Smart Machining Systems

2022 ◽  
Author(s):  
Kunpeng Zhu
Keyword(s):  
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.

Smart Machining Process Monitoring Enabled by Contextualized Process Profiles for Synchronization

2020 ◽  
Vol 4 (2) ◽  
pp. 20190040
Author(s):  
Zhigang Wang ◽  
Timothy C. Wagner ◽  
Changsheng Guo
Keyword(s):  
Process Monitoring ◽  
Machining Process ◽  
Smart Machining

Development of Smart Tooling Concepts Applied to Ultraprecision Machining

2017 ◽  
Vol 5 (2) ◽  
Author(s):  
Chao Wang ◽  
Kai Cheng ◽  
Richard Rakowski
Keyword(s):  
Cutting Force ◽  
High Speed ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Feedback Signal ◽  
Ultraprecision Machining ◽  
Precision Turning ◽  
High Speed Drilling ◽  
Smart Machining ◽  
Spindle Development

This paper presents smart tooling concepts applied to ultraprecision machining, particularly through the development of smart tool holders, two types of smart cutting tools, and a smart spindle for high-speed drilling and precision turning purposes, respectively. The smart cutting tools presented are force-based devices, which allow measuring the cutting force in real-time. By monitoring the cutting force, a suitable sensor feedback signal can be captured, which can then be applied for the smart machining. Furthermore, an overview of recent research projects on smart spindle development is provided, demonstrating that signal feedback is very closely correlated to the drilling through a multilayer composite board. Implementation aspects on the proposed smart cutting tool are also explored in the application of hybrid dissimilar material machining.

scholarly journals Optimising Production through Intelligent Manufacturing

2020 ◽  
Vol 152 ◽  
pp. 03012
Author(s):  
Isaac O. Olalere ◽  
Oludolapo A. Olanrewaju
Keyword(s):  
Decision Making ◽  
Machine Tool ◽  
Production Line ◽  
Data Analytics ◽  
Intelligent Manufacturing ◽  
Smart Manufacturing ◽  
Operating Parameters ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Smart Machining

Intelligent manufacturing system (IMS) has been the focus of most industries since Industry 4.0 revolution. IMS is being implemented through the integration of Internet of Things, (IoT), Cyber-Physical Systems (CPS), digital twin and big data analytics to optimize production through smart manufacturing. This research presents a conceptual approach of an adaptive clustering algorithm (ACA) for advanced manufacturing decision-making for smart machining manufacturing. The work considers product monitoring and assessment, machine health and operating parameters monitoring, as an important factor for intelligent decision making on a machining production line through the developed cyber twin of the machine tool for production optimisation. Cyber twin of the machine tool is developed which runs on a realtime sequence with the physical asset fussed with smart sensors and controllers enabled with cloud computing, IoT and data analytics. The ACA enables resources monitoring, production monitoring, machine condition monitoring, cloud feedback notification, product monitoring, and assessment, for intelligent decision-making from a cluster of similar machines using ANN clustering tool for self-aware, self-predict and self-reconfiguration in a smart machining production line to detect a cutting tool chipping of less than 0.25mm size. The method is proposed to optimise production by increasing productivity through intelligent decision and prediction for tool change, tool failure, maintenance, adjustment of operating parameters.

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