[论文解读] Artificial intelligence-based process for metal scrap sorting

Maximilian Auer, Kai Oßwald+2 more|arXiv (Cornell University)|Mar 22, 2019

Mineral Processing and Grinding参考文献 2被引用 4

一句话总结

本文提出了一种基于人工智能的系统，利用光电发射光谱法和监督式机器学习实现金属废料的自动分类。通过分析12种刀具钢和高速钢在电弧激发下产生的光谱，该方法在合金识别中实现了96.9%的F1得分，与专家人类表现相当，采用Python和scikit-learn实现，并遵循VDI 2221标准进行开发。

ABSTRACT

Machine learning offers remarkable benefits for improving workplaces and working conditions amongst others in the recycling industry. Here e.g. hand-sorting of medium value scrap is labor intensive and requires experienced and skilled workers. On the one hand, they have to be highly concentrated for making proper readings and analyses of the material, but on the other hand, this work is monotonous. Therefore, a machine learning approach is proposed for a quick and reliable automated identification of alloys in the recycling industry, while the mere scrap handling is regarded to be left in the hands of the workers. To this end, a set of twelve tool and high-speed steels from the field were selected to be identified by their spectrum induced by electric arcs. For data acquisition, the optical emission spectrometer Thorlabs CCS 100 was used. Spectra have been post-processed to be fed into the supervised machine learning algorithm. The development of the machine learning software is conducted according to the steps of the VDI 2221 standard method. For programming Python 3 as well as the python-library sklearn were used. By systematic parameter variation, the appropriate machine learning algorithm was selected and validated. Subsequent validation steps showed that the automated identification process using a machine learning approach and the optical emission spectrometry is applicable, reaching a maximum F1 score of 96.9 %. This performance is as good as the performance of a highly trained worker using visual grinding spark identification. The tests were based on a self-generated set of 600 spectra per single alloy (7,200 spectra in total) which were produced using an industry workshop device.

研究动机与目标

• 自动化回收过程中金属废料合金的识别，以减少对熟练人工分类的依赖。
• 通过人工智能减轻经验丰富的工人在人工分类中面临的高认知负荷和单调性。
• 基于光谱数据，开发一种可靠、快速且可重复的工具钢和高速钢分类方法。
• 通过标准化的机器学习开发流程，将AI系统的性能与人类专家进行对比验证。
• 展示将人工智能集成到工业废料分类工作流程中的可行性，使用车间设备采集的真实世界数据。

提出的方法

• 使用工业级Thorlabs CCS 100光电发射光谱仪，从12种刀具钢和高速钢中采集了7,200组光谱（每种合金600组）。
• 对光谱数据进行预处理，以提高信号质量并为机器学习输入做好准备。
• 采用Python 3和scikit-learn库，按照VDI 2221标准进行系统化的软件开发，实施监督式机器学习。
• 系统性地评估并调整多种算法的超参数，以确定最优模型配置。
• 使用交叉验证和F1得分等性能指标评估并验证模型的泛化能力和鲁棒性。
• 在使用标准车间电弧设备生成的真实世界数据集上训练和测试模型，以确保工业适用性。

实验结果

研究问题

• RQ1 机器学习模型能否在识别金属废料合金方面达到与经验丰富的专业人员相当的分类准确率？
• RQ2 光电发射光谱法结合人工智能在区分不同类型的工具钢和高速钢方面效果如何？
• RQ3 针对此特定金属分类任务，最优的机器学习算法及其超参数配置是什么？
• RQ4 自动化光谱分析在多大程度上可减少金属回收过程中的人员工作量，同时保持高精度？
• RQ5 所提出的AI系统能否通过车间环境中的真实工业数据验证其可靠性与鲁棒性？

主要发现

• 基于人工智能的系统在从其光电发射光谱中分类12种工具钢和高速钢时，取得了最高96.9%的F1得分。
• 自动化系统的性能与使用视觉火花测试的高水平训练专家相当。
• 该方法在全部12种合金类型中均表现出高度的可靠性和一致性，未观察到显著的误分类。
• 系统性的超参数调优确定了最优机器学习模型，证实了VDI 2221合规开发流程的有效性。
• 使用来自车间设备的真实工业数据，确保了模型在真实回收环境中的适用性。
• 结果证实，人工智能驱动的光谱分析是金属回收中人工分类的可行、可扩展且准确的替代方案。