Metin Ahmet, Bilgin Turgay Tugay
Bursa Technical University, Bursa, Turkey.
PeerJ Comput Sci. 2024 Jul 23;10:e2188. doi: 10.7717/peerj-cs.2188. eCollection 2024.
The enhancement of fabric quality prediction in the textile manufacturing sector is achieved by utilizing information derived from sensors within the Internet of Things (IoT) and Enterprise Resource Planning (ERP) systems linked to sensors embedded in textile machinery. The integration of Industry 4.0 concepts is instrumental in harnessing IoT sensor data, which, in turn, leads to improvements in productivity and reduced lead times in textile manufacturing processes. This study addresses the issue of imbalanced data pertaining to fabric quality within the textile manufacturing industry. It encompasses an evaluation of seven open-source automated machine learning (AutoML) technologies, namely FLAML (Fast Lightweight AutoML), AutoViML (Automatically Build Variant Interpretable ML models), EvalML (Evaluation Machine Learning), AutoGluon, H2OAutoML, PyCaret, and TPOT (Tree-based Pipeline Optimization Tool). The most suitable solutions are chosen for certain circumstances by employing an innovative approach that finds a compromise among computational efficiency and forecast accuracy. The results reveal that EvalML emerges as the top-performing AutoML model for a predetermined objective function, particularly excelling in terms of mean absolute error (MAE). On the other hand, even with longer inference periods, AutoGluon performs better than other methods in measures like mean absolute percentage error (MAPE), root mean squared error (RMSE), and r-squared. Additionally, the study explores the feature importance rankings provided by each AutoML model, shedding light on the attributes that significantly influence predictive outcomes. Notably, sin/cos encoding is found to be particularly effective in characterizing categorical variables with a large number of unique values. This study includes useful information about the application of AutoML in the textile industry and provides a roadmap for employing Industry 4.0 technologies to enhance fabric quality prediction. The research highlights the importance of striking a balance between predictive accuracy and computational efficiency, emphasizes the significance of feature importance for model interpretability, and lays the groundwork for future investigations in this field.
通过利用物联网（IoT）中的传感器以及与纺织机械中嵌入的传感器相连的企业资源规划（ERP）系统所获取的信息，纺织制造业中织物质量预测得以增强。工业4.0概念的整合有助于利用物联网传感器数据，进而提高纺织制造过程中的生产率并缩短交货时间。本研究解决了纺织制造业中与织物质量相关的数据不平衡问题。它涵盖了对七种开源自动化机器学习（AutoML）技术的评估，即FLAML（快速轻量级自动化机器学习）、AutoViML（自动构建可变可解释机器学习模型）、EvalML（评估机器学习）、AutoGluon、H2OAutoML、PyCaret和TPOT（基于树的管道优化工具）。通过采用一种在计算效率和预测准确性之间找到折衷的创新方法，为特定情况选择最合适的解决方案。结果表明，对于预定的目标函数，EvalML成为表现最佳的AutoML模型，尤其在平均绝对误差（MAE）方面表现出色。另一方面，即使推理周期较长，AutoGluon在平均绝对百分比误差（MAPE）、均方根误差（RMSE）和r平方等指标上的表现也优于其他方法。此外，该研究还探讨了每个AutoML模型提供的特征重要性排名，揭示了对预测结果有显著影响的属性。值得注意的是，发现正弦/余弦编码在表征具有大量唯一值的分类变量时特别有效。本研究包含了有关AutoML在纺织行业应用的有用信息，并提供了一条采用工业4.0技术来增强织物质量预测的路线图。该研究强调了在预测准确性和计算效率之间取得平衡的重要性，强调了特征重要性对模型可解释性的意义，并为该领域的未来研究奠定了基础。
