一种基于人工智能的金属废料分选工艺
Artificial intelligence-based process for metal scrap sorting
摘要：
机器学习为改善回收行业的工作场所和工作条件等提供了显着的好处。例如，中等价值废料的手工分拣是劳动密集型的，需要有经验和熟练的工人。一方面，他们必须高度集中精力对材料进行适当的阅读和分析，但另一方面，这项工作是单调的。因此，提出了一种机器学习方法，用于在回收行业中快速可靠地自动识别合金，而仅仅是废料处理被认为是留给工人的。为此，从现场选择了一组12种工具钢和高速钢，通过电弧诱导的光谱进行识别。对于数据采集，使用光学发射光谱仪Thorlabs CCS 100。光谱已经过后处理，以输入监督机器学习算法。机器学习软件的开发是根据ISO2221标准方法的步骤进行的。对于编程，使用了Python 3以及Python库sklearn。通过系统参数的变化，选择合适的机器学习算法并进行验证。随后的验证步骤表明，使用机器学习方法和光学发射光谱法的自动识别过程是适用的，达到96.9%的最大F1分数。该性能与训练有素的工人使用视觉磨削火花识别的性能一样好。测试基于使用工业车间设备产生的每种合金600个光谱（总共7，200个光谱）的自生成集合。
英文摘要：
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.
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