Nanomechanical characterization of vertical well-aligned single-crystal ZnO nanowires on ZnO:Ga/glass templates was performed by nanoindentation technique. The buckling loads were found to be 1465 and $215\mu \mathrm{N}$ for the ZnO nanowires of 100 and $30\mathrm{nm}$ diameters, respectively. Furthermore, the buckling energies for the ZnO nanowires of 100 and $30\mathrm{nm}$ diameters were $3.62\times {10}^{-10}$ and $3.69\times {10}^{-11}\mathrm{J}$ , respectively. Based on the Euler buckling model, Young’s modulus of the individual ZnO nanowire has been derived from two possible modes in this work.

This article presents a useful measuring system for the simultaneous measurement of six-degrees-of-freedom motion errors of a moving stage. The system integrates a miniature fiber coupled laser interferometer with specially designed optical paths and quadrant detectors, capable of measuring six-degrees-of-freedom motion errors. Using this model, the proposed measuring method provides rapid performance, simplicity of setup, and preprocess verification of a linear stage. The experimental setups and measuring procedures, and a systematic calculated method for the error verification are presented in the paper. The system’s resolution of measuring straightness error component is about $25\mathrm{nm}$ . The resolution of measuring the pitch and yaw angular error component is about $0.06\mathrm{arcs}$ . With the comparison between the HP calibration system and the proposed system in the measuring range of $120\mathrm{mm}$ , the system accuracy of measuring straightness error and angular error is within the range $\pm 0.6\mu \mathrm{m}$ and $\pm 0.3\mathrm{arcs}$ .

This paper offers a review of the artificial intelligence (AI) algorithms and applications presently being used for smart machine tools. These AI methods can be classified as learning algorithms (deep, meta-, unsupervised, supervised, and reinforcement learning) for diagnosis and detection of faults in mechanical components and AI technique applications in smart machine tools including intelligent manufacturing, cyber-physical systems, mechanical components prognosis, and smart sensors. A diagram of the architecture of AI schemes used for smart machine tools has been included. The respective strengths and weaknesses of the methods, as well as the challenges and future trends in AI schemes, are discussed. In the future, we will propose several AI approaches to tackle mechanical components as well as addressing different AI algorithms to deal with smart machine tools and the acquisition of accurate results.