Chien-Hung Liu

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.

Meshfree algorithms offer a convenient way of solving nonlinear steady-state problems in arbitrary plane areas surrounded by complicated boundary shapes. The simplest of these is the polynomial expansion approach. However, it is rarely utilized as a primary tool for this purpose because of its rather ill-conditioned behavior. A well behaved polynomial expansion algorithm is presented in this paper which can be more effectively used to solve the steady-state Allen-Cahn (AC) equation for heat transfer in thin films. In this method, modified polynomial expansion was used to cope with each iteration of the steady-state Allen-Cahn equation to produce nonlinear algebraic equations where multiple scales are automatically determined by the collocation points. These scales can largely decrease the condition number of the coefficient matrix in each nonlinear system, so that the iteration process converges very quickly. The numerical solutions were found to be accurate and stable against moderate noise to better than 7.5%. Computational results verified the method and showed the steady-state Allen-Cahn equation for heat transfer in thin films could easily be resolved for several arbitrary plane domains.

We are pleased to announce a new article type, “Patent Summary”, for Inventions.

This paper proposes a robust double‐integral T‐S fuzzy output regulator design for affine nonlinear systems in the presence of parametric uncertainty and external disturbance. First, we adopt double integrators (an error integrator and an input integrator) to obtain an augmented T‐S fuzzy model representation which has a common input matrix of fuzzy rules. This property yields less stability conditions. Next, by introducing a set of virtual desired variables (VDVs), a double‐integral VDV‐based fuzzy regulator is proposed to cope with unknown bias and to achieve asymptotical output regulation. Afterward, the controller is simplified to avoid VDV calculation and enhance robustness to uncertainty and external disturbance. In contrast to traditional regulation design, the double‐integral non‐VDV fuzzy regulator design reduces the number of fuzzy controller rules and stability LMIs. Moreover, the error coordinate transformation is removed and the uncertainty is allowed in this paper. Finally, a DC/DC buck converter system is taken as the example to demonstrate the expected performance.

A setup of two reflective-type optical sensors for the on-line real-time measurement of straightness and angular errors of a linear stage is presented. The arrangement is similar to that of a linear encoder and can make on-line measurements of stage errors for the analysis of automatic processes as well as real time monitoring. The reflector used as the sensor reference plane was a rectangular piece of commercial silicon wafer. The wafer fixed to the side of the slide was very flat and had good reflective properties. A silicon wafer is much cheaper than a long optically flat mirror of similar precision. The standard deviation of the straightness sensor and the angular error were verified as 0.06 μm and 0.08 arcsec. The accuracy of the two sensors was verified as ±0.25 μm and ±1 arcsec. The two sensors were integrated with a single axis PZT-based stage for real time straightness compensation experiments and the results showed straightness compensation from 3.5 μm down to 0.4 μm.