Fast inspection of a defect is a challenging task in mass production of curved surfaces, and photometric stereo (PS) utilizing multiple images from a single camera under a number of different illumination directions is a promising technique for this task due to its high sensitivity to surface normal perturbations. This paper adapts conventional PS and extends the technique to the inspection of non-Lambertian surfaces with high accuracy and efficiency. A data-driven PS is presented by establishing the Gaussian process (GP) model to represent the nonlinear reflectance behavior of various materials based on measured reflectance data sets. With the trained GP model, the surface normal can be estimated in two steps: prediction of bidirectional reflectance distribution function values under different light directions and the subsequent least-squares estimation of a surface normal. Comparison tests with other algorithms on the Mitsubishi Electric Research Laboratories data set and real workpieces with non-Lambertian materials show the superior accuracy and efficiency of the proposed method in surface normal estimation. After the surface normal of the workpiece is recovered, the defects can be detected by filtering out the perturbation of the surface normal. Experiments on steel and glossy polyester workpieces validate the efficacy of the proposed approach in detecting defects on curved non-Lambertian surfaces.

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.

Although four-dimensional (4D) light field imaging has many advantages over traditional two-dimensional (2D) imaging, its high computation cost often hinders the application of this technique in many fields, such as object detection and tracking. This paper presents a hybrid method to accelerate the object detection in light field imaging by integrating the deep learning with the depth estimation algorithm. The method takes full advantage of computation imaging of the light field to generate an all-in-focus image, a series of focal stacks, and multi-view images at the same time, and convolutional neural network and defocusing are consequently used to perform initial detection of the objects in three-dimensional (3D) space. The estimated depths of the detected objects are further optimized based on multi-baseline super-resolution stereo matching while efficiency is maintained, as well by compressing the searching space of the disparity. Experimental studies are conducted to demonstrate the effectiveness of the proposed method.

This paper presents a Gaussian process based Bayesian inference system for the realization of intelligent surface measurement on multi-sensor instruments. The system considers the surface measurement as a time series data collection process, and the Gaussian process is used as mathematical foundation to establish an inferring plausible model to aid the measurement process via multi-feature classification and multi-dataset regression. Multi-feature classification extracts and classifies the geometric features of the measured surfaces at different scales to design an appropriate composite covariance kernel and corresponding initial sampling strategy. Multi-dataset regression takes the designed covariance kernel as input to fuse the multi-sensor measured datasets with Gaussian process model, which is further used to adaptively refine the initial sampling strategy by taking the credibility of the fused model as the critical sampling criteria. Hence, intelligent sampling can be realized with consecutive learning process with full Bayesian treatment. The statistical nature of the Gaussian process model combined with various powerful covariance kernel functions offer the system great flexibility for different kinds of complex surfaces.

The widely used amorphous silicon material is often a deposited one. It is mainly applied in the electronic industry and shows to be a promising candidate for optical devices. The mechanical cutting of deposited amorphous silicon has not been reported so far, while ultra-precision machining of such material for optical devices can be superior to a lithographic approach. In this article, for the first time, cutting properties of deposited amorphous silicon are compared with those of single crystal silicon and polycrystalline silicon, using plunge cut experiment. It is found that the critical depth of cut (CDC) and cutting forces for the amorphous silicon are close to those of single crystalline silicon in the easy-to-cut orientation. In comparison to the polycrystalline silicon, amorphous silicon has higher CDC and lower cutting force. This may allow machining of amorphous optical devices on different semiconductors and/or moulds on various materials with enhanced and isotropic CDC, reduced tool wear and simplified cutting control.