Challenges in Developing a Computational Platform to Integrate Data Analytics With Simulation-Based Optimization

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy
Keyword(s):  
Simulation Model ◽  
Information Exchange ◽  
Data Analytics ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Modeling Tools ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Computational Platform ◽  
Smart Manufacturing Systems

The focus of the work presented in this paper is to identify and find possible solutions for major implementation challenges in designing a computational platform for integrating data analytics paradigm with the simulation-based optimization technique to facilitate the modeling of a smart manufacturing system. A simulation model of a manufacturing system generates real-time monitoring data for machine status and these data are then mined by data mining algorithms to discover hidden knowledge that might not be predefined in the simulation model. The new knowledge is then fed into the simulation model such that the model adapts and evolves, and eventually it can predict future status. This procedure involves heterogeneous modeling techniques, information exchange among different tools, as well as model composition and interaction. We extend an early presented “Hypercube” information model that was specifically developed for the purpose of formal representation of smart manufacturing systems, in order to harmonize the information required by the simulation modeling tool and the data analytics tool. A strong emphasis is given to emerging areas of multi-domain and multiscale modeling by means of integration and interoperability between existing modeling tools and technologies. A specific case study related to preventive and predictive maintenance of a typical manufacturing system has been elaborated in the paper as the initial scope and application area in order to illustrate and validate the proposed computational framework.

scholarly journals Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems

2019 ◽  
Author(s):  
Alireza Zarreh ◽  
HungDa Wan ◽  
Yooneun Lee ◽  
Can Saygin ◽  
Rafid Al Janahi
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Performance Indicator ◽  
Negative Impact ◽  
Smart Manufacturing ◽  
Network Systems ◽  
Total Productive Maintenance ◽  
Core Function ◽  
Traditional Manufacturing ◽  
Smart Manufacturing Systems

Maintenance is the core function to keep a system running and avoid failure. Total Productive Maintenance (TPM) has broadly utilized maintenance strategy to improve the customer's satisfaction and hence obtain a competitive advancement. However, the complexity of smart manufacturing systems due to the recent advancements, specifically the integration of internet and network systems with traditional manufacturing platforms, has made this function more challenging. The focus of this paper is to explain how cybersecurity could impact the TPM by affecting the overall equipment effectiveness (OEE) in a smart manufacturing system by providing a structured literature survey. First, it provides concerns on principle of TPM regarding cybersecurity in smart manufacturing systems. Then, it highlights the effect of a variety of cyber-physical threats on OEE, as a main key performance indicator of TPM and how differently they can reduce OEE. The countermeasures that could be considered to compensate for the negative impact of a cybersecurity threat on the overall effectiveness of the system also will be discussed. Finally, research gaps and challenges are identified to improve overall equipment effectiveness (OEE) in presence of cybersecurity threats in critical manufacturing industries.

An integrated framework of enterprise information systems in smart manufacturing system via business process reengineering

2018 ◽  
Vol 233 (11) ◽  
pp. 2210-2224 ◽  
Author(s):  
Yuanju Qu ◽  
Xinguo Ming ◽  
Yanrong Ni ◽  
Xiuzhen Li ◽  
Zhiwen Liu ◽  
...  
Keyword(s):  
Information Systems ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Smart Manufacturing ◽  
Process Reengineering ◽  
Enterprise Information ◽  
Enterprise Information Systems ◽  
Traditional Manufacturing ◽  
Systems Framework ◽  
Smart Manufacturing Systems

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.

A hybrid simulation-based assessment framework of smart manufacturing systems

2017 ◽  
Vol 31 (2) ◽  
pp. 115-128 ◽  
Author(s):  
Khalid Nagadi ◽  
Luis Rabelo ◽  
Mohammed Basingab ◽  
Alfonso T. Sarmiento ◽  
Albert Jones ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Hybrid Simulation ◽  
Smart Manufacturing ◽  
Assessment Framework ◽  
Simulation Based ◽  
Smart Manufacturing Systems

scholarly journals Adaptive Multi-scale Prognostics and Health Management for Smart Manufacturing Systems

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Benjamin Y. Choo ◽  
Stephen C. Adams ◽  
Brian A. Weiss ◽  
Jeremy A. Marvel ◽  
Peter A. Beling
Keyword(s):  
Manufacturing Systems ◽  
Manufacturing System ◽  
Health Management ◽  
Smart Manufacturing ◽  
Health State ◽  
Prognostics And Health Management ◽  
Multi Scale ◽  
Decision Points ◽  
The Hierarchical Structure ◽  
Smart Manufacturing Systems

The Adaptive Multi-scale Prognostics and Health Management (AM-PHM) is a methodology designed to enable PHM in smart manufacturing systems. In application, PHM information is not yet fully utilized in higher-level decisionmaking in manufacturing systems. AM-PHM leverages and integrates lower-level PHM information such as from a machine or component with hierarchical relationships across the component, machine, work cell and assembly line levels in a manufacturing system. The AM-PHM methodology enables the creation of actionable prognostic and diagnostic intelligence up and down the manufacturing process hierarchy. Decisions are then made with the knowledge of the current and projected health state of the system at decision points along the nodes of the hierarchical structure. To overcome the issue of exponential explosion of complexity associated with describing a large manufacturing system, the AM-PHM methodology takes a hierarchical Markov Decision Process (MDP) approach into describing the system and solving for an optimized policy. A description of the AM-PHM methodology is followed by a simulated industry-inspired example to demonstrate the effectiveness of AM-PHM.

Sign in / Sign up

Export Citation Format

Share Document