Determination of a Predictive Model for Print Quality of Desktop 3D Printers While at Sea
PROCEEDINGS OF ASME 2023 INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE, IDETC-CIE2023, VOL 12(2023)
Abstract
Shipboard vibration has been identified as having the potential to cause degradation of 3D printer performance. A proposed mitigation for this effect is to actively monitor vibration of the ship deck and critical parts on the printer. A series of tests are conducted wherein a MIL-STD-810H derived shipboard vibration profile is used to excite a printer while it builds basic test specimens. Response accelerations and pass/fail data are collected from these tests to train a logistic regression model that calculates a predicted likelihood of failure. A demonstration test is used to show the capability to monitor the vibration in real-time, and display a indicator to the operator with a “red-light / green-light” criterion. Results of this demonstration are indicative that the vibration monitoring system provides a reasonable predictive ability, and motivates future testing and real-world deployment on a naval vessel.
MoreTranslated text
Key words
Additive Manufacturing,Logistic Regression,Vibration
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
1967
被引用14967 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined