy and high efficiency.

Visual recognition of weld beads is essential for post-weld robotic grinding. The recognition of thin-walled weld bead boundary, especially the backside boundary, remains challenging due to the diverse features such as debris, misalignment, and deformation. Based on point cloud from a laser scanner, we present a robust and accurate backside boundary recognition method for girth weld beads of thin-walled pipes. A boundary point extraction method is designed based on an adaptive sliding window model. Without prior morphology features, the influence of misalignment and deformation on the accuracy of boundary point recognition is greatly reduced by the local model matching strategy. Leveraging the correlation among overall weld bead features, an anomalous boundary point recognition and correction method based on DBSCAN clustering is proposed to further enhance robustness. A series of validation experiments were conducted by the obtained backside point cloud data inside a girth weld pipe, and our proposed method showed a high accuracy and a high robustness to misalignment, deformation and debris features.

The process design intent is the concentration of the technologists’ design cognitive process which contains the experiential knowledge and skills. It can reproduce technologists’ design thinking process in process design and provides guidance and interpretability for the generation of process results. The machining process route, as a core component of a part's entire manufacturing process, contains substantial process design intent. If the process design intent embedded in the existing process route can be explicitly identified, subsequent technologists will be able to learn and understand the original designers’ thinking, methodologies, and intents. This understanding enables effective reuse of design thinking and logic in the process design of new parts, rather than merely reusing data. It can also promote the propagation of the expertise and skills inherent in the process design intent. However, existing research on process design intent lacks a detailed explanation of its formation and specific structure from the design cognition perspective, making it challenging to effectively predict the process design intent containing interpretable empirical knowledge in the process route. To address this issue, this paper provides a method for predicting process design intent in the process route using heterogeneous graph convolutional networks. First, the heterogeneous graph is used to represent the parts and their associated process routes in the dataset. The nodes in the graph are then labeled based on accumulated and summarized process design intent. The prediction of process design intent in the process route is then converted into a node classification issue with heterogeneous graphs. A node classification network model is constructed using a heterogeneous graph convolutional network where the input is the created heterogeneous graph, and the output is the design reason contained in the machining feature and the intent cognition embedded in the working step, both of which are part of the process design intent. After training, the proposed model accurately predicted design reasons for machining features and intent cognitions for working steps (95.13 % and 96.85 %, respectively). Finally, examples of actual process routes are analyzed to verify the method's feasibility and reliability. The method given in this article can help technologists gain a deeper understanding of process route generation, hence improving their process design capabilities.

Human–robot collaborative polishing can integrate the capabilities of humans and automation to deal with complex polishing tasks. Traditional impedance-control-based human–robot collaboration (HRC) requires operators to physically interact with robots for a good polishing performance, which brings unsafety to operators. To address this issue, a corrective shared control architecture using haptic feedback is proposed in this paper, where the direct force-reflection is used to guarantee the exact human-intention intervention. The proposed control architecture is designed with two layers: (i) the transparency layer in which the direct force-reflection and the human–robot collaborative polishing strategy are implemented; (ii) the passivity layer in which two energy tanks are designed and endowed with master and slave sides and a coupling energy scaling policy is employed to guarantee the passivity of the whole system. Under the proposed architecture, the constant force is adopted to polish normal areas of workpieces, and corrective force based on human intention is applied to deal with unexpected issues. Finally, two groups of experiments are conducted to evaluate the proposed architecture from two aspects: polishing effect and user experience.