A Dynamic Demand-driven Smart Manufacturing for Mass Individualization Production

Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.

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Demystifying data and AI for manufacturing: case studies from a major computer maker

APSIPA Transactions on Signal and Information Processing ◽

10.1017/atsip.2021.3 ◽

2021 ◽

Vol 10 ◽

Author(s):

Yi-Chun Chen ◽

Bo-Huei He ◽

Shih-Sung Lin ◽

Jonathan Hans Soeseno ◽

Daniel Stanley Tan ◽

...

Keyword(s):

Training Data ◽

Smart Manufacturing ◽

Reliable System ◽

Process Innovations ◽

Optical Inspection ◽

Technical Details ◽

Automation Technology ◽

Electronic Parts ◽

Traditional Approaches ◽

Inspection Machine

In this article, we discuss the backgrounds and technical details about several smart manufacturing projects in a tier-one electronics manufacturing facility. We devise a process to manage logistic forecast and inventory preparation for electronic parts using historical data and a recurrent neural network to achieve significant improvement over current methods. We present a system for automatically qualifying laptop software for mass production through computer vision and automation technology. The result is a reliable system that can save hundreds of man-years in the qualification process. Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches. For production needs, we design an automatic optical inspection machine suitable for our algorithm and process. We also discuss the issues for data collection and enabling smart manufacturing projects in a factory setting, where the projects operate on a delicate balance between process innovations and cost-saving measures.

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Data-Driven Intelligent 3D Surface Measurement in Smart Manufacturing: Review and Outlook

Machines ◽

10.3390/machines9010013 ◽

2021 ◽

Vol 9 (1) ◽

pp. 13

Author(s):

Yuhang Yang ◽

Zhiqiao Dong ◽

Yuquan Meng ◽

Chenhui Shao

Keyword(s):

High Resolution ◽

High Performance ◽

Sampling Design ◽

Three Dimensional ◽

Measurement Data ◽

Data Driven ◽

Surface Measurement ◽

Smart Manufacturing ◽

Future Research ◽

3D Surface

High-fidelity characterization and effective monitoring of spatial and spatiotemporal processes are crucial for high-performance quality control of many manufacturing processes and systems in the era of smart manufacturing. Although the recent development in measurement technologies has made it possible to acquire high-resolution three-dimensional (3D) surface measurement data, it is generally expensive and time-consuming to use such technologies in real-world production settings. Data-driven approaches that stem from statistics and machine learning can potentially enable intelligent, cost-effective surface measurement and thus allow manufacturers to use high-resolution surface data for better decision-making without introducing substantial production cost induced by data acquisition. Among these methods, spatial and spatiotemporal interpolation techniques can draw inferences about unmeasured locations on a surface using the measurement of other locations, thus decreasing the measurement cost and time. However, interpolation methods are very sensitive to the availability of measurement data, and their performances largely depend on the measurement scheme or the sampling design, i.e., how to allocate measurement efforts. As such, sampling design is considered to be another important field that enables intelligent surface measurement. This paper reviews and summarizes the state-of-the-art research in interpolation and sampling design for surface measurement in varied manufacturing applications. Research gaps and future research directions are also identified and can serve as a fundamental guideline to industrial practitioners and researchers for future studies in these areas.

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Multi-Scale Convolutional Gated Recurrent Unit Networks for Tool Wear Prediction in Smart Manufacturing

Chinese Journal of Mechanical Engineering ◽

10.1186/s10033-021-00565-4 ◽

2021 ◽

Vol 34 (1) ◽

Author(s):

Weixin Xu ◽

Huihui Miao ◽

Zhibin Zhao ◽

Jinxin Liu ◽

Chuang Sun ◽

...

Keyword(s):

Tool Wear ◽

Information Technologies ◽

Manufacturing Systems ◽

Smart Manufacturing ◽

Wear Prediction ◽

Multi Scale ◽

Tool Wear Prediction ◽

Machine Health Monitoring ◽

Gated Recurrent Unit ◽

Unit Network

AbstractAs an integrated application of modern information technologies and artificial intelligence, Prognostic and Health Management (PHM) is important for machine health monitoring. Prediction of tool wear is one of the symbolic applications of PHM technology in modern manufacturing systems and industry. In this paper, a multi-scale Convolutional Gated Recurrent Unit network (MCGRU) is proposed to address raw sensory data for tool wear prediction. At the bottom of MCGRU, six parallel and independent branches with different kernel sizes are designed to form a multi-scale convolutional neural network, which augments the adaptability to features of different time scales. These features of different scales extracted from raw data are then fed into a Deep Gated Recurrent Unit network to capture long-term dependencies and learn significant representations. At the top of the MCGRU, a fully connected layer and a regression layer are built for cutting tool wear prediction. Two case studies are performed to verify the capability and effectiveness of the proposed MCGRU network and results show that MCGRU outperforms several state-of-the-art baseline models.

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