کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
11004152 1468626 2018 39 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A modified method for estimating inherent strains from detailed process simulation for fast residual distortion prediction of single-walled structures fabricated by directed energy deposition
ترجمه فارسی عنوان
روش اصلاح شده برای برآورد سویه های ذاتی از شبیه سازی پردازش دقیق برای پیش بینی تخریب سریع باقی مانده ساختارهای تک سیمی ساخته شده توسط رسوب انرژی مستقیم
کلمات کلیدی
تحریف باقی مانده، روش اصلاح شده تنش ذاتی، شبیه سازی فرآیند، رسوب انرژی مستقیم، تولید مواد افزودنی فلزی،
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
چکیده انگلیسی
Predicting residual distortion in metal additive manufacturing (AM) is important to ensure quality of the fabricated component. The inherent strain method is ideal for this purpose, but has not been well developed for AM parts yet. In this paper, a modified inherent strain model is proposed to estimate the inherent strains from detailed AM process simulation of single line depositions on top of each other. The obtained inherent strains are employed in a layer-by-layer static equilibrium analysis to simulate residual distortion of the AM part efficiently. To validate the model, depositions of a single wall and a rectangular contour wall models with different number of layers deposited by a representative directed energy deposition (DED) process are studied. The proposed model is demonstrated to be accurate by comparing with full-scale detailed process simulation and experimental results. To make the method practical, a small-scale detailed simulation model is proposed to extract the mean inherent strains. Based on this approach, simulation results applied to the rectangular contour wall structures of different heights show that the modified inherent strain method is quite efficient, while the residual distortion of AM parts can be accurately computed within a short time. The improvement of the computational efficiency can be up to 80 times in some specific cases.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Additive Manufacturing - Volume 23, October 2018, Pages 471-486
نویسندگان
, , , , ,