In this work, we present a boundary oriented graph embedding (BOGE) approach for the graph neural network to assist in rapid design and digital prototyping. The cantilever beam problem has been solved as an example to validate its potential of providing physical field results and optimized designs using only 10 ms. Providing shortcuts for both boundary elements and local neighbor elements, the BOGE approach can embed unstructured mesh elements into the graph and performs an efficient regression on large-scale triangular-mesh-based finite element analysis (FEA) results, which cannot be realized by other machine-learning-based surrogate methods. It has the potential to serve as a surrogate model for other boundary value problems. Focusing on the cantilever beam problem, the BOGE approach with 3-layer DeepGCN model achieves the regression with mean square error (MSE) of 0.011 706 (2.41% mean absolute percentage error) for stress field prediction and 0.002 735 MSE (with 1.58% elements having error larger than 0.01) for topological optimization. The overall concept of the BOGE approach paves the way for a general and efficient deep-learning-based FEA simulator that will benefit both industry and Computer Aided Design (CAD) design-related areas.

In this article, we present a novel 3-D descriptor, improved dexel representation (IDR), which assists to input holistic information from an engineering computer-aided design (CAD) model to convolutional neural network (CNN) based manufacturing applications. The IDR carries the model’s position, size, and surface information, which not only provides high resolution to small-scale local (machining) features, but also has the potential to reconstruct the original CAD model. Data conversion algorithms between IDR and other CAD models (mesh and NURBS model) are efficient. CNNs with IDR input can largely improve the prediction accuracy compared to other 3-D descriptors, which reaches 98.8% in the modified machining-process-identifier dataset and 100% on the FeatureNet style 3-class dataset. IDR benefits both the manufacturing industry and other CAD-related deep learning applications in engineering fields.

In recent years, surface texturing in micro-scale has been attempted on the surface of cutting tools for multiple purposes, e.g., cutting force reduction, prolonging life-span, anti-adhesion, etc. With respect to machinability and performance, micro-groove texture (MGT) has dominated in this field compared to other textured patterns. In this study, a novel volcano-like texture (VLT) was fabricated on the rake face of cemented carbide inserts (WC-Co, YG6) by fiber laser. The following cutting experiment tested the flat, MGT and VLT tools in turning aluminum alloy 6061. The effects of coolant and cutting conditions were investigated. In addition, a validated FEM model was employed to explore the distribution of stress and temperature fields in the tool-chip interface. The initial forming process of adhesion layer on rake face was investigated as well. The results indicated that lower cutting force and less adhesion can be achieved by small scale VLT. This study not only introduced VLT on cutting tools but also revealed its comprehensive performance.

In this research, we propose an Error Separation Technique (EST) based on optical fiber sensors for on-machine straightness error measurement. Two fiber optic Fabry–Pérot interferometers have been developed serving as two displacement sensors. The displacement distance is computed according to the reflected spectrum from interferometers, which can achieve a sub-micrometer resolution. The two-point method has been employed to separate the straightness error of the slides and the profile error of a fine-polished standard block. The spacing distance between two interferometers is determined by the diameter of optical fibers so that the EST's resolution has the potential to reach the sub-millimeter scale. In the experiment, the straightness error has been measured on a commercially available computer numerical control machine tool, and the measurement has been conducted on its x-axis. The spacing distance between two optical fiber sensors is 1.5 mm which equals the EST's resolution along the machine tool's x-axis. The separated profile error of the measured standard block is around 30 µm which has been verified by a high precision Coordinate Measurement Machine (CMM). The magnitude of the separated straightness error is around 40 µm. This technique is flexible and simple to be conducted, which can contribute to the micro-machine tool calibration and other straightness error applications.