Language: | Slovenian |
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Year of publishing: | 2013 |
Typology: | 2.08 - Doctoral Dissertation |
Organization: | UM FS - Faculty of Mechanical Engineering |
Publisher: | T. Brajlih] |
UDC: | 004.896:621.762.8:544.032.65(043.3) |
COBISS: | 17435158 |
Views: | 1767 |
Downloads: | 158 |
Average score: | 0 (0 votes) |
Metadata: |
Secondary language: | English |
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Secondary title: | Development of a method for adapting selective lasers sintering parameters to geometrical properties of products |
Secondary abstract: | This work presents development of a method for adapting manufacturing parameters of selective laser sintering technology. One of the main problems when manufacturing by selective laser sintering is dimensional accuracy of products. Main causes for dimensional deviations are material shrinkage and size of laser influence zone. In the first part of this work a hypothesis is presented that says that the shrinkage and laser influence zone size depends on geometrical properties of products. A method for describing geometrical properties by numerical influence factors is also set. Analysis of variance ofmulti-factorial experiment proves the hypothesis and influence of geometrical properties. In the second part, methods for establishing manufacturing parameters adaptation model are presented. Particle swarm optimization and neural network methods are used. In the conclusion, adaption modelʼs capabilities are presented. Also, a method of practical use of developed method is presented. Adapted parameter values are tested on random product. Influence of using proposed method on achievable dimensional accuracyis discussed. Finally, some guidelines for future research are proposed. |
Secondary keywords: | selsctive laser sintering;manufacturing parameters;dimensional accuracy;optimization;geometrical properties;swarm intelligence;neural network;Selektivno lasersko sintranje;Disertacije; |
URN: | URN:SI:UM: |
Type (COBISS): | Dissertation |
Thesis comment: | Univ. v Mariboru, Fak. za strojništvo |
Pages: | VII, 92 str. |
ID: | 8727920 |