Context
This dataset comes from research by Semeion, Research Center of Sciences of Communication. The original aim of the research was to correctly classify the type of surface defects in stainless steel plates, with six types of possible defects (plus "other"). The Input vector was made up of 27 indicators that approximately describe the geometric shape of the defect and its outline. 

Content
There are 34 fields. The first 27 fields describe some kind of steel plate faults seen in images. Unfortunately, there is no other information that I know of to describe these columns.

X_Minimum
X_Maximum
Y_Minimum
Y_Maximum
Pixels_Areas
X_Perimeter
Y_Perimeter
Sum_of_Luminosity
Minimum_of_Luminosity
Maximum_of_Luminosity
Length_of_Conveyer
TypeOfSteel_A300
TypeOfSteel_A400
Steel_Plate_Thickness
Edges_Index
Empty_Index
Square_Index
Outside_X_Index
Edges_X_Index
Edges_Y_Index
Outside_Global_Index
LogOfAreas
Log_X_Index
Log_Y_Index
Orientation_Index
Luminosity_Index
SigmoidOfAreas
The last columns are the following types of faults as classes:

Pastry
Z_Scratch
K_Scatch
Stains
Dirtiness
Bumps
Other_Faults


Acknowledgements
MetaNet: The Theory of Independent Judges (PDF Download Available). Available from: https://www.researchgate.net/publication/13731626_MetaNet_The_Theory_of_Independent_Judges [accessed Sep 6, 2017].

Dataset provided by Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy. www.semeion.it

Lichman, M. (2013). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.