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Digital Twin Transforming Manufacturing
Digital Twin Transforming Manufacturing
ICT and Sensor Technology Convergence Drive Opportunities in Smart Factories
31-May-2018
Global
$4,950.00
Special Price $3,712.50 save 25 %
Description
The concept of creating a replica of equipment or a system, or physical asset called digital twin is gaining interest in the manufacturing sector. Technologies such as artificial intelligence, advanced software platforms, communication, big data analytics, and sensors are enabling improvements in efficiency and productivity. Mixed reality, holographic displays, robots, drones, and wearables are also largely driving the IoT trend in factories. A whole new dimension of optimizing performance of industrial assets can be achieved with convergence of advanced information technology, new sensing platforms, and physics based models. Concepts such as Digital Twin can help manufacturing industries improve productivity, and operational efficiency, while optimizing resources, time, and cost. Major tier firms are developing simulation software including complete solution to create digital twin for optimizing performance in manufacturing plants. The key question for most of the organizations is about the benefits of the concept versus leveraging simulation platforms.
The report on “Digital Twin Transforming Manufacturing” aims to address the benefit and the impact of the concept in a manufacturing set-up including implementation strategies. The report will be beneficial for stakeholders interested in creating digital factories. The report also covers factors that influence the adoption scenario and also provides examples of implementation cases.
Table of Contents
1.1 Research Scope
1.2 Research Methodology
1.3 Explanation of Research Methodology
1.4 Summary of Key Findings
1.4 Summary of Key Findings (continued)
2.1 Digital Twin – Brief Overview
2.2 Evolution of DT
2.3 Segmentation of the Virtual Descriptive World
2.4 Digital Twin: Benefits and Use Case
2.5 Step by Step Transformation of Manufacturing using DT: Product Impact
2.6 Digital Twin in a Production Line
2.7 Factors Driving Adoption
2.7 Factors Driving Adoption (continued)
2.8 Adoption Barriers
2.9 Technology Convergence Leads to Digital Twin
3.1 Three Types of Digital Twins
3.2 ANSYS’ Digital Twin Platform
3.3 Hero MotoCorp Digital Twin Project
3.4 Veerum Digital Twin Solution for the Oil and Gas industry
3.5 Precognize Machine Learning Platforms
3.6 DVN GL Digital Twin for Oil and Gas
3.7 Maplesoft and Rockwell Automation – Project Sherlock AI
3.8 Key Innovations Shaping the Future
3.9 Other Major Players
3.10 Businesses Swiftly Managing What-If Scenarios
4.1 Analyst Viewpoint
4.2 Should You Opt for Digital Change? Is it Right for You?
4.3 What Sort of Skills Companies Need to Acquire to Make the Transition?
4.4 What Should Manufacturers Do for a Implementing a Successful Digitized Infrastructure?
4.5 What Business Value will Digital Twin Bring to Companies?
4.6 Digital Twin is part of the Digital Thread
5.1 Key Patents on AR and Digital Twin
5.2 Key Patents on Machine Learning in Manufacturing Sector
5.2 Key Patents on Machine Learning in Manufacturing Sector (continued)
5.3 Key Contacts
Legal Disclaimer
Popular Topics
| No Index | No |
|---|---|
| Podcast | No |
| Author | Ranjana Lakshmi Venkatesh Kumar |
| Industries | Industrial Automation |
| WIP Number | D81C-01-00-00-00 |
| Is Prebook | No |
| Ti Codes | 9A37-C1,9B07-C1,D901,D903,D90A,D90D,D910,D91C,D941 |