Whittaker D S, Gregório J, Byrne T F
Data Science Department, National Physical Laboratory, Glasgow, UK.
Sci Rep. 2025 Jun 4;15(1):19534. doi: 10.1038/s41598-025-03018-3.
We apply a feedforward neural network using supervised learning to sound recordings obtained without specialised equipment as workpieces undergo a simple manufacturing process to predict their mass. We also report a simple technique to seed synthetic from real data for training and testing the algorithm. Work was performed in the frequency domain, with spectrally-resolved magnitudes fed as variables (or features) to the network. The mean absolute percentage deviation when predicting the mass of each of thirty-two workpieces of different material composition considering just real data was 19.2%. This fell to 8.7%, however, when the exercise was repeated making use of a significantly greater amount of these synthetic data to train and test it. Fractional uncertainty in measured mass is of the order of 10, and so this serves well as a general proxy to test our approach. It could find application when data to augment algorithm performance must be obtained robustly, relatively quickly and without much computational effort in the wider context of waste minimisation and green manufacturing.
我们使用监督学习的前馈神经网络，对在工件进行简单制造过程时，使用非专业设备获取的声音记录进行分析，以预测其质量。我们还报告了一种从真实数据中生成合成数据的简单技术，用于训练和测试该算法。这项工作是在频域中进行的，将频谱解析后的幅度作为变量（或特征）输入到网络中。仅考虑真实数据时，预测32个不同材料成分工件的质量时，平均绝对百分比偏差为19.2%。然而，当利用大量这些合成数据进行训练和测试时，这一偏差降至8.7%。测量质量的分数不确定性约为10，因此这可以很好地作为测试我们方法的一般代理。在更广泛的废物最小化和绿色制造背景下，当必须以稳健、相对快速且无需大量计算工作的方式获取增强算法性能的数据时，该方法可能会得到应用。
