Despite the increasing digitalization of manufacturing processes in the context of Industry 4.0, the process design and development of machining processes poses major challenges for today's manufacturing technolog...
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Despite the increasing digitalization of manufacturing processes in the context of Industry 4.0, the process design and development of machining processes poses major challenges for today's manufacturing technology. Compared to the conventional process design, which is influenced by an empirical "trial-and-error" principle, the simulative process design offers the possibility of reducing development time and costs while at the same time improving the process understanding. A possible simulation technique to achieve these goals is the Finite Element Method (FEM). The FEM enables the calculation of the thermo-mechanical load spectrum underlying the machining process. Therefore, different input models are required. One of the most critical input models is the material model, which describes the constitutive material behavior. To determine the material model parameters, either (conventional) material tests, which require an extrapolation into the regime of metal cutting, or inverse techniques are used, where the process itself is used as a material test. Using the inverse technique, the model parameters are modified iteratively until a predefined agreement between simulations and experiments is achieved. The evaluation of the agreement bases on integral process variables, such as the cutting force, and their simulative counterparts. However, the procedure of the inverse determination requires high computational efforts and is not robust. This paper presents a novel approach to enhance the robustness of the inverse material model parameter determination from the cutting process. Orthogonal cutting tests on AISI 1045 steel have been conducted on a broaching machine tool over a range of different cutting speeds and undeformed chip thicknesses to set an experimental database. Thereby, the workpiece material was investigated in the two different heat treatments: normalized and coarse-grain annealed. The machining experiments showed differences in terms of the integral proces
Despite the increasing digitalization of manufacturing processes in the context of Industry 4.0, the process design and development of machining processes poses major challenges for today’s manufacturing technology. ...
详细信息
Despite the increasing digitalization of manufacturing processes in the context of Industry 4.0, the process design and development of machining processes poses major challenges for today’s manufacturing technology. Compared to the conventional process design, which is influenced by an empirical "trial-and-error" principle, the simulative process design offers the possibility of reducing development time and costs while at the same time improving the process understanding. A possible simulation technique to achieve these goals is the Finite Element Method (FEM). The FEM enables the calculation of the thermo-mechanical load spectrum underlying the machining process. Therefore, different input models are required. One of the most critical input models is the material model, which describes the constitutive material behavior. To determine the material model parameters, either (conventional) material tests, which require an extrapolation into the regime of metal cutting, or inverse techniques are used, where the process itself is used as a material test. Using the inverse technique, the model parameters are modified iteratively until a predefined agreement between simulations and experiments is achieved. The evaluation of the agreement bases on integral process variables, such as the cutting force, and their simulative counterparts. However, the procedure of the inverse determination requires high computational efforts and is not robust. This paper presents a novel approach to enhance the robustness of the inverse material model parameter determination from the cutting process. Orthogonal cutting tests on AISI 1045 steel have been conducted on a broaching machine tool over a range of different cutting speeds and undeformed chip thicknesses to set an experimental database. Thereby, the workpiece material was investigated in the two different heat treatments: normalized and coarse-grain annealed. The machining experiments showed differences in terms of the integral proces
We report on the development of an on-line learning algorithm for ANCFIS, a neuro-fuzzy architecture employing complex fuzzy sets. ANCFIS uses a hybrid learning rule, with rule consequent parameters determined by leas...
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ISBN:
(纸本)9781424469208
We report on the development of an on-line learning algorithm for ANCFIS, a neuro-fuzzy architecture employing complex fuzzy sets. ANCFIS uses a hybrid learning rule, with rule consequent parameters determined by least-squares estimation in the forward pass, and premise parameters determined by a combination of gradient descent and chaotic simulated annealing in the backward pass. Our on-line learning algorithm replaces these with recursive least-squares in the forward pass, and the downhill-simplex algorithm in the backward pass. Experimental results on two time-series datasets show that this technique is comparable to existing results, although slightly inferior to the off-line ANCFIS results.
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