Digital twin-driven product design, manufacturing and service with big data

2017 ◽  
Vol 94 (9-12) ◽  
pp. 3563-3576 ◽  
Author(s):  
Fei Tao ◽  
Jiangfeng Cheng ◽  
Qinglin Qi ◽  
Meng Zhang ◽  
He Zhang ◽  
...  
Keyword(s):  
Big Data ◽  
2021 ◽  
Vol 1122 (1) ◽  
pp. 012064
Author(s):  
Juliza Hidayati ◽  
Shelvy Riry Gusrina ◽  
Nazaruddin Matondang

Author(s):  
Sendong Ren ◽  
Yunwu Ma ◽  
Ninshu Ma ◽  
Qian Chen ◽  
Haiyuan Wu

Abstract In the present research, a digital twin of coaxial one-side resistance spot welding (COS-RSW) was established for the real-time prediction of transient temperature field. A 3D model of COS-RSW joint was developed based on the in-house finite element (FE) code JWRIAN-SPOT. The experimental verified FE model was employed to generate the big data of temperature of COS-RSW process. Multiple dimension interpolation was applied to process database and output prediction. The FE model can predict the thermal cycle on COS-RSW joints under different parameter couples. The interpolation effect of individual welding parameters was discussed and a power weight judgement for welding time was essential to ensure accuracy. With the support of big data, the digital twin can provide visualization prediction of COS-RSW within 10 seconds, whereas numerical modelling needs at least 1 hour. The proposed application of digital twin has potential to improve the efficiency of process optimization in engineering.


Author(s):  
Fei Tao ◽  
Meng Zhang ◽  
A.Y.C. Nee
Keyword(s):  
Big Data ◽  

2020 ◽  
Author(s):  
Zakharov L.A ◽  
Derksen L.A.

This article describes of hardware and software infrastructure that provides the implementation of digital double technology. The basic approaches to determining the technologies that make up the infrastructure for the implementation of the digital twin, as well as the benefits of implementing this technology are considered. The need for processing and storing big data, as well as the benefits of implementing this technology, is substantiated. Keywords: digital twin, digital model, big data, product lifecycle, cyber-physical system, automation, machine learning, smart maintenance.


Author(s):  
Holey Ajay ◽  
Alandikar Shashank

Abstract In a manufacturing assembly line scenario, factory layout is one of the most crucial information used by manufacturing, facility and factory automation engineers for planning purposes. It is important for manufacturing, facility and operations team to work with most up-to-date layout when product, process and operational information on the shop-floor is constantly changing. There are four elements which governs availability of a real-time layout, these are nothing but Product Design, Manufacturing Process Planning, Layout Planning and Shop-floor. The layout must accommodate these changes coming from product design, process updates and shop-floor modifications on real-time basis so that there is no confusion amongst the stakeholders while referring layout data for their planning purpose. If we talk about the impact on the layout because of product design and process design, it is hardly managed real-time due to the isolated systems to manage these data. The integration of product, process and plant (PPP) is becoming crucial to facilitate collaboration and shrink new product introduction lead time where as real-time update from the shop-floor changes is expected in the era of digital transformation. One of the reasons why the integration of product, process and plant (PPP) does not happen is multiple isolated systems used to maintain this data, there are also challenges to feed data back from the shop-floor because of the non-availability of the thread between these objects. The paper is about how factory layout can be developed integrating product, process and plant (PPP) in a single dynamic environment and establish a digital thread between the product design, manufacturing process planning and factory layout to trigger real-time changes and facilitate digital twin of the factory. The methodology adopted here is to develop bill of material for manufacturing resources and align it with the product data management. This approach not only provides ability to maintain change control over resource objects but also helps in configuration management of the resource bill of material. The resources are grouped together as layout structure for the plant with each object required to manufacture the product. The detailed layout developed for the plant while integrating with product and process is used to establish connection with objects on the shop-floor through sensors and IOT (Internet of Things) devices to form digital twin. Such details added in layout which is So far there are no efforts to digitalize every information on the factory floor and able to generate Digital Twin of the factory by connecting physical objects with the digital objects. Paper will elaborate the approach to establish digital thread between PPP and how this can become foundation to drive digital twin of the factory.


2021 ◽  
Vol 3 ◽  
Author(s):  
Isuru A. Udugama ◽  
Merve Öner ◽  
Pau C. Lopez ◽  
Christan Beenfeldt ◽  
Christoph Bayer ◽  
...  

Digitalization in the form of Big Data and Digital Twin inspired applications are hot topics in today's bio-manufacturing organizations. As a result, many organizations are diverting resources (personnel and equipment) to these applications. In this manuscript, a targeted survey was conducted amongst individuals from the Danish biotech industry to understand the current state and perceived future obstacles in implementing digitalization concepts in biotech production processes. The survey consisted of 13 questions related to the current level of application of 1) Big Data analytics and 2) Digital Twins, as well as obstacles to expanding these applications. Overall, 33 individuals responded to the survey, a group spanning from bio-chemical to biopharmaceutical production. Over 73% of the respondents indicated that their organization has an enterprise-wide level plan for digitalization, it can be concluded that the digitalization drive in the Danish biotech industry is well underway. However, only 30% of the respondents reported a well-established business case for the digitalization applications in their organization. This is a strong indication that the value proposition for digitalization applications is somewhat ambiguous. Further, it was reported that digital twin applications (58%) were more widely used than Big Data analytic tools (37%). On top of the lack of a business case, organizational readiness was identified as a critical hurdle that needs to be overcome for both Digital Twin and Big Data applications. Infrastructure was another key hurdle for implementation, with only 6% of the respondents stating that their production processes were 100% covered by advanced process analytical technologies.


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