The Smart Machining System Monitoring from Machine Learning View

2021 ◽  
pp. 139-190
Author(s):  
Kunpeng Zhu
Keyword(s):  
Machine Learning ◽  
System Monitoring ◽  
Machining System ◽  
Smart Machining

Videooptical Surface Shape and Integrity Estimation in Robots Machining

2013 ◽  
Vol 332 ◽  
pp. 270-275 ◽  
Author(s):  
Tadeusz Mikolajczyk
Keyword(s):  
Surface Roughness ◽  
Industrial Robot ◽  
Surface Shape ◽  
Engineering System ◽  
Machined Surface ◽  
Reflectivity Method ◽  
Machining System ◽  
Grinding Tool ◽  
Smart Machining ◽  
Usb Camera

Paper shows system to surface shape and quality control in machining using industrial robot. To surface control videooptical methods were used. Surface shape was controlled using the special reverse engineering system. To surface roughness measure machined surface reflectivity method was used. Used own constructions non contact system was equipped with red laser light and USB camera. Wrist of robot was equipped with grinding tool. In paper shows some algorithms of presented processes. Shown too examples of experiments results in surface roughness measure in start end of grinding process. First trials of presented system shows possibility to build smart machining system for finishing of surface with unknown shape.

Smart Machining System Using Preprocessor, Postprocessor, and Interpolation Techniques

2018 ◽  
pp. 333-348
Author(s):  
Fusaomi Nagata ◽  
Koga Toshihiro ◽  
Akimasa Otsuka ◽  
Yudai Okada ◽  
Tatsuhiko Sakamoto ◽  
...  
Keyword(s):  
Machining System ◽  
Smart Machining ◽  
Interpolation Techniques

scholarly journals Development of smart machining system for optimizing feedrates to minimize machining time

2017 ◽  
Vol 5 (3) ◽  
pp. 299-304 ◽  
Author(s):  
Hong-seok Park ◽  
Bowen Qi ◽  
Duck-Viet Dang ◽  
Dae Yu Park
Keyword(s):  
Cutting Force ◽  
Machining Process ◽  
Cnc Machine ◽  
Machining Time ◽  
Virtual Machining ◽  
Milling Operations ◽  
Nc Program ◽  
Machining System ◽  
Optimal Feed ◽  
Smart Machining

Abstract Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.

Integration of Wireless Sensors and Models for a Smart Machining System

2009 ◽  
Author(s):  
Robert B. Jerard ◽  
Barry K. Fussell ◽  
Chris A. Suprock ◽  
Yanjun Cui ◽  
Jeffrey Nichols ◽  
...  
Keyword(s):  
Low Cost ◽  
Spindle Motor ◽  
Vibration Sensor ◽  
Machining Process ◽  
Sensor Data ◽  
Research Progress ◽  
Force Model ◽  
Calibration Methods ◽  
Machining System ◽  
Smart Machining

This paper describes recent research progress at the University of New Hampshire in the area of “Smart Machining Systems (SMS)”. Our approach to SMS is to integrate models with wireless embedded sensor data to monitor and improve the machining process. This paper discusses recent progress in low-cost wireless sensor development, model calibration methods, model accuracy, and tool condition monitoring for SMS. We describe a system that can estimate tool wear using the coefficients of a tangential cutting force model. The model coefficients are estimated by online measurement of spindle motor power. We also show the use of a cutting tool embedded with a wireless vibration sensor to detect the onset of chatter in real-time.

scholarly journals Calibration and Characterization of a Low-Cost Wireless Sensor for Applications in CNC End Milling

2012 ◽  
Author(s):  
Andrew Harmon ◽  
Barry K. Fussell ◽  
Robert B. Jerard
Keyword(s):  
End Milling ◽  
Low Cost ◽  
Force Sensor ◽  
Industrial Applications ◽  
Milling Process ◽  
Research Progress ◽  
Machining System ◽  
Smart Machining ◽  
Band Limited ◽  
Cnc End Milling

This paper describes recent research progress at the University of New Hampshire in the area of smart machining systems. Central to creating a smart machining system is the challenge of collecting detailed information about the milling process at the tool tip. This paper discusses the design, static calibration, dynamic characterization, and implementation of a low-cost wireless force sensor for end-milling. The sensor is observed to accurately measure force when most of the cutting power is band-limited below the sensor’s natural frequency. Sensor geometry constrains the milling application to a single tooth cutter; while this constraint is impractical for industrial applications, our sensor is shown to provide useful information in a laboratory setting.

Sign in / Sign up

Export Citation Format

Share Document