Simultaneous fixturing layout and sequence optimization based on genetic algorithm and finite element method

Hajimiri, Hossein; Abedini, Vahid; Shakeri, Mohsen; Siahmargoei, Mohammad Hassan

doi:10.1007/s00170-018-1706-6

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…The effect of the clamping forces on the working object has been studied, and the minimum clamping forces are identified [26]. In another study, the GA optimization algorithm is considered for optimizing the fixture layout and the clamping sequence [27] on a simple geometry workpiece. Further studies have shown that relying on evolutionary algorithms for joining sequence optimization of non-rigid assemblies is computationally expensive, as the objective function involves the Finite Element Method (FEM) and MC simulations [19].…”

Section: Influence Of Clamping Process In Quality Assurancementioning

confidence: 99%

Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality

Sadeghi Tabar,

Zheng,

Litwa

et al. 2024

Applied Sciences

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Geometric deviation associated with the assembly of sheet metal is a general concern for manufacturers. The typical assembly step involves a sequence of events that exert forces on the parts to enforce them to the nominal condition and to connect the parts together. The simulation and optimization of the assembly steps often neglect the sequence of operations due to the problem and computation complexity. This paper investigates the influence of the clamping sequence in the body-in-white (BIW) manufacturing process on the geometrical quality of the assembly. An approach for modeling clamping sequences for non-rigid variation simulation is introduced in a digital twin context, taking the part deviation into consideration. An optimization method is proposed to achieve minimum geometric deviation after clamping the parts and welding them together. The method is successfully applied on two reference assemblies, and the results show that the sequence of clamping can impact the total geometric deviation up to 31%. Combining clamping and welding sequence optimization can enhance the quality improvement to 77% after releasing the assembly from the fixture and springback.

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Section: Influence Of Clamping Process In Quality Assurancementioning

confidence: 99%

Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality

Sadeghi Tabar,

Zheng,

Litwa

et al. 2024

Applied Sciences

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“…In general, the number of available discrete values for each variable are different based on the reconfigurability of the fixture. If variables are integer, it varies between the lower limit ðx i L Þ and the upper limit ðx i U Þ as mentioned in equation (6). Equations ( 7) and ( 8) represent the mathematical formulation of integer variables.…”

Section: Discrete Genetic Algorithm Based Optimizationmentioning

confidence: 99%

“…Rex and Ravindran 5 implemented an integrated approach for optimal fixture layout design using FEM and the design of experiments. Hajimiri et al 6 applied the GA-FEM approach for optimizing fixture layout and fixturing sequence simultaneously. ABAQUS finite element solver was used to determine the deformation of the workpiece.…”

Section: Introductionmentioning

confidence: 99%

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Optimization of flexible fixture layout to improve form quality using parametric finite element model and mixed discrete-integer genetic algorithm

Rex¹,

Hariharasakthisudhan²,

Andrews³

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Dimensional and form accuracy of the workpiece can be improved by effective fixture layout design which shows minimum deformation of the workpiece during machining. Flexible fixtures are inevitable in industries owing to high product variety and shortened production time. Hence, an integrated approach is presented to select the optimum position of locating and clamping elements in a flexible fixture that provide good form accuracy. In this approach, a Parametric Finite Element Model (PFEM) is developed using the information about the workpiece, fixture plan and machining condition. PFEM is used to predict the elastic deformation of the workpiece for the fixture layouts generated using a discrete Genetic Algorithm (GA) with mixed integer-discrete variables. The objective is to minimize the maximum deformation of the workpiece by optimizing fixture layouts. The stability of the workpiece and fixture system is ensured by implementing non-negative reaction force constraints in GA. The proposed approach is applied for a prismatic workpiece to carry out pocket milling operation. The significance of this work is to express the flexibility and computational effectiveness of PFEM to accommodate variation in the workpiece, machining condition and fixture plan while designing flexible fixtures. Further, it highlights a significant reduction in search space due to the use of discrete GA and stability constraint as it takes less objective function calculations. An experimental analysis is performed to study the effectiveness of the proposed approach. Therefore, the proposed approach provides a viable solution to the optimization problem in flexible fixtures.

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“…Raghu [25] determined the optimum clamping sequence using the strain energy of workpiece positioning error as an evaluation criterion. Hossein [26] proposed the "clamping gap" concept to evaluate the cumulative error of workpiece deformation for different clamping sequences in a stable clamping domain and optimized the clamping position and sequence simultaneously through finite element and genetic algorithms. Jiao [27] et al…”

Section: Introductionmentioning

confidence: 99%

Support-absorption composite clamping static response prediction and clamping deformation suppression

Fang,

Liu,

Zhao

et al. 2024

Preprint

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Thin-walled spherical shells are weakly rigid and prone to clamping deformation under clamping force, which will affect machining accuracy. In this paper, the support-absorption composite clamping method is proposed and the in-situ conformal clamping strategy is obtained through the deformation coordination optimization. Firstly, the thin-walled spherical shell static response model is established, and the displacement analytic solution of equivalent constraint superposition is proposed by decoupling load and boundary constraint and Reisner's force-displacement hybrid method. Then, the vacuum generation of the Laval nozzle and pressure regulation mechanism is elucidated, and the matching mechanism of vacuum degree and spring support is revealed. Considering the support and adsorption deformation coordination optimization, the in-situ conformal clamping model is constructed. Next, the simulation explores the response of thin-walled spherical shells at different positions and adsorption effects on different wall thicknesses. The applied velocity ratio of the load in in-situ conformal clamping is analyzed. Finally, comparative experiments with different clamping methods are carried out and the results show that: Larger deformation in the top ± 10° range. The average prediction error of the theoretical model is 11.97%. With a larger load, the larger deformation, and recommended to control within 0.5Mpa. A larger number of partitions isn't recommended, nonlinearities could cause larger acceleration mutations. The thinner the thickness or the smaller the support load, the support-adsorption composite clamping effect is more obvious, the maximum can reduce the clamping deformation by 64.3%. In-situ conformal clamping method can reduce the deformation of the clamping process by 33.3%.

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Simultaneous fixturing layout and sequence optimization based on genetic algorithm and finite element method

Cited by 11 publications

References 18 publications

Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality

Digital Twin-Based Clamping Sequence Analysis and Optimization for Improved Geometric Quality

Optimization of flexible fixture layout to improve form quality using parametric finite element model and mixed discrete-integer genetic algorithm

Support-absorption composite clamping static response prediction and clamping deformation suppression

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