基于巴克豪森效应的机器学习技术在确定表面硬度中的应用

IF 0.3 Q4 THERMODYNAMICS
C. Krause, B. Uysal, M. Engler, C. Radek, M. Schaudig
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引用次数: 1

摘要

确保产品和零件的质量影响着制造业的生产率、效率和盈利能力。每个制造公司的目标都是快速识别质量下降,以便采取适当的措施来提高质量。使用巴克豪森噪声等无损检测方法与人工智能(AI)相结合,可以立即对数据进行分类,为在生产线中实现所需的质量监控提供了一种方法。本文对不同回火程度的表面硬化构件的巴克豪森信号测量数据进行了分析。为此,开发了合适的人工智能模型,并使用处理后的测量数据进行训练,以生成表面硬度预测值。采用Spyder开发环境和Python编程语言进行数据准备和进一步处理。在研究中应用、测试和优化了以下模型:支持向量机、随机森林回归和人工神经网络。经过有效的训练,该模型能够以较高的准确率预测硬度等级。总的来说,神经网络显示出最好的结果。应用的程序和方法是快速的,非破坏性的,并提供可接受的测量误差的结果,这允许他们在生产环境中使用。未来将寻求进一步的改进,例如通过应用更大量的训练数据,通过改变训练中使用的特征,以及在捕获巴克豪森信号时提高测量精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Machine Learning Techniques to Determine Surface Hardness Based on the Barkhausen Effect
Abstract Ensuring product and part quality impacts manufacturing productivity, efficiency and profitability. The goal of every manufacturing company is to quickly identify reduced quality in order to take appropriate measures to improve quality. The use of non-destructive testing methods such as Barkhausen noise in combination with artificial intelligence (AI), which immediately classifies the data, offers a way to implement the desired quality monitoring in a production line. In the present study, the measured data of the Barkhausen signal of surface hardened components with different degrees of tempering were analyzed. For this purpose, suitable AI models were developed and trained with the processed measurement data to generate prediction values for the surface hardness. Data preparation and further processing was carried out using the Spyder development environment with the Python programming language. The following models were applied, tested and optimized during the study: Support vector machine, random forest regression and an artificial neural network. The models were able to predict hardness levels with high accuracy after effective training. Overall, the neural network showed the best results. The applied procedures and methods are fast, non-destructive and provide results with acceptable measurement error, which allows their use in the production environment. Further improvements will be sought in the future, e. g. by applying a larger amount of training data, by changing the features used in the training and by increasing the measurement accuracy when capturing the Barkhausen signal.
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来源期刊
CiteScore
1.50
自引率
33.30%
发文量
43
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