Abstract With the extensive application of mass customization, fast and accurate responses to customer inquiries can not only improve the competitive advantage of an enterprise but also reduce the cost of parts at the design stage. Most cost estimation methods establish a regression relationship between features and cost based on the processing features of parts. Traditional methods, however, encounter certain problems in feature recognition, such as the inaccurate recognition of processing features and low efficiency. Deep-learning methods have the ability to automatically learn complex high-level data features from a large amount of data, which are studied to recognize processing features and estimate the cost of parts. First, this study proposes a novel three-dimensional (3D) convolutional neural network (CNN) part-feature recognition method to achieve highly accurate feature recognition. Furthermore, an innovative method of using the quantity to express the identified features and establishing the relationship between them and cost is proposed. Then, support vector machine and back propagation (BP) neural network methods are employed to establish a regression relationship between the quantity and cost. Finally, in comparison with the mean absolute percentage error values, the BP neural network yields a more accurate estimation, which has considerable application potential.

中文翻译：

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基于加工过程和深度学习方法的制造成本估算

摘要 随着大规模定制的广泛应用，快速准确地响应客户咨询，不仅可以提高企业的竞争优势，还可以降低设计阶段的零部件成本。大多数成本估算方法是根据零件的加工特征建立特征与成本之间的回归关系。然而，传统方法在特征识别方面存在一定的问题，如处理特征识别不准确、效率低等。深度学习方法具有从大量数据中自动学习复杂的高级数据特征的能力，研究这些特征以识别加工特征和估算零件成本。第一的，本研究提出了一种新颖的三维 (3D) 卷积神经网络 (CNN) 部分特征识别方法，以实现高精度的特征识别。此外，提出了一种创新的方法，即使用数量来表示识别的特征，并建立它们与成本之间的关系。然后，采用支持向量机和反向传播（BP）神经网络方法建立数量和成本之间的回归关系。最后，与平均绝对百分比误差值相比，BP 神经网络产生了更准确的估计，具有相当大的应用潜力。提出了一种创新的方法，用数量来表达识别的特征，并建立它们与成本之间的关系。然后，采用支持向量机和反向传播（BP）神经网络方法建立数量和成本之间的回归关系。最后，与平均绝对百分比误差值相比，BP 神经网络产生了更准确的估计，具有相当大的应用潜力。提出了一种创新的方法，用数量来表达识别的特征，并建立它们与成本之间的关系。然后，采用支持向量机和反向传播（BP）神经网络方法建立数量和成本之间的回归关系。最后，与平均绝对百分比误差值相比，BP 神经网络产生了更准确的估计，具有相当大的应用潜力。