Enterprise information systems play a significant role in the Industry 4.0 era and are the crucial component to realize smart manufacturing systems. However, traditional enterprise information systems have some limits: (1) lack of complete information, (2) only satisfy limited business needs, and (3) lack of seamless integration, business intelligence, value-driven processes, and dynamic optimization. Clearly, the existing enterprise information systems are unable to satisfy the requirements for smart manufacturing systems: (1) autonomous operation, (2) sustainable values, and (3) self-optimization. In addition, smart manufacturing systems have become more efficient and effective, demanding for seamless information flow in enterprise information systems, knowledge, and data-driven accurately decision. Therefore, a new enterprise information systems framework is needed to bridge gaps between the requirements for traditional manufacturing system and smart manufacturing system. In this article, the integrative framework is proposed based on the business process reengineering, lean thinking, and intelligent management methods, with inclusion of six enterprise information systems aspects to provide upgrading guidelines from traditional manufacturing to smart manufacturing. The procedure of this method contains three steps: (1) it identifies requirements and acquires best practices using AS-IS model, (2) it redesigns six aspects of enterprise information systems using TO-BE model, and (3) it proposes a new enterprise information systems framework. Finally, the proposed framework is validated by real cases.
We present a new data generation method to facilitate an automatic machine interpretation of 2D engineering part drawings. While such drawings are a common medium for clients to encode design and manufacturing requirements, a lack of computer support to automatically interpret these drawings necessitates part manufacturers to resort to laborious manual approaches for interpretation which, in turn, severely limits processing capacity. Although recent advances in trainable computer vision methods may enable automatic machine interpretation, it remains challenging to apply such methods to engineering drawings due to a lack of labeled training data. As one step toward this challenge, we propose a constrained data synthesis method to generate an arbitrarily large set of synthetic training drawings using only a handful of labeled examples. Our method is based on the randomization of the dimension sets subject to two major constraints to ensure the validity of the synthetic drawings. The effectiveness of our method is demonstrated in the context of a binary component segmentation task with a proposed list of descriptors. An evaluation of several image segmentation methods trained on our synthetic dataset shows that our approach to new data generation can boost the segmentation accuracy and the generalizability of the machine learning models to unseen drawings.
In this paper, we propose a real-time shape-assisted graph attention neural network to perform local pointcloud repairment. The orderless pointclouds require an effective shape encoder to distill local and global geometric feature descriptors. Previous work has attempted to convert pointcloud representation into a voxelized shape or perform grid-transformations. While these approaches can subsequently allow common convolution operations on the structured data, they either pose additional computational cost or disrupt the local geometric information.
We present SAGA-Net, an efficient graph attention neural network framework with a prior shape inquiry protocol that effectively extracts local geometric information, locates the descriptor for the missing region and accurately reconstructs the local region in a real-time manner. Our framework has been validated on a benchmark dataset, ShapeNet.
We demonstrate that our framework can repair each partial pointcloud with accuracy surpassing other frameworks in most object categories, and improve the computational efficiency by orders of magnitude in terms of time cost.
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