Current non-Lambertian photometric stereo methods generally require a large number of images to ensure accurate surface normal estimation. To achieve accurate surface normal recovery under a sparse set of lights, this paper proposes a non-Lambertian photometric stereo network based on a derived inverse reflectance model with collocated light. The model is deduced using monotonicity of isotropic reflectance and the univariate property of collocated light to decouple the surface normal from the reflectance function. Thus, the surface normal can be estimated by three steps, i.e., model fitting, shadow rejection, and normal estimation. We leverage a supervised deep learning technique to enhance the shadow rejection ability and the flexibility of the inverse reflectance model. Shadows are handled through max-pooling. Information from a neighborhood image patch is utilized to improve the flexibility to various reflectances. Experiments using both synthetic and real images demonstrate that the proposed method achieves state-of-the-art accuracy in surface normal estimation.

High-bandwidth nano-positioning stages (NPSs) have boosted the advancement of modern ultra-precise, ultra-fast measurement and manufacturing technologies owing to their fast dynamic response, high stiffness and nanoscale resolution. However, the nonlinear actuation, lightly damped resonance and multi-axis cross-coupling effect bring significant challenges to the design, modeling and control of high-bandwidth NPSs. Consequently, numerous advanced works have been reported over the past decades to address these challenges. Here, this article provides a comprehensive review of high-bandwidth NPSs, which covers four representative aspects including mechanical design, system modeling, parameters optimization and high-bandwidth motion control. Besides, representative high-bandwidth NPSs applied to atomic force microscope and fast tool servo are highlighted. By providing an extensive overview of the design procedure for high-bandwidth NPSs, this review aims to offer a systemic solution for achieving operation with high speed, high accuracy and high resolution. Furthermore, remaining difficulties along with future developments in this fields are concluded and discussed.

Precision measurement and reconstruction of detailed surfaces topography is a challenging task for non-diffuse complex parts. Although coordinate measurement machines (CMM) with the touch-trigger probe are widely used in current industry, the measurement efficiency limits their application in the measurement of complex surfaces. This article proposes a multisensor data fusion strategy by integrating the technical merits of CMM and photometric stereo (PS) to achieve multiscale reconstruction of a complex surface with high efficiency. Considering the complementary measurement characteristics of the two approaches, the sparse points from CMM are used to provide global shape information, and the high-resolution surface normal map from PS is used to provide local detailed structure. A multistage neural network is then proposed to fuse these two kinds of modality information such that the global features from the sparse points and the local features from the surface normal map are fused in a coarse-to-fine multistage process so as to make the training process more stable and the reconstruction more accurate. To enhance the generality of the fusion neural network, a synthetic training data set is also designed to include a large variety of multiscale features enriched surfaces. Experiments are conducted to verify the effectiveness of the proposed multisensor fusion strategy in accurate reconstruction of complex surfaces with high efficiency.

This paper presents an investigation of the mechanism of the brittle–ductile cutting mode transition from the perspective of the mechanics. A mechanistic model is proposed to analyze the relationship between undeformed chip thickness, deformation, and stress levels in the elastic stage of the periodic chip formation process, regarding whether brittle or ductile mode deformation is to follow the elastic stage. It is revealed that, the distance of tool advancement required to induce the same level of compressive stress decreases with undeformed chip thickness, and thereby the tensile stress below and behind the tool decreases with undeformed chip thickness. As a result, the tensile stress becomes lower than the critical tensile stress for brittle fracture when the undeformed chip thickness becomes sufficiently small, enabling the brittle–ductile cutting mode transition. The finite element method is employed to verify the analysis of the mechanics on a typical brittle material 6H silicon carbide, and confirmed that the distance of the tool advancement required to induce the same level of compressive stress becomes smaller when the undeformed chip thickness decreases, and consequently smaller tensile stress is induced below and behind the tool. The critical undeformed chip thicknesses for brittle–ductile cutting mode transition are estimated according to the proposed mechanics, and are verified by plunge cutting experiments in a few crystal directions. This study should contribute to better understanding of the mechanism of brittle–ductile cutting mode transition and the ultra-precision machining of brittle materials.

Coordinate measuring machines are widely used in the precision measurement of manufacturing workpieces. However, the nature of a point-by-point probing characteristic limits their efficiency in the measurement of complex parts, which normally requires dense sampling points for fully evaluating machining errors with high fidelity. To address this problem, this article proposes a generative model-driven sampling strategy to reduce the number of sampling points while preserving the measurement accuracy. Specifically, the reconstruction of surface errors with sparse sampling is transformed as a point cloud super-resolution task, which constructs a generative model to estimate accurate dense results from sparse sampled data. A multiscale neural network architecture is designed to achieve reconstruction, and the fractional Brownian motion is applied to simulate machining errors and synthesize a large-scale dataset for model training. The generalized neural model can then utilize sparse measurements to reconstruct global machining errors, which dramatically reduces the sampling time and increases measurement efficiency. Both computer simulations and actual measurements are carried out to verify the effectiveness of the proposed method.