相关实验视频
Updated: Jun 23, 2025
09:17
Surrogate Model Development for Digital Experiments in Welding
Published on: March 28, 2025
767
切割工具磨损预测的置信区间估计在使用基于引导式人工神经网络的转向中
Lorenzo Colantonio1, Lucas Equeter1, Pierre Dehombreux1
1Machine Design and Production Engineering Lab, Research Institute for Science and Material Engineering, Research Institute for the Science and Management of Risks, University of Mons, 7000 Mons, Belgium.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的方法,用于使用基于启动的人工神经网络来监测切削工具的降解. 这种技术准确地预测了工具磨损,并为最佳更换提供了置信区间,降低了加工成本.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 切削工具的退化在加工中构成重大挑战,导致由于不可预测的磨损而增加运营成本.
- 最佳的工具更换对于效率至关重要,但降解的变化使得及时的决策变得复杂.
研究的目的:
- 开发和验证使用基于引导式人工神经网络的切削工具降解监测技术.
- 准确估计侧翼磨损 (VB) 并为预测工具更换提供置信区间.
主要方法:
- 从转操作中使用的输入指标:切削力和扭矩的RMS值,加工时间和加工总长度.
- 采用特定架构的人工神经网络:两个隐藏层 (6个神经元与Tanh,6个神经元与ReLu).
- 整合了一个引导方法来生成磨损预测的置信区间.
主要成果:
- 拟议的神经网络模型准确地追踪切割工具的退化情况,并检测使用寿命结束.
- 置信区间有效地估计了预测变化,有助于制定最佳的工具替换策略.
- 通过两层隐藏层网络 (Tanh和ReLu激活功能) 获得最佳结果.
结论:
- 基于bootstrap的人工神经网络为监控切割工具退化提供了强大的解决方案.
- 开发的技术可以及时准确地更换工具,优化加工操作并降低成本.
- 置信区间增强了对预测性维护和工具管理政策的决策.
相关概念视频
01:24Bootstrapping
601
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
601
01:03Prediction Intervals
2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.2K
01:21Confidence Intervals
6.2K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
A...
6.2K
00:54Uncertainty: Confidence Intervals
4.1K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
4.1K
01:25Confidence Interval for Estimating Population Mean
7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K
01:19Survival Tree
79
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
79