Alatefi Moath, Al-Ahmari Abdulrahman M, AlFaify Abdullah Yahia, Saleh Mustafa
Industrial Engineering Department, College of Engineering, King Saud University, Riyadh, Saudi Arabia.
PLoS One. 2024 Dec 5;19(12):e0308380. doi: 10.1371/journal.pone.0308380. eCollection 2024.
The rapid advancement of additive manufacturing (AM) requires researchers to keep up with these advancements by continually improving the AM processes. Improving manufacturing processes involves evaluating the process outputs and their conformity to the required specifications. Process capability indices, calculated using critical quality characteristics (QCs), have long been used in the evaluation process due to their proven effectiveness. AM processes typically involve multi-correlated critical QCs, indicating the need to develop a multivariate process capability index (MPCI) rather than a univariate capability index, which may lead to misleading results. In this regard, this study proposes a general methodological framework for evaluating AM processes using MPCI. The proposed framework starts by identifying the AM process and product design. Fused Deposition Modeling (FDM) is chosen for this investigation. Then, the specification limits associated with critical QCs are established. To ensure that the MPCI assumptions are met, the critical QCs data are examined for normality, stability, and correlation. Additionally, the MPCI is estimated by simulating a large sample using the properties of the collected QCs data and determining the percent of nonconforming (PNC). Furthermore, the FDM process and its capable tolerance limits are then assessed using the proposed MPCI. Finally, the study presents a sensitivity analysis of the FDM process and suggestions for improvement based on the analysis of assignable causes of variation. The results revealed that the considered process mean is shifted for all QCs, and the most variation is associated with part diameter data. Moreover, the process data are not normally distributed, and the proposed transformation algorithm performs well in reducing data skewness. Also, the performance of the FDM process according to different designations of specification limits was estimated. The results showed that the FDM process is incapable of different designs except with very coarse specifications.
增材制造（AM）的快速发展要求研究人员通过不断改进增材制造工艺来跟上这些发展步伐。改进制造工艺涉及评估工艺输出及其与所需规格的符合性。使用关键质量特性（QC）计算的过程能力指数长期以来因其已被证明的有效性而用于评估过程。增材制造工艺通常涉及多相关的关键质量特性，这表明需要开发多变量过程能力指数（MPCI）而非单变量能力指数，因为单变量能力指数可能会导致误导性结果。在这方面，本研究提出了一个使用MPCI评估增材制造工艺的通用方法框架。所提出的框架首先确定增材制造工艺和产品设计。本研究选择熔融沉积建模（FDM）进行调查。然后，建立与关键质量特性相关的规格限。为确保满足MPCI假设，对关键质量特性数据进行正态性、稳定性和相关性检查。此外，通过利用所收集质量特性数据的属性模拟大样本并确定不合格百分比（PNC）来估计MPCI。此外，然后使用所提出的MPCI评估FDM工艺及其能力公差限。最后，该研究对FDM工艺进行了敏感性分析，并基于对可归属变异原因的分析提出了改进建议。结果表明，所考虑的过程均值在所有质量特性上均有偏移，并且最大的变异与零件直径数据相关。此外，过程数据不呈正态分布，并且所提出的变换算法在减少数据偏度方面表现良好。同时，还估计了根据不同规格限指定的FDM工艺性能。结果表明，除了非常粗略的规格外，FDM工艺无法满足不同的设计要求。
