Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals

2017 ◽  
Vol 48 ◽  
pp. 279-291 ◽  
Author(s):  
K. Patra ◽  
A.K. Jha ◽  
T. Szalay ◽  
J. Ranjan ◽  
L. Monostori
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Thrust Force ◽  
Tool Condition Monitoring ◽  
Tool Condition ◽  
Artificial Neural ◽  
Peck Drilling

Tool Condition Monitoring Using Artificial Neural Network Models

2022 ◽  
pp. 400-426
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

scholarly journals Decision support system for tool condition monitoring in milling process using artificial neural network

2021 ◽  
Author(s):  
.Mohanraj T ◽  
◽  
Tamilvanan A. ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Mean Squared Error ◽  
Flank Wear ◽  
Tool Condition Monitoring ◽  
Ann Model ◽  
Vibration Signals ◽  
Tool Condition ◽  
Artificial Neural

This work discusses the development of tool condition monitoring system (TCMs) during milling of AISI stainless steel 304 using sound pressure and vibration signals. Response Surface Methodology (RSM) was used to design the experiments. The various milling parameters and vegetable-based cutting fluids (VBCFs) were optimized to reduce the surface roughness and flank wear. The experimental results reveal the direct relationship between the flank wear and sound and vibration signals. The various statistical parameters were extracted from the measured signals and given as input data to train the artificial neural network (ANN). From the developed ANN model, the flank wear was predicted with the mean squared error (MSE) of 0.0656 mm.

Tool Condition Monitoring Using Artificial Neural Network Models

2020 ◽  
pp. 550-576
Author(s):  
Srinivasa P. Pai ◽  
Nagabhushana T. N.
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Condition Monitoring ◽  
Medium Carbon Steel ◽  
Tool Condition Monitoring ◽  
Training Time ◽  
Tool Condition ◽  
Cell Structures ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Tool wear is a major factor that affects the productivity of any machining operation and needs to be controlled for achieving automation. It affects the surface finish, tolerances, dimensions of the workpiece, increases machine down time, and sometimes performance of machine tool and personnel are affected. This chapter deals with the application of artificial neural network (ANN) models for tool condition monitoring (TCM) in milling operations. The data required for training and testing the models studied and developed are from live experiments conducted in a machine shop on a widely used steel, medium carbon steel (En 8) using uncoated carbide inserts. Acoustic emission data and surface roughness data has been used in model development. The goal is for developing an optimal ANN model, in terms of compact architecture, least training time, and its ability to generalize well on unseen (test) data. Growing cell structures (GCS) network has been found to achieve these requirements.

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