An investigation of the influence of microstructure surface topography on the imaging mechanism to explore super-resolution microstructure.

Vision-based precision measurement is limited by the optical resolution. Although various super-resolution algorithms have been developed, measurement precision and accuracy are difficult to guarantee. To achieve nanoscale resolution measurement, a super-resolution microstructure concept is proposed which is based on the idea of a strong mathematical mapping relationship that may exist between microstructure surface topography features and the corresponding image pixel intensities. In this work, a series of microgrooves are ultra-precision machined and their surface topographies and images are measured. A mapping relationship model is established to analyze the effect of the microgroove surface topography on the imaging mechanism. The results show that the surface roughness and surface defects of the microgroove have significant effects on predicting the imaging mechanism. The optimized machining parameters are determined afterward. This paper demonstrates a feasible and valuable work to support the design and manufacture super-resolution microstructure which has essential applications in precision positioning measurement.

On-Machine Detection of Sub-Microscale Defects in Diamond Tool Grinding during the Manufacturing Process Based on DToolnet.

Nowadays, tool condition monitoring (TCM), which can prevent the waste of resources and improve efficiency in the process of machining parts, has developed many mature methods. However, TCM during the production of cutting tools is less studied and has different properties. The scale of the defects in the tool production process is tiny, generally between 10 µm and 100 µm for diamond tools. There are also very few samples with defects produced by the diamond tool grinding process, with only about 600 pictures. Among the many TCM methods, the direct inspection method using machine vision has the advantage of obtaining diamond tool information on-machine at a low cost and with high efficiency, and the method is accurate enough to meet the requirements of this task. Considering the specific, above problems, to analyze the images acquired by the vision system, a neural network model that is suitable for defect detection in diamond tool grinding is proposed, which is named DToolnet. DToolnet is developed by extracting and learning from the small-sample diamond tool features to intuitively and quickly detect defects in their production. The improvement of the feature extraction network, the optimization of the target recognition network, and the adjustment of the parameters during the network training process are performed in DToolnet. The imaging system and related mechanical structures for TCM are also constructed. A series of validation experiments is carried out and the experiment results show that DToolnet can achieve an 89.3 average precision (AP) for the detection of diamond tool defects, which significantly outperforms other classical network models. Lastly, the DToolnet parameters are optimized, improving the accuracy by 4.7%. This research work offers a very feasible and valuable way to achieve TCM in the manufacturing process.

Identification of anti-hepatic fibrosis components in Periplaneta americana based on spectrum-effect relationship and chemical component separation.

Periplaneta americana (PA) is used as a traditional medicine for hepatic diseases such as hepatic fibrosis in China. However, the relationship between the corresponding therapeutic effect and the chemical composition is still unclear. In this study, spectrum-effect relationship and chemical component separation were used to discover the potential of anti-hepatic fibrosis components of PA. The fingerprints of 10 batches of samples were established using HPLC, and the anti-hepatic fibrosis effect was determined using HSC-T6 cells. The spectrum-effect relationship between common peaks and efficacy values was established using partial least squares analysis. Partial peaks in the fingerprints were identified, including X4 (9,12-heptadecanedenoic acid glyceride), X5 (nonadecanoic acid methyl ester), X6 (glyceryl oleate), X7 (13,16,19-eicosatrienoic acid), X9 (linoleic acid), X10 (9,12,15-octadecatrienoic acid glyceride), X12 (hexadecanoic acid), X13 (oleic acid), and X14 (octadecanoic acid), and their anti-hepatic fibrosis activity was tested to verify the results of spectrum-effect relationships. The results showed that X4 , X6 , X7 , and X10 were the active ingredients of PA. This work successfully identified the partial anti-hepatic fibrosis components of PA, which can be used to explain the material basis for the PA anti-hepatic fibrosis effect.

An Investigation of the Cutting Strategy for the Machining of Polar Microstructures Used in Ultra-Precision Machining Optical Precision Measurement.

In this paper, an investigation of cutting strategy is presented for the optimization of machining parameters in the ultra-precision machining of polar microstructures, which are used for optical precision measurement. The critical machining parameters affecting the surface generation and surface quality in the machining of polar microstructures are studied. Hence, the critical ranges of machining parameters have been determined through a series of cutting simulations, as well as cutting experiments. First of all, the influence of field of view (FOV) is investigated. After that, theoretical modeling of polar microstructures is built to generate the simulated surface topography of polar microstructures. A feature point detection algorithm is built for image processing of polar microstructures. Hence, an experimental investigation of the influence of cutting tool geometry, depth of cut, and groove spacing of polar microstructures was conducted. There are transition points from which the patterns of surface generation of polar microstructures vary with the machining parameters. The optimization of machining parameters and determination of the optimized cutting strategy are undertaken in the ultra-precision machining of polar microstructures.

A Method of Multiple Dynamic Objects Identification and Localization Based on Laser and RFID.

In an indoor environment, object identification and localization are paramount for human-object interaction. Visual or laser-based sensors can achieve the identification and localization of the object based on its appearance, but these approaches are computationally expensive and not robust against the environment with obstacles. Radio Frequency Identification (RFID) has a unique tag ID to identify the object, but it cannot accurately locate it. Therefore, in this paper, the data of RFID and laser range finder are fused for the better identification and localization of multiple dynamic objects in an indoor environment. The main method is to use the laser range finder to estimate the radial velocities of objects in a certain environment, and match them with the object's radial velocities estimated by the RFID phase. The method also uses a fixed time series as "sliding time window" to find the cluster with the highest similarity of each RFID tag in each window. Moreover, the Pearson correlation coefficient (PCC) is used in the update stage of the particle filter (PF) to estimate the moving path of each cluster in order to improve the accuracy in a complex environment with obstacles. The experiments were verified by a SCITOS G5 robot. The results show that this method can achieve an matching rate of 90.18% and a localization accuracy of 0.33m in an environment with the presence of obstacles. This method effectively improves the matching rate and localization accuracy of multiple objects in indoor scenes when compared to the Bray-Curtis (BC) similarity matching-based approach as well as the particle filter-based approach.