Reducing deformation, stress, and tool wear during milling processes using simulation-based multiobjective optimization

Montalvo-Urquizo, Jonathan; Niebuhr, Carsten; Schmidt, Alfred; Villarreal‐Marroquín, María G.

doi:10.1007/s00170-018-1681-y

Cited by 11 publications

(10 citation statements)

References 21 publications

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“…The parameter ranges for P and s were taken as [20 W, 100 W] and [2 mm, 3 mm], respectively. The lower and upper limits of v are defined by v lb and v ub (see Equations (19) and 20), respectively. Thus, the range of v is different at distinct values of P and s and, as a consequence, the optimization search space is not a cube.…”

Section: Optimization Of a Laser-based Materials Accumulation Processmentioning

confidence: 99%

“…By selecting this process window, constructed around the area where experiments suggested an energy efficient process, we restrict our optimization methodology to a relatively small input domain, allowing for a higher precision. We also avoid including solutions far away from the interesting non-linear search space defined by equations (19) and (20) that might potentially mislead the construction of the metamodels.…”

Section: Optimization Of a Laser-based Materials Accumulation Processmentioning

confidence: 99%

“…Then, the values of P and s were mapped to their original ranges. After that, for each pair of P and s, the range of v was calculated using equations (19) and (20). Finally, the scaled value of v is mapped according to the corresponding range.…”

Section: Run Initial Experimental Designmentioning

confidence: 99%

“…The combination of optimization methods with computer simulations where simulations are used to transform input parameters (controllable processing variables, or CPVs) into the relevant performance measures (PMs) is an actual engineering need [19,24,27,29]. This requires evaluating optimization functionals on a large amount of candidate solutions, making it a demanding computational task when the simulations are numerically expensive [16].…”

Section: Introductionmentioning

confidence: 99%

“…To avoid running a large number of expensive simulations, we pursue a strategy to put together an optimization methodology with surrogate models (also called metamodels), similar to our implementation for milling processes in [19]. In addition to this former application, we are able to use here non-rectangular sets of admissible process parameters, which correspond to reasonable and effective experimental parameter sets.…”

Section: Introductionmentioning

confidence: 99%

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Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process

et al. 2020

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Common goals of modern production processes are precision and efficiency. Typically, they are conflicting and cannot be optimized at the same time. Multi-objective optimization methods are able to compute a set of good parameters, from which a decision maker can make a choice for practical situations. For complex processes, the use of physical experiments and/or extensive process simulations can be too costly or even unfeasible, so the use of surrogate models based on few simulations is a good alternative. In this work, we present an integrated framework to find optimal process parameters for a laser-based material accumulation process (thermal upsetting) using a combination of meta-heuristic optimization models and finite element simulations. In order to effectively simulate the coupled system of heat equation with solid-liquid phase transitions and melt flow with capillary free surface in three space dimensions for a wide range of process parameters, we introduce a new coupled numerical 3d finite element method. We use a multi-objective optimization method based on surrogate models. Thus, with only few direct simulations necessary, we are able to select Pareto sets of process parameters which can be used to optimize three or six different performance measures.

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Section: Optimization Of a Laser-based Materials Accumulation Processmentioning

confidence: 99%

Section: Optimization Of a Laser-based Materials Accumulation Processmentioning

confidence: 99%

Section: Run Initial Experimental Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process

et al. 2020

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Decreasing the cycle time for in‐mold coating (IMC) of sheet molding compound (SMC) compression molding

Bhuyan

Villarreal³

et al. 2019

Polymer Engineering & Sci

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In-mold coating (IMC) is a thermosetting liquid applied to compression molded sheet molding compound (SMC) exterior automotive or truck body panels as an environmentally friendly primer to improve surface quality and make the part conductive for subsequent electrostatic painting. The IMC is injected onto the surface of the SMC then cures and bonds to provide a smooth conductive and protective surface. In IMC as in many other reactive polymer processes, to have short cycle time while maintaining adequate flow time and pot life is required. This allows enough time to fill the mold before solidification. In this study, the effect of inhibitor (p-benzoquinone), initiator (t-butyl peroxybenzoate), and mold temperature on the flow and cure time of IMC materials has been experimentally investigated using differential scanning calorimeter. A cure model is developed based on experiments to predict inhibition and cure time. A multiple criteria optimization method was employed to identify the setting parameters of the controllable process variables that provide the best compromise (Pareto frontier [PF]) between flow and cure time. The analysis shows that simultaneous addition of initiator and inhibitor allows the molding to be performed at a higher temperature, which moves the PF toward the ideal location. Hence, minimizes the cure time and maximizes the flow time simultaneously.

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Simulation and Multi-Objective Optimization of Thermal Distortions for Milling Processes

Schmidt¹,

Niebuhr²,

Montalvo-Urquizo³

et al. 2019

Progress in Industrial Mathematics at ECMI 2018

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Reducing deformation, stress, and tool wear during milling processes using simulation-based multiobjective optimization

Cited by 11 publications

References 21 publications

Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process

Simulation and multi-objective optimization to improve the final shape and process efficiency of a laser-based material accumulation process

Decreasing the cycle time for in‐mold coating (IMC) of sheet molding compound (SMC) compression molding

Simulation and Multi-Objective Optimization of Thermal Distortions for Milling Processes

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