RISS 검색 - 국내학술지논문

다국어 입력

あぁかがさざただなはばぱまやゃらわゎんいぃきぎしじちぢにひびぴみりうぅくぐすずつづっぬふぶぷむゆゅるえぇけげせぜてでねへべぺめれおぉこごそぞとどのほぼぽもよょろを

アァカサザタダナハバパマヤャラワヮンイィキギシジチヂニヒビピミリウゥクグスズツヅッヌフブプムユュルエェケゲセゼテデヘベペメレオォコゴソゾトドノホボポモヨョロヲ ―

http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.

변환된 중국어를 복사하여 사용하시면 됩니다.

예시)

ㅥ ㅦ ㅧ ㅨ ㅩ ㅪ ㅫ ㅬ ㅭ ㅮ ㅯ ㅰ ㅱ ㅲ ㅳ ㅴ ㅵ ㅶ ㅷ ㅸ ㅹ ㅺ ㅻ ㅼ ㅽ ㅾ ㅿ ㆀ ㆁ ㆂ ㆃ ㆄ ㆅ ㆆ ㆇ ㆈ ㆉ ㆊ ㆋ ㆌ ㆍ ㆎ

Α Β Γ Δ Ε Ζ Η Θ Ι Κ Λ Μ Ν Ξ Ο Π Ρ Σ Τ Υ Φ Χ Ψ Ω α β γ δ ε ζ η θ ι κ λ μ ν ξ ο π ρ σ τ υ φ χ ψ ω

á à Á À é è É È ç Ç ê

Ä Ö Ü ä ö ü ß

‘ ’ “ ” 〔〕〈〉「」『』【】＂（）［］｛｝

± × ÷ ≠ ≤ ≥ ∞ ∴ ♂ ♀ ∠ ⊥ ⌒ ∂ ∇ ≡ ≒ ≪ ≫ √ ∽ ∝ ∵ ∫ ∬ ∈ ∋ ⊆ ⊇ ⊂ ⊃ ∪ ∩ ∧ ∨ ￢ ⇒ ⇔ ∀ ∃ ∮ ∑ ∏ ＋－＜＝＞

、。 · ‥ … ¨ 〃 ― ∥ ＼ ∼ ´ ～ ˇ ˘ ˝ ˚ ˙ ¸ ˛ ¡ ¿ ː ！＇，．／：；？＾＿｀｜

½ ⅓ ⅔ ¼ ¾ ⅛ ⅜ ⅝ ⅞ ¹ ² ³ ⁴ ⁿ ₁ ₂ ₃ ₄

Æ Ð Ħ Ĳ Ł Ø Œ Þ Ŧ Ŋ æ đ ð ħ ı ĳ ĸ ŀ ł ø œ ß þ ŧ ŋ ŉ

А Б В Г Д Е Ё Ж З И Й К Л М Н О П Р С Т У Ф Х Ц Ч Ш Щ Ъ Ы Ь Э Ю Я а б в г д е ё ж з и й к л м н о п р с т у ф х ц ч ш щ ъ ы ь э ю я

′ ″ ℃ Å ￠￡￥ ¤ ℉ ‰ ＄％Ｆ￦㎕㎖㎗ ℓ ㎘㏄㎣㎤㎥㎦㎙㎚㎛㎜㎝㎞㎟㎠㎡㎢㏊㎍㎎㎏㏏㎈㎉㏈㎧㎨㎰㎱㎲㎳㎴㎵㎶㎷㎸㎹㎀㎁㎂㎃㎄㎺㎻㎽㎾㎿㎐㎑㎒㎓㎔ Ω ㏀㏁㎊㎋㎌㏖㏅㎭㎮㎯㏛㎩㎪㎫㎬㏝㏐㏓㏃㏉㏜㏆

§ ※ ☆ ★ ○ ● ◎ ◇ ◆ □ ■ △ ▽ → ← ↑ ↓ ↔ 〓 ◁ ◀ ▷ ▶ ♤ ♠ ♡ ♥ ♧ ♣ ⊙ ◈ ▣ ◐ ◑ ▒ ▤ ▥ ▨ ▧ ▦ ▩ ♨ ☏ ☎ ☜ ☞ ¶ † ‡ ↕ ↗ ↙ ↖ ↘ ♭ ♩ ♪ ♬ ㉿㈜ № ㏇ ™ ㏂㏘ ℡ ＃＆＊＠ ª º

ⅰ ⅱ ⅲ ⅳ ⅴ ⅵ ⅶ ⅷ ⅸ ⅹ Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ Ⅶ Ⅷ Ⅸ Ⅹ

ا ب ت ث ج ح خ د ذ ر ز س ش ص ض ط ظ ع غ ف ق ک ل م ن ه و ی

RISS 인기검색어

검색결과 좁혀 보기

오늘 본 자료

정확도순

내림차순

10개씩 출력

1
Determination of optimum SLA process parameters of H-shaped parts

Emad Rajabi Khorasani,Hamid Baseri 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.3
A model was proposed for optimization of stereolithography (SLA) process parameters to achieve the minimum shrinkage of H-shaped parts. A neural network was designed to correlate the input parameters to dimensional error of the parts manufactured by SLA. For this purpose, the data of a previous study from the literature was used that investigated the effect of three important parameters (layer thickness, hatch overcure and hatch spacing) of the SLA process by measuring the H-shaped parts manufactured by SLA 250. Then, the neural network model was imported into two optimization algorithms (genetic algorithm and simulated annealing) and the optimal values were determined. Results showed that the combination of neural network and optimization algorithms could determine the optimal input parameters for the minimum shrinkage with good accuracy.