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Part 2: Model Evaluation Rate-Dependent Elasto-Viscoplastic Constitutive Model for Industrial Powders.

机译:第2部分:工业粉末的模型评估速率相关的弹粘塑性本构模型。

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The PSU-EVP model's constitutive parameters for alumina powder are presented.The PSU-EVP model was also used to back-predict the triaxial test data obtained for MZF and alumina powders using constitutive parameters such as the initial voids ratio (e_0),compression index (lambda),and spring-back index (kappa).In the case of MZF powder,8 out of 12 back-prediction cases had average relative difference (ARD) values below 20%.In the case of alumina powder,7 out of 11 back-prediction cases had ARD values below 20%.Based on the back-prediction results,it was concluded that the PSU-EVP model gave fairly good results for most triaxial test data collected at 0.62 MPa/minute and 6.21 MPa/minute.However,the back-prediction results obtained at 20.7MPa/minute had high ARD values.A sensitivity analysis was done to study the effect of changes in parameter values on the hydrostatic triaxial compression (HTC) and conventional triaxial compression (CTC) back-prediction results.From the sensitivity analysis,+-10% (standard deviation variation from +- 0.8sigma to +- 2.3sigma) changes in lambda and e_0 mean values had marked effect on the HTC results.However,changes in the lambda,kappa,and e_0 mean values do not produce any noticeable effect on the CTC prediction results.Overall,the PSU-EVP model can be considered to be the first step towards the development of a more robust and accurate model for prediction of stresses and strains in a dry powder compression process.
机译:介绍了PSU-EVP模型的氧化铝粉的本构参数,并使用PSU-EVP模型使用初始孔隙率(e_0),压缩指数等本构参数对MZF和氧化铝粉的三轴测试数据进行了反向预测。 (lambda)和回弹指数(kappa)。对于MZF粉末,在12个反向预测案例中,有8个的平均相对差(ARD)值低于20%。在氧化铝粉末的情况下,其中7个11个反向预测案例的ARD值低于20%。基于反向预测结果,可以得出结论,对于以0.62 MPa / min和6.21 MPa / min收集的大多数三轴测试数据,PSU-EVP模型给出了相当好的结果。然而,以20.7MPa / min的速度获得的反向预测结果具有较高的ARD值。进行敏感性分析以研究参数值变化对静水三轴压缩(HTC)和常规三轴压缩(CTC)反向预测的影响结果。从灵敏度分析是,lambda和e_0平均值的变化为+ -10%(标准偏差从+-0.8sigma到+-2.3sigma)对HTC结果有显着影响。但是,lambda,kappa和e_0平均值的变化确实总体而言,PSU-EVP模型可被视为迈向开发更强大,更准确的模型以预测干粉压缩过程中应力和应变的第一步。

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