scholarly journals Additive manufacturing of silica glass using laser stereolithography with a top-down approach and fast debinding

RSC Advances ◽  
2018 ◽  
Vol 8 (29) ◽  
pp. 16344-16348 ◽  
Author(s):  
Chang Liu ◽  
Bin Qian ◽  
Xiaofeng Liu ◽  
Limin Tong ◽  
Jianrong Qiu
Keyword(s):  
Additive Manufacturing ◽  
Silica Glass ◽  
Top Down ◽  
New Approach ◽  
Glass Additive ◽  
Laser Stereolithography

A new approach for glass additive manufacturing with stereolithography that raises the efficiency in a great extent.

A New Approach for SRAM Soft Defect Root Cause Identification

2007 ◽  
Author(s):  
Peter Egger ◽  
Stefan Müller ◽  
Martin Stiftinger
Keyword(s):  
Integrated Circuits ◽  
Top Down ◽  
Feature Size ◽  
Design Rules ◽  
New Approach ◽  
Root Cause ◽  
Wafer Fab ◽  
Sram Cell ◽  
Tcad Simulation ◽  
Root Cause Identification

Abstract With shrinking feature size of integrated circuits traditional FA techniques like SEM inspection of top down delayered devices or cross sectioning often cannot determine the physical root cause. Inside SRAM blocks the aggressive design rules of transistor parameters can cause a local mismatch and therefore a soft fail of a single SRAM cell. This paper will present a new approach to identify a physical root cause with the help of nano probing and TCAD simulation to allow the wafer fab to implement countermeasures.

scholarly journals Multiscale additive manufacturing of polymers using 3D photo-printable self-assembling ionic liquid monomers

2019 ◽  
Vol 4 (3) ◽  
pp. 580-585 ◽  
Author(s):  
Bineh G. Ndefru ◽  
Bryan S. Ringstrand ◽  
Sokhna I.-Y. Diouf ◽  
Sönke Seifert ◽  
Juan H. Leal ◽  
...  
Keyword(s):  
Ionic Liquid ◽  
Additive Manufacturing ◽  
Self Assembly ◽  
Low Cost ◽  
Low Resolution ◽  
Top Down ◽  
Bottom Up ◽  
Cost Approach ◽  
Self Assembling ◽  
Nanoscale Features

Combining bottom-up self-assembly with top-down 3D photoprinting affords a low cost approach for the introduction of nanoscale features into a build with low resolution features.

Additive Manufacturing of Transparent Silica Glass from Solutions

2018 ◽  
Vol 10 (22) ◽  
pp. 18879-18885 ◽  
Author(s):  
Ido Cooperstein ◽  
Efrat Shukrun ◽  
Ofir Press ◽  
Alexander Kamyshny ◽  
Shlomo Magdassi
Keyword(s):  
Additive Manufacturing ◽  
Silica Glass

scholarly journals Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment

2018 ◽  
Vol 5 (5) ◽  
pp. 939-945 ◽  
Author(s):  
Grace X. Gu ◽  
Chun-Teh Chen ◽  
Deon J. Richmond ◽  
Markus J. Buehler
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Additive Manufacturing ◽  
Convolutional Neural Networks ◽  
New Approach ◽  
Hierarchical Materials

A new approach to design hierarchical materials using convolutional neural networks is proposed and validated through additive manufacturing and testing.

Dealing with Multiple Models in System Dynamics

2014 ◽  
Vol 3 (4) ◽  
pp. 17-35 ◽  
Author(s):  
Willem L. Auping ◽  
Erik Pruyt ◽  
Jan H. Kwakkel
Keyword(s):  
System Dynamics ◽  
Simulation Models ◽  
Dynamics Simulation ◽  
Multiple Models ◽  
Top Down ◽  
Bottom Up ◽  
New Approach ◽  
Behavioural Patterns ◽  
Copper Availability

This paper introduces an approach to compare simulation runs from multiple System Dynamics simulation models. Three dynamic hypotheses regarding the uncertain evolutions of long-term copper availability are introduced and used to illustrate the new approach. They correspond to three different perspectives on the copper system (global top-down, global bottom-up, and regional top-down). Although each of these models allows to generate a wealth of behavioural patterns, the focus in this paper is on the differences in trajectories caused by different models for identical values of shared parameters and identical settings of other assumptions, not on differences in behavioural patterns caused by each of the models. Hence, differences in trajectories between the three models are identified, quantified, and classified based on a quantified measure of difference. For these models, small differences between the trajectories are only found in stable runs, while the alternative perspectives are largely responsible for medium to large differences. Hence, it is concluded that multiple dynamic hypotheses may have to be modelled when dealing with uncertain issues.

A new approach to outdoor scene description based on learning and top-down segmentation

2001 ◽  
Vol 19 (14) ◽  
pp. 1041-1055 ◽  
Author(s):  
Joan Martı́ ◽  
Jordi Freixenet ◽  
Joan Batlle ◽  
Alı́cia Casals
Keyword(s):  
Top Down ◽  
New Approach ◽  
Scene Description

scholarly journals Sensing and control in glass additive manufacturing

Mechatronics ◽  
2018 ◽  
Vol 56 ◽  
pp. 188-197 ◽  
Author(s):  
Daniel Peters ◽  
Joseph Drallmeier ◽  
Douglas A. Bristow ◽  
Robert G. Landers ◽  
Edward Kinzel
Keyword(s):  
Additive Manufacturing ◽  
Glass Additive ◽  
And Control

Selection of additive manufacturing processes

2017 ◽  
Vol 23 (2) ◽  
pp. 434-447 ◽  
Author(s):  
Yuanbin Wang ◽  
Robert Blache ◽  
Xun Xu
Keyword(s):  
Additive Manufacturing ◽  
New Technologies ◽  
Rapid Development ◽  
A Priori ◽  
Design Stage ◽  
Selection Methods ◽  
Sequential Decision ◽  
Process Selection ◽  
New Approach ◽  
Content Type

Purpose This study aims to review the existing methods for additive manufacturing (AM) process selection and evaluate their suitability for design for additive manufacturing (DfAM). AM has experienced a rapid development in recent years. New technologies, machines and service bureaus are being brought into the market at an exciting rate. While user’s choices are in abundance, finding the right choice can be a non-trivial task. Design/methodology/approach AM process selection methods are reviewed based on decision theory. The authors also examine how the user’s preferences and AM process performances are considered and approximated into mathematical models. The pros and cons and the limitations of these methods are discussed, and a new approach has been proposed to support the iterating process of DfAM. Findings All current studies follow a sequential decision process and focus on an “a priori” articulation of preferences approach. This kind of method has limitations for the user in the early design stage to implement the DfAM process. An “a posteriori” articulation of preferences approach is proposed to support DfAM and an iterative design process. Originality/value This paper reviews AM process selection methods in a new perspective. The users need to be aware of the underlying assumptions in these methods. The limitations of these methods for DfAM are discussed, and a new approach for AM process selection is proposed.

Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data

2019 ◽  
Author(s):  
Nathan Decker ◽  
Qiang Huang
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Three Dimensional ◽  
Triangular Mesh ◽  
Geometric Accuracy ◽  
New Approach ◽  
Machine Learning Model ◽  
3D Printed ◽  
3D Shapes ◽  
Complex Parts

Abstract While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.

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