A significant challenge in comprehensive geometric accuracy control of an additive manufacturing (AM) system is the specification of shape deviation models for different computer-aided design products manufactured on its constituent AM processes. Current deviation modeling techniques do not satisfactorily address this challenge because they can require substantial user inputs and efforts to implement. We present a new model building methodology based on a class of Bayesian neural networks (NNs) that directly addresses this challenge with much less effort. Our method enables automated deviation modeling of different shapes and AM processes and yields models with higher predictive accuracies compared to the existing modeling methods on the same samples of manufactured products. A fundamental innovation in our methodology is the design of new and connectable NN structures that facilitate the leveraging of previously specified deviation models for adaptive model building of new shapes and AM processes. The power and broad scope of our method are demonstrated with several case studies on both in-plane and out-of-plane deviations for a wide variety of shapes manufactured under different stereolithography processes. Our Bayesian methodology for automated and comprehensive deviation modeling can ultimately help to advance flexible, efficient, and high-quality manufacturing in an AM system. Note to Practitioners —Additive manufacturing (AM) systems possess an intrinsic capability for one-of-a-kind manufacturing of a vast variety of shapes across a wide spectrum of constituent processes. Learning how to control geometric shape accuracy in a comprehensive manner for an AM system is vital to its operation. This task is challenging due to constraints on the number of test shapes that can be manufactured and user efforts that can be devoted for learning and predicting geometric errors of different sets of shapes and AM processes. This article presents an automated machine learning methodology for comprehensive learning and prediction of geometric errors in an AM system based on a limited number of test shapes manufactured under different processes. Several case studies serve to validate the potential of our methodology to learn effective geometric accuracy control policies for general AM systems in practice.

中文翻译：

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通过贝叶斯神经网络对增材制造系统进行自动几何形状偏差建模

增材制造（AM）系统的全面几何精度控制中的一项重大挑战是，针对在其组成的增材制造工艺中制造的不同计算机辅助设计产品的形状偏差模型的规范。当前的偏差建模技术不能令人满意地解决这一挑战，因为它们可能需要大量的用户投入和努力来实现。我们提出了一种基于贝叶斯神经网络（NN）类的新模型构建方法，该方法可以用更少的精力直接解决这一挑战。我们的方法可以对不同形状和AM工艺进行自动偏差建模，并且与现有建模方法相比，在相同的制造产品样本上可以产生具有更高预测精度的模型。我们方法学的一项根本创新是设计了新的可连接的NN结构，该结构便于利用先前指定的偏差模型来构建新形状和AM过程的自适应模型。通过在不同的立体光刻工艺下制造的各种形状的平面内和平面外偏差的几个案例研究，证明了我们方法的强大功能和广泛的应用范围。我们用于自动化和全面偏差建模的贝叶斯方法可以最终帮助在增材制造系统中推进灵活，高效和高质量的制造。通过在不同的立体光刻工艺下制造的各种形状的平面内和平面外偏差的几个案例研究，证明了我们方法的强大功能和广泛的应用范围。我们用于自动化和全面偏差建模的贝叶斯方法可以最终帮助在增材制造系统中推进灵活，高效和高质量的制造。通过在不同的立体光刻工艺下制造的各种形状的平面内和平面外偏差的几个案例研究，证明了我们方法的强大功能和广泛的应用范围。我们用于自动化和全面偏差建模的贝叶斯方法可以最终帮助在增材制造系统中推进灵活，高效和高质量的制造。执业者注意 —增材制造（AM）系统具有一种内在能力，可以在广泛的组成过程中一种种类繁多的形状制造。学习如何以全面的方式控制AM系统的几何形状精度对于其操作至关重要。由于可以制造的测试形状的数量受到限制，并且用户的工作可以专门用于学习和预测不同形状和AM过程的几何误差，因此这项任务具有挑战性。本文提出了一种自动化的机器学习方法，该方法可以基于在不同过程中制造的有限数量的测试形状，对AM系统中的几何误差进行全面学习和预测。