Effect of Sheet Surface Roughness and Painting Variables on Painted Sheet Appearance

1980 ◽  
Author(s):  
T. G. Nilan ◽  
B. M. Perfetti ◽  
B. J. Scialabba
Keyword(s):  
Surface Roughness ◽  
Sheet Surface

Aluminum sheet surface roughness correlation with adhesion in polymer metal hybrid overmolding

CIRP Annals ◽  
2011 ◽  
Vol 60 (1) ◽  
pp. 559-562 ◽  
Author(s):  
G. Lucchetta ◽  
F. Marinello ◽  
P.F. Bariani
Keyword(s):  
Surface Roughness ◽  
Aluminum Sheet ◽  
Sheet Surface ◽  
Polymer Metal

The Effect of Lubricants and Material Properties in Surface Roughness and Formability for Single Point Incremental Forming Process

2014 ◽  
Vol 979 ◽  
pp. 359-362
Author(s):  
Nuttaphong Sornsuwit ◽  
Sunthorn Sittisakuljaroen
Keyword(s):  
Surface Roughness ◽  
Adhesive Wear ◽  
Incremental Forming ◽  
Single Point ◽  
Single Point Incremental Forming ◽  
Forming Process ◽  
Sheet Surface ◽  
Air Blowing ◽  
Asymmetric Shape ◽  
High Roughness

The single point incremental forming (SPIF) is a manufacturing process which allows small batch and asymmetric shape fabrication. The research focuses on its applications by consider surface roughness and formability. The surface roughness of specimen was resulted by the influence obtained between tool and specimen, where the lubricant played a significant role during the forming process, as well as material elongation as a mechanical property governed the formability of metal sheet. Surface roughness tester, SEM, EDS and profilometer were used for the characterizations. The results showed that low roughness value (Ra) of SUS 304 and SUS 316L obtained by applying air blowing as a lubricant, while Ti Gr2 could obtain low roughness by using MoS2. The behavior of wear was an adhesive wear which transfer to an abrasive wear. SUS 304 and SUS 316L sheet of test specimens achieved higher depth in forming by air lubricant and MoS2, and Ti Gr2 sheet revealed a better formability with MoS2. Furthermore, the highest depth was correlated with high roughness value for each material.

scholarly journals Effect of Sheet Surface Roughness on Clarity of Painted Sheet Steel

1989 ◽  
Vol 75 (11) ◽  
pp. 2090-2097 ◽  
Author(s):  
Kouzou TSUNOYAMA ◽  
Makoto IMANAKA ◽  
Kusuo FURUKAWA ◽  
Akira KISHIDA ◽  
Yasuhiro YAMADA
Keyword(s):  
Surface Roughness ◽  
Sheet Steel ◽  
Sheet Surface

scholarly journals Analysis of sheet surface roughness change under contact with flat and spherical indenters

2018 ◽  
Vol 55 (06) ◽  
pp. 413-428
Author(s):  
T. TRZEPIECINSKI ◽  
Ł. BĄK ◽  
F. STACHOWICZ ◽  
S. BOSIAKOV ◽  
S. ROGOSIN
Keyword(s):  
Surface Roughness ◽  
Sheet Surface ◽  
Roughness Change

scholarly journals Predict the Effects of Forming Tool Characteristics on Surface Roughness of Aluminum Foil Components Formed by SPIF Using ANN and SVR

2020 ◽  
Vol 22 (1) ◽  
pp. 13-26
Author(s):  
Sherwan Mohammed Najm ◽  
Imre Paniti
Keyword(s):  
Surface Roughness ◽  
Transfer Function ◽  
Transfer Functions ◽  
Single Point ◽  
Gradient Boosting ◽  
Tool Geometry ◽  
Support Vector ◽  
Tool Materials ◽  
Sheet Surface ◽  
Tool Surface

AbstractIn the present work, multiple forming tests were conducted under different forming conditions by Single Point Incremental Forming (SPIF). In which surface roughness, arithmetical mean roughness (Ra) and the ten-point mean roughness (Rz) of AlMn1Mg1 sheet were experimentally measured. Also, an Artificial Neural Network (ANN) was used to predict the (Ra) and (Rz) by adopting the data collected from 108 components that were formed by SPIF. Forming tool characteristics played a key role in all the predictions and their effect on the final product surface roughness. In the aim to explore the proper materials and geometry of forming tools, different ANN structures, different training, and transfer functions have been applied to predict (Ra) and (Rz) as an output argument. Furthermore, Support Vector Regression (SVR) with different kernel types have been used for prediction, together with Gradient Boosting regression to sort the effective parameters on the surface roughness. The input arguments were tool materials, tool shape, tool end/corner radius, and tool surface roughness (Ra and Rz). The actual data subjected to a fit regression model to generate prediction equations of Ra and Rz. The results showed that ANN with one output gives the best R-Square (R2). Levenberg-Marquardt backpropagation (Trainlm) training function recorded the highest value of R2, 0.9628 for prediction Ra using Softmax transfer function whereas 0.9972 for Rz by Log- Sigmoid transfer function. Furthermore, tool materials, together with tool surface (Ra), are playing a significant importance role, affecting the sheet surface roughness (Ra). Whereas tool roughness Rz was the critical parameter effected on the Rz of the product. Also, there was a significant positive effect of tool geometry on the sheet surface roughness.

Roughness measurements of nonlinear optical polymer films by scanning tunneling microscopy

1989 ◽  
Vol 47 ◽  
pp. 22-23
Author(s):  
I. H. Musselman ◽  
R.-T. Chen ◽  
P. E. Russell
Keyword(s):  
Surface Roughness ◽  
Scanning Tunneling Microscopy ◽  
Nonlinear Optical ◽  
Polymer Surface ◽  
Light Extinction ◽  
Scanning Tunneling ◽  
Silicon Substrates ◽  
Tunneling Microscopy ◽  
Optical Polymer ◽  
Total Light

Scanning tunneling microscopy (STM) has been used to characterize the surface roughness of nonlinear optical (NLO) polymers. A review of STM of polymer surfaces is included in this volume. The NLO polymers are instrumental in the development of electrooptical waveguide devices, the most fundamental of which is the modulator. The most common modulator design is the Mach Zehnder interferometer, in which the input light is split into two legs and then recombined into a common output within the two dimensional waveguide. A π phase retardation, resulting in total light extinction at the output of the interferometer, can be achieved by changing the refractive index of one leg with respect to the other using the electrooptic effect. For best device performance, it is essential that the NLO polymer exhibit minimal surface roughness in order to reduce light scattering. Scanning tunneling microscopy, with its high lateral and vertical resolution, is capable of quantifying the NLO polymer surface roughness induced by processing. Results are presented below in which STM was used to measure the surface roughness of films produced by spin-coating NLO-active polymers onto silicon substrates.

Surface roughness measurements of vanadium-contaminated fluidized cracking catalysts by atomic force microscopy

1996 ◽  
Vol 54 ◽  
pp. 866-867
Author(s):  
H. Kinney ◽  
M.L. Occelli ◽  
S.A.C. Gould
Keyword(s):  
Surface Roughness ◽  
Zeolite Y ◽  
Atomic Level ◽  
Microporous Structure ◽  
Contact Mode ◽  
Force Microscopy ◽  
Atomic Force ◽  
Before And After ◽  
Roughness Measurements ◽  
Cracking Catalysts

For this study we have used a contact mode atomic force microscope (AFM) to study to topography of fluidized cracking catalysts (FCC), before and after contamination with 5% vanadium. We selected the AFM because of its ability to well characterize the surface roughness of materials down to the atomic level. It is believed that the cracking in the FCCs occurs mainly on the catalysts top 10-15 μm suggesting that the surface corrugation could play a key role in the FCCs microactivity properties. To test this hypothesis, we chose vanadium as a contaminate because this metal is capable of irreversibly destroying the FCC crystallinity as well as it microporous structure. In addition, we wanted to examine the extent to which steaming affects the vanadium contaminated FCC. Using the AFM, we measured the surface roughness of FCCs, before and after contamination and after steaming.We obtained our FCC (GRZ-1) from Davison. The FCC is generated so that it contains and estimated 35% rare earth exchaged zeolite Y, 50% kaolin and 15% binder.

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