Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme

IF 6.1 1区 工程技术 Q1 ENGINEERING, MANUFACTURING
Sampson Canacoo , Shashank Galla , Yuhao Zhong , Saikiran Chary Nalband , Sean Michael Hayes , Monika Biener , Suhas Bhandarkar , Satish T.S. Bukkapatnam
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引用次数: 0

Abstract

Lawrence Livermore National Laboratory (LLNL) has made significant strides in generating clean energy through its inertial confinement fusion (ICF) experiments. These experiments rely on high-density carbon (HDC) coated shells to encapsulate the fusion fuel. The success of these experiments is heavily dependent on the surface quality of these shells, as even minor imperfections, such as deep pits, can negatively impact fusion yield. Ensuring the required smoothness involves an extensive surface-finishing process that spans approximately 20 stages, making it both time-intensive and resource-demanding. A critical challenge in this process is the need for high-resolution scans to detect rare deep pits, which can be costly and impractical if performed on every shell. This highlights the necessity of developing more efficient scanning methods to optimize time and cost without compromising accuracy. To address these challenges, we introduce a novel approach that employs the multivariate Dvoretzky–Kiefer–Wolfowitz (DKW) inequality to provide a probabilistic upper bound on the error in estimating pit distribution characteristics via a Kernel Density Estimator (KDE). This error bound enables efficient and reliable estimation of pit distribution characteristics at a specified statistical confidence level using a minimal number of surface scans. The integrated DKW-KDE approach was validated through surface-finishing experiments across two batches of HDC-coated shells, demonstrating consistent and robust performance across multiple stages of the surface-finishing experiments. The validation studies suggest that the integrated DKW-KDE approach achieves comparable accuracy in estimating the risk of deleterious large pits with six scans, thus conserving time and resources. Further evaluations show that performance remains consistent across batches and over multiple polishing stages. Based on these findings, one can leverage the minimal-scan insights to strategically improve the bottleneck inspection process, thus enhancing the productivity and quality of shell polishing and similar challenging manufacturing processes.
使用极简采样方案有效筛选抛光表面上罕见的大坑异常
劳伦斯利弗莫尔国家实验室(LLNL)通过其惯性约束聚变(ICF)实验,在生产清洁能源方面取得了重大进展。这些实验依靠高密度碳(HDC)涂层的外壳来封装聚变燃料。这些实验的成功很大程度上取决于这些壳的表面质量,因为即使是很小的缺陷,如深坑,也会对熔合收率产生负面影响。为了确保所需的光洁度,需要进行大约20道工序的表面处理,这既耗时又耗资源。在这个过程中,一个关键的挑战是需要高分辨率的扫描来检测罕见的深坑,如果在每个壳上都进行扫描,成本高昂且不切实际。这突出了开发更有效的扫描方法以优化时间和成本而不影响精度的必要性。为了解决这些挑战,我们引入了一种新的方法,该方法采用多元Dvoretzky-Kiefer-Wolfowitz (DKW)不等式,通过核密度估计器(KDE)提供估计坑分布特征误差的概率上界。这种误差范围能够在指定的统计置信水平上使用最少的表面扫描次数有效可靠地估计坑分布特征。集成的DKW-KDE方法通过两批hdc涂层外壳的表面处理实验进行了验证,在表面处理实验的多个阶段显示出一致且稳健的性能。验证研究表明,综合DKW-KDE方法在估计六次扫描的有害大凹坑风险方面达到了相当的准确性,从而节省了时间和资源。进一步的评估表明,在不同批次和多个抛光阶段,性能保持一致。基于这些发现,人们可以利用最小扫描的见解来战略性地改进瓶颈检测过程,从而提高外壳抛光和类似具有挑战性的制造过程的生产率和质量。
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来源期刊
Journal of Manufacturing Processes
Journal of Manufacturing Processes ENGINEERING, MANUFACTURING-
CiteScore
10.20
自引率
11.30%
发文量
833
审稿时长
50 days
期刊介绍: The aim of the Journal of Manufacturing Processes (JMP) is to exchange current and future directions of manufacturing processes research, development and implementation, and to publish archival scholarly literature with a view to advancing state-of-the-art manufacturing processes and encouraging innovation for developing new and efficient processes. The journal will also publish from other research communities for rapid communication of innovative new concepts. Special-topic issues on emerging technologies and invited papers will also be published.
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