scholarly journals Development of inclined planetary milling machine with automatic tool axis inclination instrument

Procedia CIRP ◽  
2018 ◽  
Vol 77 ◽  
pp. 50-53
Author(s):  
Kaoru Fukushima ◽  
Hidetake Tanaka
Keyword(s):  
Milling Machine ◽  
Tool Axis ◽  
Automatic Tool

Applications of Transmission Electron Microscopy in the Characterization of Metal Machinability

1968 ◽  
Vol 26 ◽  
pp. 442-443 ◽  
Author(s):  
L.E. Murr ◽  
A.B. Draper
Keyword(s):  
Electron Microscopy ◽  
State Physics ◽  
Test Material ◽  
Milling Machine ◽  
Rotary Table ◽  
Solid State Physics ◽  
Materials Properties ◽  
Transmission Electron ◽  
Selection Of

The industrial characterization of the machinability of metals and alloys has always been a very arbitrarily defined property, subject to the selection of various reference or test materials; and the adoption of rather naive and misleading interpretations and standards. However, it seems reasonable to assume that with the present state of knowledge of materials properties, and the current theories of solid state physics, more basic guidelines for machinability characterization might be established on the basis of the residual machined microstructures. This approach was originally pursued by Draper; and our presentation here will simply reflect an exposition and extension of this research.The technique consists initially in the production of machined chips of a desired test material on a horizontal milling machine with the workpiece (specimen) mounted on a rotary table vice. A single cut of a specified depth is taken from the workpiece (0.25 in. wide) each at a new tool location.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

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