scholarly journals Estimation of Position and Size of a Contaminant in Aluminum Casting Using a Thin-Film Magnetic Sensor

Micromachines ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 127
Author(s):  
Tomoo Nakai

Advanced manufacturing processes require an in-line full inspection system. A nondestructive inspection system able to detect a contaminant such as tool chipping was utilized for the purpose of detecting a defective product as well as damaged machine tools used to fabricate the product. In a previous study, a system able to detect magnetic tool steel chipping in conductive material such as aluminum was developed and tested. In this study, a method of position and size estimation for magnetic chipping was investigated and is described. An experimental confirmation of the proposed method was also carried out using an actual prototype system.

2013 ◽  
Vol 230 (s2) ◽  
pp. 73-76
Author(s):  
Sandeep Saxena ◽  
Carsten H. Meyer
Keyword(s):  

2005 ◽  
Vol 297-300 ◽  
pp. 2022-2027 ◽  
Author(s):  
Jin Yi Lee ◽  
Ji Seoung Hwang ◽  
Tetsuo Shoji ◽  
Jae Kyoo Lim

The magneto-optical nondestructive inspection system (hereafter refer to as RMO system) using magneto-optical sensor (hereafter refer to as MO sensor) offers the benefits of providing image data and LMF information at the same time. Therefore this system makes it possible to carry out remote and high speed inspection of cracks from the intensity of the reflected light and to estimate the shape of a crack more effectively than by already existing methods. In other words, the shape of crack could be evaluated using image data, and crack depth can be determined by calculating the intensity of reflected light. The purposes of this study were to confirm the vertical components of leakage magnetic flux from a crack using RMO system and to verify the effects of MO sensor using the finite element method and dipole model calculation. The effectiveness of these analysis methods was compared with experiments using a RMO system and several types and sizes of the crack on plate specimens. The volume of a crack could be estimated using the optical intensity regardless of the shape of cracks.


2010 ◽  
Vol 56 (1(1)) ◽  
pp. 333-337 ◽  
Author(s):  
Seung-Kyu Park ◽  
Sung-Hoon Baik ◽  
Hyung-Ki Cha ◽  
Yong-Moo Cheong ◽  
Young-June Kang

2005 ◽  
Author(s):  
Valery F. Godínez-Azcuaga ◽  
Finlayson Richard D. ◽  
Basavaraju B. Raju

Author(s):  
M. Shiraishi ◽  
T. Yamagiwa ◽  
A. Ito

Monitoring of machine tools and optimization of manufacturing processes require accurate values of in process measured quantities such as dimensional error, force, and surface roughness. The measurement as workpiece is in particular important because the final output in machining is evaluated as the quality machined workpiece itself. A new hybrid sensor using pneumatic and optical method has been developed which can monitor the dimensional error and surface roughness in turning. Satisfactory results were obtained through several experiments.


Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 766 ◽  
Author(s):  
Fernando Veiga ◽  
Alain Gil Del Val ◽  
Alfredo Suárez ◽  
Unai Alonso

In the current days, the new range of machine tools allows the production of titanium alloy parts for the aeronautical sector through additive technologies. The quality of the materials produced is being studied extensively by the research community. This new manufacturing paradigm also opens important challenges such as the definition and analysis of the optimal strategies for finishing-oriented machining in this type of part. Researchers in both materials and manufacturing processes are making numerous advances in this field. This article discusses the analysis of the production and subsequent machining in the quality of TI6Al4V produced by Wire Arc Additive Manufacturing (WAAM), more specifically Plasma Arc Welding (PAW). The promising results observed make it a viable alternative to traditional manufacturing methods.


2020 ◽  
Vol 10 (19) ◽  
pp. 6856 ◽  
Author(s):  
Leandro Ruiz ◽  
Manuel Torres ◽  
Alejandro Gómez ◽  
Sebastián Díaz ◽  
José M. González ◽  
...  

The aerospace sector is one of the main economic drivers that strengthens our present, constitutes our future and is a source of competitiveness and innovation with great technological development capacity. In particular, the objective of manufacturers on assembly lines is to automate the entire process by using digital technologies as part of the transition toward Industry 4.0. In advanced manufacturing processes, artificial vision systems are interesting because their performance influences the liability and productivity of manufacturing processes. Therefore, developing and validating accurate, reliable and flexible vision systems in uncontrolled industrial environments is a critical issue. This research deals with the detection and classification of fasteners in a real, uncontrolled environment for an aeronautical manufacturing process, using machine learning techniques based on convolutional neural networks. Our system achieves 98.3% accuracy in a processing time of 0.8 ms per image. The results reveal that the machine learning paradigm based on a neural network in an industrial environment is capable of accurately and reliably estimating mechanical parameters to improve the performance and flexibility of advanced manufacturing processing of large parts with structural responsibility.


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