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Data Driven Smart Manufacturing Technologies and Applications

Part of the Springer Series in Advanced Manufacturing Book Series

Editors: Li, Weidong; Liang, Yuchen; Wang, Sheng

DOI: 10.1007/978-3-030-66849-5

ISBN: 978-3-030-66848-8

Publisher: Springer International Publishing - Springer Journals

Feb 21, 2021

1 - 9 of 9 Chapters

Available

Introduction

Li, W. D.; Liang, Y. C.; Wang, S.

Pages: 1 –12

[In the past century, the manufacturing industry has undergone significant paradigm shifts, from mass production (Ford assembly line) (1900s) to lean manufacturing (Toyota production system) (1960s), flexible manufacturing (mass customization) (1980s), reconfigurable manufacturing (1990s),...
Available

Fog Computing and Convolutional Neural Network Enabled Prognosis for Machining Process Optimization

Liang, Y. C.; Li, W. D.; Lu, X.; Wang, S.

Pages: 13 –35

[CloudCloudenabled prognosisPrognosis systems have been increasingly adopted by manufacturing industries. The effectiveness of the cloud systems is, however, crippled by the high latency of data transfer between shop floors and the cloud. To overcome the limitation, this chapter presents an...
Available

Big Data Enabled Intelligent Immune System for Energy Efficient Manufacturing Management

Wang, S.; Liang, Y. C.; Li, W. D.

Pages: 37 –64

[The Big Data driven approach has become a new trend for manufacturing optimization. In this chapter, an innovative Big Data enabled Intelligent Immune System (I2S) has been developed to monitor, analyze and optimize machining processes over lifecycles in order to achieve energy efficient...
Available

Adaptive Diagnostics on Machining Processes Enabled by Transfer Learning

Liang, Y. C.; Li, W. D.; Wang, S.; Lu, X.

Pages: 65 –90

[Faults on machines or cutting tooling during machining processes generate negative impacts on productivity, production quality and scrap rate. Effective diagnostics to identify faults throughout the lifecycle of a machining process adaptively is foremost for achieving overall manufacturing...
Available

CNN-LSTM Enabled Prediction of Remaining Useful Life of Cutting Tool

Zhang, X. Y.; Lu, X.; Li, W. D.; Wang, S.

Pages: 91 –123

[To enhance production quality, productivityHurst Exponent and energy consumption, it is paramount to predict the Remaining Useful LifeRemaining Useful Life (RUL) (RUL) of a cutting tool accurately and efficiently. Deep learningDeep learning algorithm-driven approaches have been actively explored...
Available

Thermal Error Prediction for Heavy-Duty CNC Machines Enabled by Long Short-Term Memory Networks and Fog-Cloud Architecture

Liang, Y. C.; Li, W. D.; Lou, P.; Hu, J. M.

Pages: 125 –150

[Heavy-duty CNC Computerized Numerical Control (CNC)machineHeavy-duty CNC machines are important equipment in manufacturing large-scale and high-end products. During the machining processes, a significant amount of heat is generated to bring working temperatures rising, which leads to deformation...
Available

Deep Transfer Learning Enabled Estimation of Health State of Cutting Tools

Marei, M.; Zaataria, S. El; Li, W. D.

Pages: 151 –172

[Effective Prognostics and Health ManagementPrognostics and Health Management (PHM) (PHM) for cutting tools during Computerized Numerical Control (CNC)Computerized Numerical Control (CNC) processes can significantly reduce downtime and decrease losses throughout manufacturing processes. In recent...
Available

Prediction of Strength of Adhesive Bonded Joints Based on Machine Learning Algorithm and Finite Element Analysis

Liang, Y. C.; Liu, Y. D.; Li, W. D.

Pages: 173 –190

[Adhesive bonded jointsAdhesive bonded joints are one of important joining technologies in supporting various manufacturing applications. It is important to predict the optimal strength of adhesive bonded joints in order to fit design requirements. Prediction on joint strengths is usually based...
Available

Enhancing Ant Colony Optimization by Adaptive Gradient Descent

Zhou, Y.; Li, W. D.; Wang, X.; Qiu, Q.

Pages: 191 –215

[Ant Colony OptimizationAnt Colony Optimization (ACO) (ACO) is one of the widely used metaheuristic algorithms applicable to various optimization problems. The ACO design has been inspired by the foraging behavior of ant colonies. The performance of the algorithm, however, is not satisfactory...

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