Monitoring of End Milling Operations in Unmanned Machining

1989 ◽  
pp. 370-378
Author(s):  
Andrzej A. Markowski
Keyword(s):  
End Milling ◽  
Milling Operations

Supervision of Tool Wear and Surface Quality During End Milling Operations

1994 ◽  
Vol 27 (4) ◽  
pp. 507-512 ◽  
Author(s):  
H. Konrad ◽  
R. Isermann ◽  
H.U. Oette
Keyword(s):  
Tool Wear ◽  
Surface Quality ◽  
End Milling ◽  
Milling Operations

Directionally Independent Failure Prediction of End-Milling Tools by Tracking Increasing Chaotic Noise at the Machining Frequencies Due to Wear

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Christopher A. Suprock ◽  
John T. Roth
Keyword(s):  
Discrete Cosine Transform ◽  
End Milling ◽  
Dimensional Space ◽  
Data Sets ◽  
Cosine Transform ◽  
Modal Reduction ◽  
Milling Operations ◽  
Higher Dimensional ◽  
System Energy ◽  
Fourier Type

Accurate on-line forecasting of a tool’s condition during end-milling operations is advantageous to the functionality and reliability of automated industrial processes. The ability to disengage the tool prior to catastrophic failure reduces manufacturing costs, excessive machine deterioration, and personnel hazards. Rapid computational feedback describing the system’s state is critical for realizing a practical failure forecasting model. To this end, spectral analysis by fast Fourier type algorithms allows a rapid computational response. The research described herein explores the development of nontraditional real fast Fourier transform (discrete cosine transform) based algorithms performed in unique higher-dimensional states of observed data sets. Moreover, the developed Fourier algorithm quantifies chaotic noise rather than relying on the more traditional observation of system energy. By increasing the vector dimensionality of the discrete cosine transform, the respective linear transform basis more effectively cross correlates the transform data into fewer (more significant) transform coefficients. Thus, a single vector in orthogonally higher-dimensional space is observed instead of multiple orthogonal vectors in single-dimensional space. More specifically, a novel modal reduction technique is utilized to track trends measured from triaxial force dynamometer signals. This transformation effectively achieves both modal reduction and directional independence by observing the chaotic noise instead of system energy. Algorithm output trends from six end-milling life tests are tracked from both linear and pocketing maneuvers in order to demonstrate the technique’s capabilities. In all six tests, the algorithm predicts impending tool failure with sufficient time for tool removal.

scholarly journals Study of machinability of VP 100 steel with different levels of titanium in end milling operations

2015 ◽  
Vol 37 (1) ◽  
pp. 41
Author(s):  
Jean Robert Pereira Rodrigues ◽  
Mauro Araújo Medeiros ◽  
Flávia Cristina Sousa e Silva ◽  
Keyll Carlos Ribeiro Martins ◽  
Marcio Bacci da Silva ◽  
...  
Keyword(s):  
End Milling ◽  
Milling Operations ◽  
Different Levels

Calibration of ball nose micro end milling operations for sculptured surfaces machining

2017 ◽  
Vol 19 (6) ◽  
pp. 587 ◽  
Author(s):  
Gerardo Garcia ◽  
Elisa V. Vázquez ◽  
Héctor R. Siller ◽  
Leopoldo Ruiz Huerta ◽  
Alberto Caballero Ruiz
Keyword(s):  
End Milling ◽  
Sculptured Surfaces ◽  
Milling Operations ◽  
Micro End Milling

A Taguchi-Neural-Based In-Process Tool Breakage Monitoring System in End Milling Operations

2010 ◽  
Vol 450 ◽  
pp. 251-254 ◽  
Author(s):  
Po Tsang B. Huang ◽  
James C. Chen ◽  
Chiuhsiang Joe Lin ◽  
Peng Hua Lyu ◽  
Bo Chen Lai
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Monitoring System ◽  
End Milling ◽  
Computer Integrated Manufacturing ◽  
Cnc Machine ◽  
Machining Processes ◽  
Tool Breakage ◽  
Milling Operations

The use of CNC machines is one of the successful factors in the computer integrated manufacturing (CIM). Even though the CNC machine can automatically perform the machining processes, some of the situations that may significantly influence the quality of product such as a cutting tool breakage. Therefore, to prevent the machine from damaging and ensure the quality of product, it is important to develop a system that can monitor the tool conditions. The purpose of this study is to develop a Taguchi-neural-based in-process tool breakage monitoring system in end milling operations that can monitor the tool conditions and immediately response a proper action. For an in-process tool breakage monitoring system, a neural network was applied to making decisions of monitoring. One of the disadvantages of neural network is the training processes. It is difficult to determine an optimal combination of training parameters of neural networks. Traditionally, the try-and-error method is time-consuming and without systematic base. Therefore, the optimization of training parameters for neural networks using Taguchi design was applied to training the neural network model and to enhance the accuracy of the tool breakage monitoring system.

Predictive-Inspection Based Process Control in End Milling Operations

2005 ◽  
Author(s):  
Aftab Khan ◽  
Dawn Tilbury ◽  
James Moyne
Keyword(s):  
Process Control ◽  
Product Quality ◽  
Manufacturing Process ◽  
Process Model ◽  
Manufacturing Industry ◽  
End Milling ◽  
Loop Control ◽  
Diagnostic Data ◽  
Milling Operations ◽  
Final Inspection

In the manufacturing industry, product quality control is often treated as a completely different problem than process diagnostics. Diagnostics methods are used to quickly identify faults during the process, whereas product quality is assessed at the final inspection stage, after the process is completed. The large amount of data that is collected to enable the on-line diagnostics, however, can be used to predict the product quality before the part is measured at the final inspection stage. This predicted quality can be used as feedback to a control system that improves the process quality by adjusting the process inputs. In this paper we propose a predictive inspection based process control solution for a manufacturing process. A manufacturing process is modelled as an input-output system with the machine settings as inputs and two kinds of outputs: diagnostic data and quality. This model is obtained from off-line experiments using a combination of process inputs and external disturbances. In the online implementation the predictive process model is updated with the diagnostic data collected during runtime and quality is improved by using a two-loop control strategy, on a part-to-part or run-to-run (R2R) basis. The proposed approach is applied to and developed for an end milling operation and simulation and experimental results are presented.

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