Sharing Information Between Machine Tools to Improve Surface Finish Forecasting
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model to predict surface-roughness measurements for a turning machining process. The hierarchical model is compared to multiple independent Bayesian linear regression models to showcase the benefits of partial pooling in a machining setting with respect to prediction accuracy and uncertainty quantification.
Code (0)
등록된 구현이 없습니다.
Tasks
PredictionregressionUncertainty QuantificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Should ChatGPT and Bard Share Revenue with Their Data Providers? A New Business Model for the AI Era
With various AI tools such as ChatGPT becoming increasingly popular, we are entering a true AI era. We can foresee that exceptional AI tools will soon reap considerable profits. A crucial question arise: should AI tools …
Measuring Dimensions of Self-Presentation in Twitter Bios and their Links to Misinformation Sharing
Social media platforms provide users with a profile description field, commonly known as a ``bio," where they can present themselves to the world. A growing literature shows that text in these bios can improve our unders…
MisinformationPrivacy-Preserving Graph Machine Learning from Data to Computation: A Survey
In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importa…
AttributePrivacy PreservingImproving Performance of Commercially Available AI Products in a Multi-Agent Configuration
In recent years, with the rapid advancement of large language models (LLMs), multi-agent systems have become increasingly more capable of practical application. At the same time, the software development industry has had…
Parameter Sharing Methods for Multilingual Self-Attentional Translation Models
In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilin…
Machine TranslationTranslation