A Study of On-Machine Micro Milling Cutter Condition Inspection Based on Machine Vision

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
Xi Zhang ◽  
Yuanyuan Shi ◽  
Benzheng Zhang ◽  
Chunying Si
Keyword(s):  
Machine Vision ◽  
Axial Direction ◽  
Industrial Applications ◽  
Micro Milling ◽  
Inspection System ◽  
Fusion Method ◽  
Edge Image ◽  
Milling Cutter ◽  
Dimension Measurement ◽  
Online Process

In order to inspect the condition of micro milling cutter automatically and accurately in the online process, a dedicated micro milling cutter condition inspection system was established in this paper, which can effectively inspect micro cutter condition from both radial and axial direction. The key methods—the automatic dimension measurement and the fusion method for compositing all-in-focus cutting edge image of micro milling cutters—are studied. The experiments verify that the proposed methods and the developed inspection system can fulfill the needs of industrial applications.

Study on Key Methods of On-Machine Micro Milling Cutter Condition Inspection Based on Machine Vision

2017 ◽  
Author(s):  
Xi Zhang ◽  
Benzheng Zhang ◽  
Yuanyuan Shi ◽  
Bo Shang
Keyword(s):  
Axial Direction ◽  
Industrial Applications ◽  
Cutting Edge ◽  
Micro Milling ◽  
Edge Condition ◽  
Inspection System ◽  
Milling Cutter ◽  
Dimension Measurement ◽  
On Line ◽  
Line Process

In order to inspect the condition of micro milling cutter automatically and accurately in the on-line process, a dedicated micro milling cutter condition inspection system was established in this paper, which includes an on-machine inspection device and a controller. The key methods of the automatic dimension measurement and the cutting edge condition inspection for micro milling cutters are studied. The proposed methods can measure cutter diameter and clamping height, classify tool type as well as inspect cutting edge condition from both radial and axial direction. The experiments verify that the proposed methods and the developed inspection system can fulfill the needs of industrial applications.

Feasibility Study of On-Machine Inspection of Micro Milling Cutter Runout

2018 ◽  
Author(s):  
Xi Zhang ◽  
Chunying Si ◽  
Yuanyuan Shi
Keyword(s):  
Detection Method ◽  
Industrial Applications ◽  
Measuring System ◽  
Micro Milling ◽  
Measurement Unit ◽  
Cutter Runout ◽  
Milling Cutter ◽  
Tool Runout ◽  
Maximum Value

During the process of micro machining, the existence of tool runout not only aggravates the wear and breakage of the cutter, but also seriously affects the surface quality of the parts. In order to observe the runout of micro-milling cutter, a detection method based on machine vision was proposed in this paper, which can calculate the tool runout by measuring the maximum value of external fluctuation of the cutter assembly near the tool tip. The proposed method can realize the direct measurement of radial runout of a micro-milling cutter. A dedicated prototype measuring system was established, which includes an on-machine measurement unit, a controller and the software. To obtain the images of maximum profile of the cutter at different angles, the cutter should be perpendicular with the optical axis of camera lens in the on-machine measurement unit. The experiments verified that the proposed method is feasible and the developed measurement system can fulfill the needs of industrial applications.

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.

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