Advanced Engineering Visualization with Standardized 3D Formats

The simulation of production processes using a Digital Twin is a promising tool for predictive planning, analysis of existing systems or process-parallel monitoring. In the process industry, the concept of Digital Twin provides significant support for process optimization. The generation of the Digital Twin of an already existing plant is a major challenge – in particular for small and medium-sized enterprises. In this sense, the twinning of the existing physical environment has got a particular importance due to high effort. Shape segmentation from unstructured (e.g. point cloud data) is a core step of the digital twinning process for industrial facilities. This is an inherent issue of Product Lifecycle Management how to acquire data of existing goods. The practice of Digital Twin is described based on object recognition by using methods of Machine Learning. The exploration of the pipeline semantics presents a particular challenge. The highly automated procedure for the generation of Digital Twin is described based on a use case of a biogas plant. Commercial deployment, pitfalls, drawbacks and potential for further developments are further explored.

Section: Process Definitionmentioning

confidence: 99%

Automated Generation of a Digital Twin of a Process Plant by Using 3D Scan and Artificial Intelligence

Grau¹,

Korol²,

Lützenberger³

et al. 2021

“…Product Lifecycle Management (PLM) is widely understood as concept for the creation, processing, storage, and retrieval of data, information and, ideally, knowledge throughout the lifecycle of a product from its conceptualization or inception to its disposal, recycling or recovery [17]. PLM is seen in the industry as one of the core concepts to fulfil a number of business requirements in the manufacturing industry with respect to the completeness, high transparency, rapid accessibility and high visibility of all product data during a product's lifecycle [18]. Those requirements also are related to financial aspects like cost management and revenue growth, to the product itself like innovation, competitive functionality, time to market, quality and high productivity, and to regulatory aspects as compliance, product risk management and documentation.…”

Section: Product Lifecycle Management (Plm)mentioning

confidence: 99%

Modular Approach to Technical Risk Management in Product Lifecycle Management

Castaneda¹,

Majić²,

Ostrosi³

et al. 2019

The current trend in product development is toward usage and integration of different advanced technologies which are increasingly embedded in a comprehensive PDM system as a company-wide information back-bone. With such an approach, the transparency is increased and many business processes accelerated. While modularization is a mega-trend, the modular approach is also used to align development processes with the workflows in PDM. The goal of an effective risk management system is to allow continuous business running and achieving the goals of an organization. In order to achieve this, it is necessary, as far as it is possible, to identify and to analyze risks the organization is confronting and to evaluate their effects on the business. In this paper, an approach is presented on how to integrate the technical risk management in Product Lifecycle Management by definition of an additional software module and its implementation.

“…At the same time, database reconciliation enables the use of simpler and cheaper scanning methods [12]. The definition of suitable interfaces enables the transfer of information into a program for simulation of production systems and a precisely fitting Digital Twin of the manufacturing can be generated -almost without manual interventions [13] [14].…”

Section: Introductionmentioning

confidence: 99%

Automated Generation of a Digital Twin of a Manufacturing System by Using Scan and Convolutional Neural Networks

Sommer¹,

Stjepandić²,

Stobrawa³

et al. 2020

The simulation of production processes using a Digital Twin is a promising means for prospective planning, analysis of existing systems or process-parallel monitoring. However, many companies, especially small and medium-sized enterprises, do not apply the technology, because the generation of a Digital Twin is cost-, time- and resource-intensive and IT expertise is required. This obstacle can be removed by a novel approach to generate a Digital Twin using fast scans of the shop floor and subsequent object recognition in the point cloud. We describe how parameters and data should be acquired in order to generate a Digital Twin automatically. An overview of the entire process chain is given. A particular attention is given to the automatic object recognition and its integration into Digital Twin.