Parametric design of automotive ball joint based on variable design methodology using knowledge and feature-based computer assisted 3D modelling

Geren, Necdet; Akal, Osman Oktay; Bayramolu, Melih

doi:10.1016/j.engappai.2017.08.011

Cited by 19 publications

(31 citation statements)

References 20 publications

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“…The initial positions of such steel hemispheres against the rigid spherical surface were assumed to be the same as in the case of PTFE ones. It allows for a simplification and limits the amount of calculations [36].…”

Section: Tribological Modelmentioning

confidence: 99%

A Study on Wear and Friction of Passenger Vehicles Control Arm Ball Joints

et al. 2021

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The following paper describes research on vehicle suspension elements: the ball joints. The worn surface roughness of selected ball pins and their bearings was compared in terms of vehicle mileage, utilization period, and car model. Ball pin roughness was measured using a scanning tunneling microscope (STM), whereas for the bearing surface, a profilometer was used. The aim of this study was to determine the resistive torque in an unloaded ball joint. Using the finite element method, models of the unloaded ball joint were analyzed in two scenarios: with and without interference between the worn ball and its bearing. Calculated values of resistive torques in the ball joint were compared, and recommendations were given relative to the mileage and the time after which it was necessary to perform verification or replacement of the ball joints.

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Section: Tribological Modelmentioning

confidence: 99%

A Study on Wear and Friction of Passenger Vehicles Control Arm Ball Joints

et al. 2021

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“…Modern 3D CAD software supports parametric design as well as the ability to prepare and input parameters from the other software [12,23]. In this paper, Autodesk Inventor CAD software integration with MS Excel has been implemented.…”

Section: Parametric Design Of Partsmentioning

confidence: 99%

Product Platform for Automatic Configuration of Modular Strongrooms

et al. 2020

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Modular strongrooms (MSR) consist of industrially made elements, which are assembled at the place of use. They are designed on individual customer's requests. The customer chooses the resistance grade according to EN 1143-1 standard and provides dimensions of the available space for requested product to be fit in. Configuration of MSR is realized by combining standard modules which are, for the selected resistance grade, repeated in each new project, and differ only by their number and dimensions. This paper presents product platform developed for automatic MSR configuration, based on parametric and variable design, which provides requested configuration of the product, eliminates possible errors in design process, provides requested product quality level, decreases design and manufacturing costs, shortens design time and provides faster response to customer's requests. The proposed concept is tested and confirmed on numerous examples of realized projects.

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“…These works have facilitated the modelling work to some extent, but there is a certain distance away from automated modelling. Geren et al (2017) presented a parametric modelling of assembly based on knowledge and features, which is effective for certain situations but not for general assembly. Anantha et al (1996) proposed an inferring method based on geometrical constraints compensation.…”

Section: Literature Reviewmentioning

confidence: 99%

3D modelling of a frame assembly using deep learning and the Chu–Liu–Edmonds Algorithm

Cao

Mo²,

Wan

2019

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Purpose The proposed method is to generate the 3 D model of frame assemblies based on their topological model automatedly. It was a very demanding task and there was no appropriate automated method to facilitate this work. Design/methodology/approach The proposed method includes two stages. The first stage is decisive. In this stage, a deep learning network and the Chu–Liu–Edmonds algorithm are used to recognize contact relations among parts. Based on this recognition, the authors perform a geometrical computation in the second stage to finalize the 3 D model. Findings The authors verify the feasibility of the proposed method using a case study and find that the classification rate of the deep learning network for part contact relations is higher than 75 per cent. Furthermore, more accurate results could be achieved with modification by the Chu–Liu–Edmonds algorithm. The proposed method has lower computational complexity compared with traditional heuristic methods, and its results are more consistent with existing designs. Research limitations/implications The paper introduces machine learning method into assembly modelling issue. The proposed method divides the assembly modelling into two steps and solves the assemble relation creatively. Practical implications Frame assemblies are fundamental to many areas. The proposed method could automate frame assembly modelling in a viable way. It could benefit design and manufacture process significantly. Originality/value The proposed method expands the application of machine learning into a new field. It would be more useful than simple machine learning in industry. The proposed method is better than general heuristic algorithms. It outputs identical results when the inputs are the same. Meanwhile, the algorithmic complexity in worst situation is better than general heuristic algorithms.

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Parametric design of automotive ball joint based on variable design methodology using knowledge and feature-based computer assisted 3D modelling

Cited by 19 publications

References 20 publications

A Study on Wear and Friction of Passenger Vehicles Control Arm Ball Joints

A Study on Wear and Friction of Passenger Vehicles Control Arm Ball Joints

Product Platform for Automatic Configuration of Modular Strongrooms

3D modelling of a frame assembly using deep learning and the Chu–Liu–Edmonds Algorithm

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