Quality Prediction and Control in Coal-Fired Rotary Kilns at Tata Sponge Iron Ltd.

2019 ◽  
Author(s):  
S. Sahoo ◽  
P. Deo ◽  
P. Chaudhary ◽  
B. Malakar ◽  
P. Mondal ◽  
...  
Keyword(s):  
Sponge Iron ◽  
Quality Prediction ◽  
Prediction And Control ◽  
Rotary Kilns ◽  
And Control

scholarly journals Dynamic quality prediction and control in rotary sponge iron kilns

2020 ◽  
Vol 872 ◽  
pp. 012077
Author(s):  
Shibu Meher ◽  
Puneet Choudhary ◽  
Vijay Surya Vempati ◽  
Brahma Deo ◽  
Partho Chattopadhyay
Keyword(s):  
Sponge Iron ◽  
Quality Prediction ◽  
Prediction And Control ◽  
Dynamic Quality ◽  
And Control

scholarly journals Quality Prediction and Control of Assembly and Welding Process for Ship Group Product Based on Digital Twin

Scanning ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Lei Li ◽  
Di Liu ◽  
Jinfeng Liu ◽  
Hong-gen Zhou ◽  
Jiasheng Zhou
Keyword(s):  
Quality Control ◽  
Welding Process ◽  
Quality Prediction ◽  
Twin Data ◽  
Time Data ◽  
Digital Twin ◽  
Twin Model ◽  
Prediction And Control ◽  
And Control ◽  
Group Product

In view of the problems of lagging and poor predictability for ship assembly and welding quality control, the digital twin technology is applied to realize the quality prediction and control of ship group product. Based on the analysis of internal and external quality factors, a digital twin-based quality prediction and control process was proposed. Furthermore, the digital twin model of quality prediction and control was established, including physical assembly and welding entity, virtual assembly and welding model, the quality prediction and control system, and twin data. Next, the real-time data collection based on the Internet of Things and the twin data organization based on XML were used to create a virtual-real mapping mechanism. Then, the machine learning technology is applied to predict the process quality of ship group products. Finally, a small group is taken as an example to verify the proposed method. The results show that the established prediction model can accurately evaluate the welding angular deformation of group products and also provide a new idea for the quality control of shipbuilding.

Quality prediction and control of tube hollow

2011 ◽  
Vol 18 (3) ◽  
pp. 767-772
Author(s):  
Dong Xiao ◽  
Ji-chun Wang ◽  
Xiao-li Pan ◽  
Zhi-zhong Mao
Keyword(s):  
Quality Prediction ◽  
Prediction And Control ◽  
And Control

An insight into Case Management and Challenges in Veteran Suicide Prevention

2020 ◽  
pp. 14-25
Author(s):  
Tamara Green
Keyword(s):  
Case Management ◽  
Suicide Prevention ◽  
Suicide Risk ◽  
Digital Transformation ◽  
Prevention Science ◽  
Historical Records ◽  
Military Members ◽  
Prediction And Control ◽  
And Control ◽  
Insight Into

Much of the literature, policies, programs, and investment has been made on mental health, case management, and suicide prevention of veterans. The Australian “veteran community is facing a suicide epidemic for the reasons that are extremely complex and beyond the scope of those currently dealing with them.” (Menz, D: 2019). Only limited work has considered the digital transformation of loosely and manual-based historical records and no enablement of Artificial Intelligence (A.I) and machine learning to suicide risk prediction and control for serving military members and veterans to date. This paper presents issues and challenges in suicide prevention and management of veterans, from the standing of policymakers to stakeholders, campaigners of veteran suicide prevention, science and big data, and an opportunity for the digital transformation of case management.

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