Fixture layout optimization for multi point respot welding of sheet metals

Ahmad, Zeshan; Sultan, Tipu; Asad, Muhammad; Zoppi, Matteo; Molfino, Rezia

doi:10.1007/s12206-018-0331-5

Cited by 14 publications

(3 citation statements)

References 21 publications

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“…The current paper belongs to the third mentioned category. [11] Planning algorithm, clamping force evaluation 2 Mild Strong Parvaz and Nategh [12] 3 locating rules + 2 validation conditions 3 Strong Strong Mihaylov [13] If-else logic on CAD 1 Weak Strong Manafi and Nategh [14] Machining rules in setup planning 2 Mild Mild Optimization Sundararaman et al [15] GA + RSM 1 Weak Strong Ahmad et al [16] GA + static forces analysis 4 Weak Strong Chen et al [17] GA + static forces analysis 4 Weak Strong Ma et al [18] GA + radial-basis function 2 Weak Strong Rex et al [19] GA + RSM vs ANN comparison 1 Weak Strong Li et al [20] Whale optimization + strain energy analysis 2 Weak Strong Nambiar et al [21] Fixture position optimization 1 gives an overview of publications on the topic and the different approaches taken historically to formulating the fixture layout problem. The publications are classified attending to their category, the main developed tool framework, and the challenges mentioned.…”

Section: Previous Work On Fixture Layoutmentioning

confidence: 99%

Application of optimized convolutional neural network to fixture layout in automotive parts

Toro

Wiberg

Tarkian

2023

Int J Adv Manuf Technol

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Fixture layout is a complex task that significantly impacts manufacturing costs and requires the expertise of well-trained engineers. While most research approaches to automating the fixture layout process use optimization or rule-based frameworks, this paper presents a novel approach using supervised learning. The proposed framework replicates the 3-2-1 locating principle to layout fixtures for sheet metal designs. This principle ensures the correct fixing of an object by restricting its degrees of freedom. One main novelty of the proposed framework is the use of topographic maps generated from sheet metal design data as input for a convolutional neural network (CNN). These maps are created by projecting the geometry onto a plane and converting the Z coordinate into gray-scale pixel values. The framework is also novel in its ability to reuse knowledge about fixturing to lay out new workpieces and in its integration with a CAD environment as an add-in. The results of the hyperparameter-tuned CNN for regression show high accuracy and fast convergence, demonstrating the usability of the model for industrial applications. The framework was first tested using automotive b-pillar designs and was found to have high accuracy (≈ 100%) in classifying these designs. The proposed framework offers a promising approach for automating the complex task of fixture layout in sheet metal design.

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Section: Previous Work On Fixture Layoutmentioning

confidence: 99%

Application of optimized convolutional neural network to fixture layout in automotive parts

Toro

Wiberg

Tarkian

2023

Int J Adv Manuf Technol

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“…Therefore, an efficient fixture design is to use multiple locating elements on the primary datum to form a positioning lattice consistent with the contour of the positioning surface. Based on this theory, N -2-1, 9 N -2-1-1, 10 N -3-2-1, 11 and N - M 12 locating principles have been proposed successively. These methods can meet the positioning requirements of different thin-walled parts, but they are deficient in some aspects.…”

Section: Introductionmentioning

confidence: 99%

Fixture layout optimization for car dashboard based on N-X locating principle

Liu

Sun

2022

Proceedings of the Institution of Mechanical Engineers, Part B:

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A fixture is a crucial component in the car dashboard manufacturing system and is used to position and restrain the car dashboard under external forces. However, the conventional fixture is only suitable for a specific type of car dashboard, which has poor adaptability. To this end, a new flexible fixture based on the N- X locating principle is proposed in this paper to adapt to different types of car dashboards. In the presented fixture, N clamping elements clamp the dashboard edge, while X movable supporting elements support the area close to the tool along the machining path. Furthermore, the fixture layout is optimized by combining a genetic algorithm (GA) and finite element analysis (FEA). In the optimization program, a multi-objective optimization function is established to suppress the deformation of the car dashboard. FEA is applied to calculate the machining deformation. The optimal locations of the fixture elements are determined by GA. A case is implemented to validate the proposed method and the results show that the maximum deformation and average deformation are reduced by 53.8% and 40%, respectively, compared with the empirical method.

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“…Its supporting motion only has vertical upward degrees of freedom. However, its limitation lies in that it can only support the open surface with small curvature change and low crimp degree [3]. In order to improve the control precision of the flexible fixture, the current scholars have made a lot of efforts, and there are mainly two methods to improve the control precision.…”

Section: Introductionmentioning

confidence: 99%

Accurate Clamping Method of Multipoint Flexible Fixture for Large Complex Surface

Mao

Zhang

et al. 2021

Mathematical Problems in Engineering

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In the field of aviation and astronautics, large complex surface parts such as aircraft cabin cover are complex, varying in model, and produced in small batch. In order to reduce fixture cost and improve production efficiency, a variable multipoint and multi-DOF supporting fixture is designed. The coordinate system of the complex structure fixture is defined, and the kinematics of the multibody structures driven by air cylinders are modeled according to the topological principle. With the help of this data structure, fast and optimal search for clamping state can be realized. With the dichotomy method, the driving amount of the electric cylinder of the suction cups and the two rotation angles of two rotation axes at the end of the linkages are solved accurately. Based on the digital twin simulation method, the precise clamping motion of flexible fixture is calculated for an aircraft cockpit cover with a software developed by the authors in C++ language on the Visual studio platform. The distance between the clamping point and the surface was verified with a laser tracker. Finally, the practical experiment of a real cockpit cover clamping proves the practicability and effectiveness of the proposed method.

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Fixture layout optimization for multi point respot welding of sheet metals

Cited by 14 publications

References 21 publications

Application of optimized convolutional neural network to fixture layout in automotive parts

Application of optimized convolutional neural network to fixture layout in automotive parts

Fixture layout optimization for car dashboard based on N-X locating principle

Accurate Clamping Method of Multipoint Flexible Fixture for Large Complex Surface

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