Digital twin modeling

Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.

https://www.mdpi.com/2071-1050/13/5/2495/pdf

Digital Twin Technologies for Turbomachinery in a Life Cycle Perspective: A Review

The present paper provides an overview of the state-of-the-art research, outlining the applications of the Industry 4.0 (I4.0) technologies on the aircraft manufacturing sector and their maturity state based on the technology readiness level (TRL) scale. A literature review has been conducted for the identification, selection, and evaluation of the published research. A total of 57 papers extracted from the two most relevant scientific databases for the area (Web of Science and Scopus), from 2010 to March 2021, were analysed and summarized. The research, analysis, and evaluation of these papers has provided an outlook of how the aircraft manufacturing industry is inserted into the I4.0 context, based on a classification of the I4.0 technologies maturity for this industrial branch. Then, a survey was performed with 12 specialists from 5 different aircraft manufacturing companies aiming to report the practical point-of-view in this area. Thus, this paper highlights and discusses the gaps found in the literature related to the I4.0 technologies applied to aircraft manufacturing and their main useful implications not only from the academic point-of-view but also from competitive business aspects, providing recommendations for industrial managers, engineers, and stakeholders. Finally, this paper proposes new opportunities and challenges for future research. 

https://link.springer.com/content/pdf/10.1007/s00170-022-08769-1.pdf

Readiness levels of Industry 4.0 technologies applied to aircraft manufacturing—a review, challenges and trends

One of the most important criteria in the production of modern aircraft is the length of the production cycle (lead-time). This indicator largely determines the commercial product success. In the framework of this work, issues of ensuring electronic layout and configuration integrity at various life cycle stages of a modern aircraft. The problem started in one aircraft enterprise in case of impossibility to provide assembly ability of different parts. A serious problem is the designation of tolerances on the parts dimensions. A model for eliminating contradictions in deviations created. One of the most important research questions is the issue of reducing the duration of the production cycle through optimization and control of the electronic layout and product configuration. Using modern approaches for setting product dimensional chains, using electronic layout optimization - a list of recommendations for the product design process developed. A methodology compiled to save the integrity of the digital layout at the various aircraft design stages. Was created simulating model which describe all assembly stages. The influence of configuration integrity loss on the product cycle time was shown.

Development of a model for analyzing the impact of configuration integrity loss on the aircraft product cycle time duration

Assembly accuracy of aeroengines influences operation performance and service life. The coaxiality of the aeroengine is the main index of assembly accuracy and is also a core index to represent assembly quality. However, direct measurement of coaxiality is a difficult technical problem due to the sealed structure of the aeroengine casing system. A coaxiality prediction method is proposed to obtain coaxiality and assist assembly by geometric distribution error modeling and point cloud deep learning. The prediction process consists of three steps. In the beginning, the geometric distribution error model is established to construct the accurate dense point cloud of aeroengine part surfaces by the non-uniform rational B-splines (NURBS) method based on the coordinate measuring machine collecting information. Then, the mapping between the dense point cloud and coaxiality is established to obtain an assembly dataset by the virtual assembly. Finally, the dataset is fed to a new point cloud deep learning backbone, Self-channel cross attention point network, and realizes end-to-end coaxiality prediction based on the aeroengine surface point cloud. The geometric distribution error model is tested on the aeroengine simulated parts with 0.001 mm accuracy. The prediction method is verified on the aeroengine simulated parts and compared with other point cloud deep learning baselines. The method proposed in this paper realizes 93.17% prediction accuracy with 0.01 mm coaxiality precision which is a high performance and meets the requirements of industrial measurement. This paper provides an effective coaxiality prediction model for the aeroengine casing system, to improve the accuracy and efficiency of the aeroengine assembly. 

Coaxiality prediction for aeroengines precision assembly based on geometric distribution error model and point cloud deep learning

The prediction models of assembly processes for critical parts will enable ensuring adaptive control of assembly based on the measured information. Direct simulation of the mating process with employing computational mating models and finite element models of assemblies requires considerable computational resources and is frequently accompanied by the solution convergence problem. To solve the above problems, neural network models may be employed, which describe basic regularities for the mating process based on the cumulative results. The work describes the technique for predicting the mating precision of parts based on real geometric models of surfaces. Real models of parts represents dot arrays of their surfaces. The technique employs the developed model, allowing to calculate the assembly geometric parameters of parts. The results of mating simulation for the disk and the spacer of the turbine rotor are considered. To predict the parameter of “radial run-out” depending on the value and nature of form deviation and tension of mating surfaces, a radial-base neural network was developed and trained.

Predicting geometric parameters of assemblies with neural network models

The manufacturing errors of the turbine rotor lead to the occurrence of vibrations that limit the acceptable modes of operation of aircraft engines. In order to reduce such vibrations, the current manufacturing technology of the rotor contains a complex procedure for balancing it. Creating a digital twin of the turbine rotor will allow to abandon the balancing procedure and reduce the cost of manufacturing parts. This paper presents such stages of creating a digital twin as the determination of typical errors in the manufacture of a turbine rotor, the construction of a digital twin of a part taking into account the errors in manufacture and the determination of its key characteristics. Analysis of the properties of the twin allows to optimize its parameters and to reduce vibration of the entire engine.

https://iopscience.iop.org/article/10.1088/1742-6596/1368/5/052013/pdf

Information model and software architecture for the implementation of the digital twin of the turbine rotor

A model is proposed for determining the geometric parameters in the mating of parts with conical surfaces that include shape errors. Theoretical and experimental results on the mating of such parts are presented.

Mating of components with conical surfaces

The mating of components at plane–cylinder surfaces is studied, by simulation. The influence of the shape and positional precision of the coupled surfaces on the relative position of the components is analyzed.

Coupling of components at plane–cylinder surfaces

The article presents an analysis of factors affecting the errors in bolt joint tightening. For the purpose of desk studies, the dependencies between the resulting axial force and applied torque were used with the application of the developed finite element model. Strain stress behavior of bolt joint was evaluated using the finite element model designed in the ANSYS system. Experimental studies of the identified factors effect on the value of axial force during bolt joint tightening are presented. Experimental studies involved checking of existing methods for bolt joint tightening control. For the purpose of experimental procedure, a developed test bench, a torque wrench and a bolt with strain gauges were used.

Analysis of Errors in Bolt Joint Tightening

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