Machine vision for high-precision volume measurement applied to levitated containerless material processing

2005 ◽  
Vol 76 (12) ◽  
pp. 125108 ◽  
Author(s):  
R. C. Bradshaw ◽  
D. P. Schmidt ◽  
J. R. Rogers ◽  
K. F. Kelton ◽  
R. W. Hyers
Keyword(s):  
Material Processing ◽  
Machine Vision ◽  
High Precision ◽  
Volume Measurement

scholarly journals A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2732 ◽  
Author(s):  
Xinman Zhang ◽  
Jiayu Zhang ◽  
Mei Ma ◽  
Zhiqi Chen ◽  
Shuangling Yue ◽  
...  
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Absolute Error ◽  
Detection Methods ◽  
Inspection System ◽  
Quality Inspection ◽  
Construction Site ◽  
High Definition ◽  
Steel Bar ◽  
Steel Bars

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

Automatic Cell Cutting by High-Precision Microfluidic Control

2011 ◽  
Vol 23 (1) ◽  
pp. 13-18 ◽  
Author(s):  
Akihiko Ichikawa ◽  
◽  
Tamio Tanikawa ◽  
Satoshi Akagi ◽  
Kohtaro Ohba ◽  
...  
Keyword(s):  
High Precision ◽  
Microfluidic Chip ◽  
Microfluidic Channel ◽  
Volume Measurement ◽  
Microchannel Flow ◽  
Syringe Pump ◽  
Animal Cells ◽  
Positioning Control ◽  
System A ◽  
Image Volume

We have developed automated cell cutting by highprecision microfluidic control using a high-response and high-precision syringe pump. A microfluidic chip containing 2 orthogonal microchannels was used for cutting animal cells softened by cytochalasin and aspirated and fixed in 1 microchannel, then a high-velocity microchannel flow was generated from another channel to cut the cell. To control microchannel flow precisely, we made a syringe pump with a minimum flow of 0.35 × 10-3µl/min and response time of 10 ms. The syringe pump was connected to the microfluidic chip by a thin, hard Teflon tube to reduce the pressure transmission delay between the syringe pump and microfluidic channel. A microbead control experiment depending on PI control using the syringe pump was conducted to check the microchannel flow delay. Bovine oocytes softened by cytochalasin were injected into the microfluidic chip and bisected by microscopic image volume measurement. This paper reports the automatic cell cutting strategy and system, a result of microbead positioning control, and a result of automatic cell cutting.

scholarly journals Research on Tire Marking Point Completeness Evaluation Based on K-Means Clustering Image Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4687 ◽  
Author(s):  
Yuan Yu ◽  
Jinsheng Ren ◽  
Qi Zhang ◽  
Weimin Yang ◽  
Zhiwei Jiao
Keyword(s):  
Image Segmentation ◽  
Machine Vision ◽  
High Precision ◽  
Clustering Algorithm ◽  
Dynamic Balance ◽  
Experimental Results ◽  
Quality Inspection ◽  
Point Evaluations ◽  
Smart Factories

The tire marking points of dynamic balance and uniformity play a crucial guiding role in tire installation. Incomplete marking points block the recognition of tire marking points, and then affect the installation of tires. It is usually necessary to evaluate the marking point completeness during the quality inspection of finished tires. In order to meet the high-precision requirements of the evaluation of tire marking point completeness in the smart factories, the K-means clustering algorithm is introduced to segment the image of marking points in this paper. The pixels within the contour of the marking point are weighted to calculate the marking point completeness on the basis of the image segmentation. The completeness is rated and evaluated by completeness calculation. The experimental results show that the accuracy of the marking point completeness ratings is 95%, and the accuracy of the marking point evaluations is 99%. The proposed method has an important guiding significance of practice to evaluate the tire marking point completeness during the tire quality inspection based on machine vision.

High-precision alignment system of machine vision based on Hough transform

2017 ◽  
Vol 38 (6) ◽  
pp. 523-526
Author(s):  
Wei Li ◽  
Guo Yujing ◽  
Lu Xiangning
Keyword(s):  
Machine Vision ◽  
High Precision ◽  
Hough Transform ◽  
Alignment System
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