As smart manufacturing expands, businesses see the importance of digital transformation, especially for small and medium-sized enterprises (SMEs). Unlike larger companies, SMEs face greater challenges when undergoing digital transformation due to technological questions. However, recent advancements in high-performance computing and reduced hardware costs have made deep learning-based digital transformation more financially feasible for SMEs. While previous research utilized machine learning for product quality prediction, there remains a lack of comprehensive in adaptive quality prediction specifically designed for SMEs. This study presents a systematic framework utilizing various machine learning methods and validates research cases using CRISP-DM (Cross-Industry Standard Process for Data Mining). The first step involves applying XGBoost(eXtreme Gradient Boosting)for feature selection, the second step utilizes GRU for parameter prediction. Finally, in the third step, SVM (Support Vector Machine) is employed for quality classification. The integrated framework achieves high accuracy, with R2reaching 90 % for predicted parameters and nearly 95 % for classification indicators. Moreover, this research addresses the research gap in quality prediction and adaptability and provide an effective digital transformation solution for SMEs without substantial investment. The proposed research framework can be applied SMEs of other domains, such as the machining and traditional manufacturing industry.

中文翻译：

---

通过机器学习促进中小企业数字化转型：自适应质量预测框架


随着智能制造的扩展，企业看到了数字化转型的重要性，尤其是对于中小企业 （SME）。与大公司不同，由于技术问题，中小企业在进行数字化转型时面临更大的挑战。然而，高性能计算和硬件成本降低的最新进展使基于深度学习的数字化转型对中小企业来说在财务上更加可行。虽然以前的研究利用机器学习进行产品质量预测，但仍然缺乏专门为中小企业设计的自适应质量预测的全面性。本研究提出了一个利用各种机器学习方法的系统框架，并使用 CRISP-DM（数据挖掘的跨行业标准流程）验证了研究案例。第一步涉及应用 XGBoost（eXtreme Gradient Boosting） 进行特征选择，第二步使用 GRU 进行参数预测。最后，在第三步中，采用 SVM （Support Vector Machine） 进行质量分类。该集成框架实现了很高的准确率，预测参数的 R2 达到 90%，分类指标的 R2 接近 95%。此外，本研究解决了质量预测和适应性方面的研究差距，并为中小企业提供了一种有效的数字化转型解决方案，而无需大量投资。所提出的研究框架可以应用于其他领域的中小企业，例如机械加工和传统制造业。