52,678 research outputs found
Machine-Part cell formation through visual decipherable clustering of Self Organizing Map
Machine-part cell formation is used in cellular manufacturing in order to
process a large variety, quality, lower work in process levels, reducing
manufacturing lead-time and customer response time while retaining flexibility
for new products. This paper presents a new and novel approach for obtaining
machine cells and part families. In the cellular manufacturing the fundamental
problem is the formation of part families and machine cells. The present paper
deals with the Self Organising Map (SOM) method an unsupervised learning
algorithm in Artificial Intelligence, and has been used as a visually
decipherable clustering tool of machine-part cell formation. The objective of
the paper is to cluster the binary machine-part matrix through visually
decipherable cluster of SOM color-coding and labelling via the SOM map nodes in
such a way that the part families are processed in that machine cells. The
Umatrix, component plane, principal component projection, scatter plot and
histogram of SOM have been reported in the present work for the successful
visualization of the machine-part cell formation. Computational result with the
proposed algorithm on a set of group technology problems available in the
literature is also presented. The proposed SOM approach produced solutions with
a grouping efficacy that is at least as good as any results earlier reported in
the literature and improved the grouping efficacy for 70% of the problems and
found immensely useful to both industry practitioners and researchers.Comment: 18 pages,3 table, 4 figure
Materials property prediction using symmetry-labeled graphs as atomic-position independent descriptors
Computational materials screening studies require fast calculation of the
properties of thousands of materials. The calculations are often performed with
Density Functional Theory (DFT), but the necessary computer time sets
limitations for the investigated material space. Therefore, the development of
machine learning models for prediction of DFT calculated properties are
currently of interest. A particular challenge for \emph{new} materials is that
the atomic positions are generally not known. We present a machine learning
model for the prediction of DFT-calculated formation energies based on Voronoi
quotient graphs and local symmetry classification without the need for detailed
information about atomic positions. The model is implemented as a message
passing neural network and tested on the Open Quantum Materials Database (OQMD)
and the Materials Project database. The test mean absolute error is 20 meV on
the OQMD database and 40 meV on Materials Project Database. The possibilities
for prediction in a realistic computational screening setting is investigated
on a dataset of 5976 ABSe selenides with very limited overlap with the OQMD
training set. Pretraining on OQMD and subsequent training on 100 selenides
result in a mean absolute error below 0.1 eV for the formation energy of the
selenides.Comment: 14 pages including references and 13 figure
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