scholarly journals A Cyber-physical System Architecture in Shop Floor for Intelligent Manufacturing

Procedia CIRP ◽  
2016 ◽  
Vol 56 ◽  
pp. 372-377 ◽  
Author(s):  
Chao Liu ◽  
Pingyu Jiang
Keyword(s):  
System Architecture ◽  
Physical System ◽  
Intelligent Manufacturing ◽  
Shop Floor ◽  
Cyber Physical System

An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Ming-Chuan Chiu ◽  
Chien-De Tsai ◽  
Tung-Lung Li
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Fault Detection ◽  
Real Time ◽  
Physical System ◽  
Shop Floor ◽  
Cyber Physical System ◽  
Machine Learning Method ◽  
Learning Method ◽  
The Impact

Abstract A cyber-physical system (CPS) is one of the key technologies of industry 4.0. It is an integrated system that merges computing, sensors, and actuators, controlled by computer-based algorithms that integrate people and cyberspace. However, CPS performance is limited by its computational complexity. Finding a way to implement CPS with reduced complexity while incorporating more efficient diagnostics, forecasting, and equipment health management in a real-time performance remains a challenge. Therefore, the study proposes an integrative machine-learning method to reduce the computational complexity and to improve the applicability as a virtual subsystem in the CPS environment. This study utilizes random forest (RF) and a time-series deep-learning model based on the long short-term memory (LSTM) networking to achieve real-time monitoring and to enable the faster corrective adjustment of machines. We propose a method in which a fault detection alarm is triggered well before a machine fails, enabling shop-floor engineers to adjust its parameters or perform maintenance to mitigate the impact of its shutdown. As demonstrated in two empirical studies, the proposed method outperforms other times-series techniques. Accuracy reaches 80% or higher 3 h prior to real-time shutdown in the first case, and a significant improvement in the life of the product (281%) during a particular process appears in the second case. The proposed method can be applied to other complex systems to boost the efficiency of machine utilization and productivity.

scholarly journals A Study on Cyber-physical System Architecture to Predict Cutting Tool Condition in Machining

2020 ◽  
pp. 565-569
Author(s):  
Yasuo Kondo ◽  
Mitsugu Yamaguchi ◽  
Satoshi Sakamoto ◽  
Kenji Yamaguchi
Keyword(s):  
System Architecture ◽  
Physical System ◽  
Cutting Tool ◽  
Cyber Physical System ◽  
Tool Condition

Analyzing the cyber-physical system–based autonomous collaborations among smart manufacturing resources in a smart shop floor

2019 ◽  
Vol 234 (3) ◽  
pp. 489-500 ◽  
Author(s):  
Kai Ding ◽  
Jingyuan Lei ◽  
Fuqiang Zhang ◽  
Yan Wang ◽  
Chuang Wang
Keyword(s):  
Physical System ◽  
Production Control ◽  
Small World ◽  
Smart Manufacturing ◽  
Shop Floor ◽  
Collaboration Network ◽  
Cyber Physical System ◽  
Design Data ◽  
Complex Network Theory ◽  
Manufacturing Resources

Industry 4.0 focuses on the realization of smart manufacturing from shop floors to factories and to the whole supply chain. As a key technology of smart manufacturing, cyber-physical system has been widely discussed in the aspects of system design, data collection and processing, and cyber-physical synchronization. In a smart shop floor, manufacturing resources with intelligence and autonomy are abstracted as cyber-physical system units. They can communicate with each other autonomously to make optimal production decisions according to the real-time status of the shop floor. In this article, an autonomous collaboration network comprised of cyber-physical system–based smart manufacturing resources is modeled by using complex network theory. The collaboration activities among them are further analyzed, from which the information of key cyber-physical system units and key collaboration relationships are excavated. A demonstrative case is studied to verify the feasibility of the proposed model. From the case, it can be seen that (1) autonomous collaboration network has a small-world feature; (2) cyber-physical system units with bigger degrees and the collaborative relationships with bigger tightness are more important; (3) the workload of cyber-physical system units needs to be balanced because some cyber-physical system units have exceeded their capacities; and (4) cyber-physical system units with larger collaboration clustering coefficients will attract other nodes to form communities centered by them. Based on these results, the autonomous production control and management of smart shop floor will become more accurate, efficient, and balanced.

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