A new approach to predict the flexibility and precision of manufacturing systems using geometric constraints and small displacement torsors

Khan, Hana Amin; Butt, Sajid Ullah; Baqai, Aamer Ahmed; Saeed, Hasan Aftab

doi:10.1016/j.promfg.2018.10.049

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…technology [28]. And the small displacement torsor is also proposed to describe the tiny poses in space [29][30][31]. Their work has promoted the development and application of aeroengine rotor assembly technology.…”

Section: Quick Browse and Highlightmentioning

confidence: 99%

Assembly accuracy prediction and optimization of aero-engine rotor under the separation condition of assembly and measurement

Wang

Sun

et al. 2022

Int J Adv Manuf Technol

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The measurement and assembly of aeroengine rotor are separated from each other, the uncertainty of the test position leads to the test data cannot reflect the geometric characteristics of the rotor itself, which makes it difficult to accurately measure and predict the assembly accuracy. Combined with the fact that the 2 geometric characteristics of parts / components are not related to the measurement datum, an assembly accuracy test and prediction method is proposed to reduce the datum deviation and ensure the consistency of test data. Firstly, the small displacement torsor is used to describe the datum deviation, the inverse matrix transformation is applied to reduce the datum deviation, and the datum independent matrix is utilized to express the pose characteristics of the parts / components, which provides the data basis for the accurate prediction of assembly accuracy. Then, the pose transfer model based on the datum independent matrix is established, which is more comprehensive and clearer than the traditional accuracy prediction model. Furthermore, a direct optimization method is also established, which is more efficient than the traditional genetic algorithm. The assembly experiment of aeroengine rotor shows that the model and method proposed in this paper are beneficial to reduce the coaxiality of the front and rear fulcrum and they can better reflect the geometric characteristics of the rotor itself. The related research also has reference significance for other large-scale and high-precision mechanical product assembly.

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Section: Quick Browse and Highlightmentioning

confidence: 99%

Assembly accuracy prediction and optimization of aero-engine rotor under the separation condition of assembly and measurement

Wang

Sun

et al. 2022

Int J Adv Manuf Technol

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“…Secondly, distorted components can create challenges during downstream assembly processes. Misaligned components can require additional processing and can even lead to production line disruptions [11]. Finally, deviations from the intended form can negatively impact the aesthetic qualities of the final product.…”

Section: Introductionmentioning

confidence: 99%

A Method for Straightening Distorted Giga-Cast Large Thin-Walled Components

Park,

Kim

2024

Materials

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Giga-casting, a revolutionary approach for manufacturing large, single-piece car body components from aluminium, has emerged as a potential game-changer in the automotive industry. However, these large, thin-walled castings are prone to distortions during solidification and heat treatment processes. Straightening these distortions is crucial to ensure structural integrity, facilitate downstream assembly, and maintain aesthetic qualities. This paper proposes a novel method for straightening giga-cast components using a multi-pin straightening machine. The machine’s versatility stems from its ability to adapt to various geometries through multiple strategically controlled straightening pins. This paper introduces the concept of a “straightening stroke decision algorithm” to achieve precise straightening and overcome the challenges of complex shapes. This algorithm determines the stroke length for each pin, combining a polynomial model representing the global stiffness of the component with a machine learning model that captures the stiffness changes arising from the current geometry. The effectiveness of the proposed approach is evaluated through comprehensive numerical experiments using finite element analyses. The straightening performance is assessed for the straightening algorithm with different machine learning models (deep neural network and XGBoost) and compared to a traditional optimisation method. The proposed surrogate models decided the straightening strokes so that the maximum remaining distortion became 0.02% of the largest dimension of each target geometry. The results of the numerical experiment showed that the proposed straightening method is suitable for straightening distortion in large thin-walled components.

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