A recommender system applied to the indirect materials selection process (RS-IMSP) for producing automobile spare parts

2016 ◽  
Vol 82 ◽  
pp. 233-244 ◽  
Author(s):  
Selene Hernández-Rodríguez ◽  
Carlos Alberto Hernández-Lira ◽  
Adolfo Aguilar-Rico ◽  
Georgina Flores-Becerra ◽  
Javier Flores-Méndez ◽  
...  
Keyword(s):  
Recommender System ◽  
Selection Process ◽  
Spare Parts ◽  
Materials Selection

Performance of Oxidation and Creep Resistant Alloys for Primary Surface Recuperators for the Mercury 50 Gas Turbine

2005 ◽  
Author(s):  
James M. Rakowski ◽  
Charles P. Stinner ◽  
David S. Bergstrom ◽  
Mark Lipschutz ◽  
J. Preston Montague
Keyword(s):  
Gas Turbine ◽  
Selection Process ◽  
Minor Element ◽  
Materials Selection ◽  
Breakaway Oxidation ◽  
Gas Streams ◽  
High Temperature Materials ◽  
Discharge Gas ◽  
Promising Alloy ◽  
Gas Pressures

Recuperation is a means for increasing the efficiency of a simple-cycle gas turbine, allowing for heat transfer between the exhaust and compressor discharge gas streams to occur in a highly efficient, relatively compact package. The primary surface recuperator operates at high temperatures and gas pressures and is constructed from thin metal sections, presenting challenges for high temperature materials selection. This paper discusses a joint Solar Turbines-ATI Allegheny Ludlum project which was undertaken to address the issue of elevated temperature attack in the presence of high levels of water vapor and its relevance to alloys intended for use in primary surface recuperators. An overview of the alloy selection process will be presented, followed by a detailed study of the two most promising alloy candidates. Breakaway oxidation was mitigated by using alloys with higher nickel and chromium content, and oxide scale evaporation was controlled with selected minor element additions.

scholarly journals Penerapan Metode Rank Order Centroid dan Additive Ratio Assessment Pada Aplikasi Rekomendasi Supplier

2021 ◽  
Vol 5 (1) ◽  
pp. 31-40
Author(s):  
Ronny Addenan ◽  
◽  
Wilda Susanti ◽  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Selection Process ◽  
Rank Order ◽  
Spare Parts ◽  
Computerized Decision Support ◽  
K Value ◽  
Long Time ◽  
The Right

PT.Yanmarindo Perkasa is a company in the sale of diesel engines, generators, tooling tools and engine spare parts. Many suppliers want to distribute their goods through PT.Yanmarindo Perkasa, thus demanding the company to choose the right supplier, in order to ensure the smooth fulfillment of stock items. The process of selecting suppliers is still done manually with criteria limited to quantitative criteria, so that the process takes a long time and the results are not precise. The purpose of this study is to build a decision support system for supplier recommendations quickly and precisely in meeting the company's needs. The supplier selection process uses the Rank-Order Centroid (ROC) method in weighting the criteria and the Additive Ratio Assessment (ARAS) for ranking. ARAS value obtained is the K value that is ranked to get the best supplier by using a computerized decision support system. The application of these two methods can speed up the results of decisions, does not take a long time and the results obtained are also maximum.

The Effect of Form on Attributing Meanings to Materials

2007 ◽  
Author(s):  
Elvin Karana ◽  
Wikke van Weelderen ◽  
Ernst-Jan van Woerden
Keyword(s):  
Product Design ◽  
Selection Process ◽  
Materials Selection ◽  
Technical Aspects ◽  
Use Context ◽  
Meaning Attribution

Materials in product design used to be selected based especially on manufacturability concerns and technical aspects such as strength, conductivity, elasticity, etc. Nowadays, the increasing recognition for more intangible issues like meaning attribution or creating emotions in product design made designers shift their focus towards the intangible aspects in their materials selection activity as well. In this research, we aim to concentrate particularly on attributing meanings to materials. It is crucial to realize that several aspects (function, use, context, user, etc.) can be effective in attributing meanings to materials and they should be taken into consideration during the selection process. In this paper, we focus intensively on one of these aspects: the effect of form on attributing meanings to materials. The paper consists of four related studies exploring how people associate some forms with some particular materials and weather form can be effective in changing these ascribed meanings, or not.

Materials Selection Process for Compound-Extruded Aluminium Matrix Composites

2005 ◽  
Vol 7 (12) ◽  
pp. 1150-1155 ◽  
Author(s):  
K. A. Weidenmann ◽  
C. Fleck ◽  
V. Schulze ◽  
D. Löhe
Keyword(s):  
Selection Process ◽  
Aluminium Matrix ◽  
Matrix Composites ◽  
Materials Selection ◽  
Aluminium Matrix Composites

Improving Materials Selection in a Mechanical Engineering Capstone Course

2014 ◽  
Vol 1716 ◽  
Author(s):  
Bridget M. Smyser
Keyword(s):  
Mechanical Engineering ◽  
Materials Science ◽  
Selection Process ◽  
Materials Selection ◽  
Capstone Course ◽  
Materials Properties ◽  
High Temperature Alloys ◽  
Physical Prototype ◽  
Systematic Selection ◽  
Capstone Design

ABSTRACTThe Capstone Design course in the Department of Mechanical Engineering at Northeastern University requires students to build a physical prototype by the end of the two semester sequence. Although students have long been required to take an introductory materials science course as part of their curriculum, there was concern that materials selection was a weakness in the design process. Beginning in Fall 2011, the CES Edupack materials selection software was introduced into the Capstone Design class. The current work means to investigate: 1) how to assess designs for effective materials selection 2) whether the new software was actually used by the student teams and 3) whether there was evidence of improved materials selection in the projects that occurred after the new software was introduced. Final capstone design reports from 10 previous terms were examined to look for evidence of systematic materials selection procedures and clear discussion of materials properties as the basis for selecting a material. References to the software were also noted. Results show that 24% of the groups used the CES Edupack software in the first three terms that the software was available. In addition, there was an increase in the number of groups that used a systematic selection process based on research into published materials properties rather than choosing materials based on rough experimentation or convenience. Finally, there has been an increase in the number of projects which consider or incorporate composites, high temperature alloys, and advanced polymers as the software has increased awareness of these options.

scholarly journals Sistem Rekomendasi Suku Cadang Berdasarkan Item Based Filtering

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Christian Wibisono ◽  
Lucky Surya Haryadi ◽  
Juan Elisha Widyaya ◽  
Swat Lie Liliawati
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Selection Process ◽  
Spare Part ◽  
Data Sparsity ◽  
High Data ◽  
Single Value Decomposition ◽  
Value Decomposition ◽  
Value Model

Replaceable spare part on workshop have many transaction and possibility thus recommender system is needed to simplify the selection process. We propose recommender system with item collaborative filtering, with high data sparsity. With Single Value Decomposition we reduce the matriks to improve the system and decrease “noise” value. Model will be evaluated using MAE, RMSE, and FCP metrics. The results of recommendation model are MAE = 1.2752, RMSE = 1.4882, dan FCP = 0.4947.

scholarly journals A Novel Automatic Journal Recommender System

2020 ◽  
Vol 8 (6) ◽  
pp. 2608-2612
Keyword(s):  
Correlation Coefficient ◽  
Recommender System ◽  
Selection Process ◽  
Pearson Correlation ◽  
Experimental Result ◽  
Journal Title ◽  
Pearson Correlation Coefficient ◽  
The Novel ◽  
User Research ◽  
Journal Publication

Recommender Systems (RS) have developed into an important application in several user domains. RS may aid users to discover appropriate items in vast data. The selection of journal publication is generally based on the research domain or topics of document. The traditional method for journal recommendation is carried out by analyzing the document and matching its topics with relavent journal utilizing content-based examination. Though, this approach might create errors because of disparities in manuscript comparisons. In this paper, a novel Jaccard based Journal Finder Neural Network is proposed with pearson correlation coefficient (JJFNN-PC) for journal recommendation. The proposed recommender system allows the researchers to automatically find appropriate publication with journal title and abstract. Similarity coefficient is computed among the journal database and journal title and abstract of user distinctly through Jaccard similarity. The obtained outcome is used for training the novel JFNN that automatically find appropriate publication for user research article. The pearson correlation coefficient is established to validate the correlation between title and abstract of the recommended journal. The experimental result of automatic journal selection process provides the exact journal list and obtained better performance with accuracy of 98.41%.

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