Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

Yang, Bo; Wang, Zhongqi; Yang, Yuan; Jing, Zewang; Kang, Young Jin

doi:10.1051/matecconf/20179507018

Cited by 2 publications

(1 citation statement)

References 15 publications

(11 reference statements)

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“…Therefore, most researchers have formulated optimization models of multipoint fixture layouts to achieve the minimum workpiece deformation and computed the deflection under different fixture layouts by the finite element method (FEM). To further solve optimization formulations, many different types of optimization algorithms have been introduced, such as the genetic algorithm [9,10], particle swarm optimization [11], the bat algorithm [12], the nondominated sorting genetic algorithm [13,14], the cuckoo search algorithm [15], and the grey prediction model [16]. All in all, extensive research has been conducted to optimize the fixture layout of thin shell parts by using probability optimization algorithms coupled with FEM.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Driven Reconfigurable Fixturing Optimization for Trimming Operation of Aircraft Skins

2022

Aerospace

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The incomplete constraint induced by multipoint reconfigurable fixturing and the inherently weak rigidity of thin shell parts significantly hinder the stability of flexible fixturing systems. In particular, during the trimming operation, the number of effective locators may change with the progressive separation of the desired shape from that of the blank part, which easily produces the cliff effect (instantaneous dramatic reduction) of the system stiffness. As a result, the location layout becomes a main crux in reality. Regarding this issue, the author herein presents a digital twin-based decision-making methodology to generate reconfigurable fixturing schemes through integrating virtual and physical information. Considering the intrinsic features of the trimming process, such as the time-varying propagation of the system stiffness and the coupling effects of multiattribute process parameters, the hidden Markov model was introduced to cope with reconfigurable fixturing optimization. To achieve fast convergence and seek a feasible solution, local information (where low system rigidity occurs) was extracted and shared to guide the optimization process in a front-running simulation. To demonstrate the presented method, trimming experiments were performed on a large-size compliant workpiece held by a reconfigurable fixturing system that was developed independently by our research group. The experimental results indicate that the proposed method could adaptively iterate out the optimal locating schema and process control reference from the virtual fixturing and trimming simulation to guarantee the time-varying stability of the trimming process in the real world. Clearly, the digital twin-based reconfigurable fixturing planning approach generated a high possibility of building a context-specific, closed-loop decision-making paradigm and allowing the reconfigurable fixturing system to behave in a more adaptable and flexible manner.

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Section: Introductionmentioning

confidence: 99%

Digital Twin-Driven Reconfigurable Fixturing Optimization for Trimming Operation of Aircraft Skins

2022

Aerospace

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A novel particle swarm optimization-based grey model for the prediction of warehouse performance

Islam

Ali

Fathollahi-Fard

et al. 2021

Journal of Computational Design and Engineering

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Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

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Determination of the Number of Fixture Locating Points for Sheet Metal By Grey Model

Cited by 2 publications

References 15 publications

Digital Twin-Driven Reconfigurable Fixturing Optimization for Trimming Operation of Aircraft Skins

Digital Twin-Driven Reconfigurable Fixturing Optimization for Trimming Operation of Aircraft Skins

A novel particle swarm optimization-based grey model for the prediction of warehouse performance

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