基于机器学习的工艺控制,用于注塑回收聚烯的注塑成型.
Joshua Krantz1, Juliana Licata1, Muntaqim Ahmed Raju2
1Department of Plastics Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Polymers
|April 12, 2025
概括
机器学习通过预测质量结果来增强使用回收材料的注塑成型. 这种闭环控制提高了工艺稳定性和可持续材料使用.
科学领域:
- 材料科学与工程 材料科学与工程
- 人工智能和机器学习
- 制造过程 制造过程 制造过程
背景情况:
- 制造业越来越多地使用人工智能 (AI) 和机器学习 (ML) 来优化流程和提高效率.
- 注塑成型面临的挑战是回收材料的变化,影响零件质量和加工稳定性.
- 可持续材料的采用受到控制回收材料属性的困难所阻碍.
研究的目的:
- 开发和评估一种新的闭环过程控制方法,用于使用机器学习注射成型.
- 预测回收材料的加工投入和质量结果的适应性.
- 评估不同机器学习模型在预测材料特性和过程参数方面的性能.
主要方法:
- 利用人工神经网络 (ANN),线性回归和多项式回归来模型循环聚烯 (rPP) 特性和注塑成型参数之间的关系.
- 使用TensorFlow和Keras实现了一个ANN模型,具有特定的架构选择 (六个隐藏层,32个神经元/层,ReLU激活,Adam优化器).
- 采用经验调整和早期停止用于模型优化,并采用平均绝对误差 (MAE),平均平方误差 (MSE) 和预测评估的百分比误差.
主要成果:
- 产应力,最终延长和零件重量在模型中以高准确度 (在5-10%的误差范围内) 预测.
- 模块预测显示出更高的可变性和更低的可靠性 (错误率高达40%),特别是多项式回归.
- 处理输入预测的错误范围从3%到25%,因模型和响应而异.
结论:
- 由机器学习驱动的闭环过程控制有效地预测了回收材料注塑中的质量参数.
- 这项研究强调了模拟回收材料行为的复杂性,没有一种方法始终优于其他方法.
- 拟议的方法可以提高工艺稳定性,提高材料利用率,并促进在制造业中使用可持续材料.
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