Experimental Study on Effect of Stress Relief and Gas Exsolution on Sample Quality

2017 ◽  
pp. 332-337 ◽  
Author(s):  
SL yang ◽  
T Lunne ◽  
KH Andersen ◽  
G Yetginer
Keyword(s):  
Experimental Study ◽  
Stress Relief ◽  
Sample Quality

Experimental Study on the Relationship between Dislocation Density and Internal Stress in Micro Electroforming Layer

2015 ◽  
Vol 645-646 ◽  
pp. 405-410 ◽  
Author(s):  
Chang Song ◽  
Li Qun Du ◽  
Tong Yang ◽  
Lei Luo ◽  
You Sheng Tao ◽  
...  
Keyword(s):  
Experimental Study ◽  
Dislocation Density ◽  
Internal Stress ◽  
Compressive Stress ◽  
Stress Relief ◽  
Ultrasonic Power ◽  
Pile Up ◽  
Ultrasonic Stress Relief ◽  
The Relationship ◽  
Dislocation Density Increase

In the micro electroforming process, the existence of electroforming layer defects caused by macro internal stress seriously limits the application and development of the micro electroforming technology. Currently, some studies have shown that ultrasonic can reduce the internal stress. But the formation process of the internal stress and the mechanism of ultrasonic stress relief in micro electroforming layer are still unclear now. In this paper, the relationship between dislocation density and internal stress under ultrasonic was studied. The results show that the ultrasonic can make the dislocation density increase and the compressive stress decrease. When the ultrasonic power is 200W, the dislocation density and the compressive stress culminate 3.8×10-15m-2 and-144.4MPa, respectively. The ultrasonic can excite the movement of dislocation proliferation, pile-up and opening, which leads to a micro plastic deformation in the crystal, and thereby releases the internal stress.

scholarly journals Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

2021 ◽  
Author(s):  
Yahia Zakaria ◽  
Mayada Hadhoud ◽  
Magda Fayek
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Sample Quality ◽  
Diversity Sampling ◽  
And Performance

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

scholarly journals Model Experimental Study on Stress Relief of Advanced Boring.

1991 ◽  
Vol 107 (13) ◽  
pp. 965-969
Author(s):  
Sohei SHIMADA
Keyword(s):  
Experimental Study ◽  
Stress Relief

A numerical and experimental study of London Clay subjected to passive stress relief

Géotechnique ◽  
1991 ◽  
Vol 41 (1) ◽  
pp. 1-15 ◽  
Author(s):  
A. B. Fourie ◽  
D. M. Potts
Keyword(s):  
Experimental Study ◽  
Stress Relief ◽  
London Clay ◽  
Passive Stress

scholarly journals An Experimental Study on State Representation Extraction for Vision-Based Deep Reinforcement Learning

2021 ◽  
Vol 11 (21) ◽  
pp. 10337
Author(s):  
Junkai Ren ◽  
Yujun Zeng ◽  
Sihang Zhou ◽  
Yichuan Zhang
Keyword(s):  
Experimental Study ◽  
Reinforcement Learning ◽  
Network Architecture ◽  
Representation Learning ◽  
Evaluation Metrics ◽  
High Dimensional ◽  
Regularization Methods ◽  
Challenging Problem ◽  
State Representation ◽  
Sample Quality

Scaling end-to-end learning to control robots with vision inputs is a challenging problem in the field of deep reinforcement learning (DRL). While achieving remarkable success in complex sequential tasks, vision-based DRL remains extremely data-inefficient, especially when dealing with high-dimensional pixels inputs. Many recent studies have tried to leverage state representation learning (SRL) to break through such a barrier. Some of them could even help the agent learn from pixels as efficiently as from states. Reproducing existing work, accurately judging the improvements offered by novel methods, and applying these approaches to new tasks are vital for sustaining this progress. However, the demands of these three aspects are seldom straightforward. Without significant criteria and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the previous methods are meaningful. For this reason, we conducted ablation studies on hyperparameters, embedding network architecture, embedded dimension, regularization methods, sample quality and SRL methods to compare and analyze their effects on representation learning and reinforcement learning systematically. Three evaluation metrics are summarized, including five baseline algorithms (including both value-based and policy-based methods) and eight tasks are adopted to avoid the particularity of each experiment setting. We highlight the variability in reported methods and suggest guidelines to make future results in SRL more reproducible and stable based on a wide number of experimental analyses. We aim to spur discussion about how to assure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.

scholarly journals Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

2021 ◽  
Author(s):  
Yahia Zakaria ◽  
Mayada Hadhoud ◽  
Magda Fayek
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Sample Quality ◽  
Diversity Sampling ◽  
And Performance

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are either nonexistent or limited to the works they extend upon. This paper’s goal is to conduct an experimental study on four recent deep learning procedural level generation methods for Sokoban (size = 7 × 7) to explore their strengths and weaknesses and provide insights for possible research directions. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models’ quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

scholarly journals A combined computational and experimental study on vibration stress relief for large welded DH36 steel tube

2016 ◽  
Vol 18 (3) ◽  
pp. 1486-1496 ◽  
Author(s):  
Hongyuan Fang ◽  
Shuqi Li ◽  
Xuesong Liu ◽  
Wei Wang ◽  
Qiang Wang ◽  
...  
Keyword(s):  
Experimental Study ◽  
Stress Relief ◽  
Steel Tube ◽  
Vibration Stress
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