Mechanical method to reduce aberrations for a two-sided coating axisymmetric optical lens based on the specialized finite element method.

Two-sided coated optical lenses are important in optical applications. A film-stress-induced aberration can adversely affect the lens performance. In this paper, a mechanical method has been developed to reduce this aberration. The proposed method uses a specialized finite element method with an easy modeling process and high versatility to analyze the impact of film parameters (including stress, the thickness, and the coating range) on aberrations under different lens geometric parameters. Theoretically, by selecting the property film parameters within the range of an application's requirements can reduce the aberrations. The proposed method could reduce film-stress-induced aberrations to make the aberration compensation easier.

A distribution-based selective optimization method for eliminating periodic defects in harmonic signals.

Due to environmental interference and defects in measured objects, measurement signals are frequently affected by unpredictable noise and periodic defects. Moreover, there is a lack of effective methods for accurately distinguishing defect components from measurement signals. In this study, a distribution-based selective optimisation method (SOM) is proposed to mitigate the effects of noise and defect components. The SOM can be seen as a binary- or multiple-class signal classifier based on an error distribution, which can simultaneously eliminate periodic defect components of measurement signals and proceed with signal-fitting regression. The effectiveness, accuracy, and feasibility of the SOM are verified in theoretical and realworld measurement settings. Based on theoretical simulations under various parameter conditions, some criteria for selecting operation variables among a selection of parameter conditions are explained in detail. The proposed method is capable of separating defect components from measurement signals while also achieving a satisfactory fitting curve for the measurement signals. The proposed SOM has broad application prospects in signal processing and defect detection for mechanical measurements, electronic filtering, instrumentation, part maintenance, and other fields.

Surface Quality Improvement for Ultrasonic-Assisted Inner Diameter Sawing with Six-Axis Force Sensors.

Ultrasonic-assisted inner diameter machining is a slicing method for hard and brittle materials. During this process, the sawing force is the main factor affecting the workpiece surface quality and tool life. Therefore, based on indentation fracture mechanics, a theoretical model of the cutting force of an ultrasound-assisted inner diameter saw is established in this paper for surface quality improvement. The cutting experiment was carried out with alumina ceramics (99%) as an exemplar of hard and brittle material. A six-axis force sensor was used to measure the sawing force in the experiment. The correctness of the theoretical model was verified by comparing the theoretical modeling with the actual cutting force, and the influence of machining parameters on the normal sawing force was evaluated. The experimental results showed that the ultrasonic-assisted cutting force model based on the six-axis force sensor proposed in this paper was more accurate. Compared with the regular tetrahedral abrasive model, the mean value and variance of the proposed model's force prediction error were reduced by 5.08% and 2.56%. Furthermore, by using the proposed model, the sawing processing parameters could be updated to improve the slice surface quality from a roughness Sa value of 1.534 µm to 1.129 µm. The proposed model provides guidance for the selection of process parameters and can improve processing efficiency and quality in subsequent real-world production.