Parametric analysis to quantify process input influence on the printed densities of binder jetted alumina ceramics
Parametric analysis to quantify process input influence on the printed densities of binder jetted alumina ceramics
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DOI：
10.1016/j.addma.2019.100864
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发表时间：
2019-12-01
影响因子：
11
通讯作者：
Beuth, Jack
中科院分区：
文献类型：
--
作者：
Jimenez, Edgar Mendoza;Ding, Daming;Beuth, Jack
粘结剂喷射是一种选择性地将液体粘结剂沉积到粉末床上的商业增材制造工艺，可以成为增材制造陶瓷的可行方法。然而，尚未研究工艺参数/输入对打印输出（例如，部件密度和几何分辨率）的影响，也没有用于新材料工艺开发的系统方法。在这项工作中，一个参数的研究，包括18个实验与独特的工艺输入组合，探讨了7个工艺输入的相对密度的印刷（绿色）氧化铝（Al 2 O3）零件的影响。敏感性分析比较了每个输入对绿色密度的影响。多变量线性和高斯过程回归提供了用于预测作为粘结剂喷射过程输入的函数的绿色密度的模型。参数研究表明，两个过程的输入，即重涂速度和振荡器速度，显着影响绿色密度。多元线性和高斯过程回归模型表明，降低复涂速度和提高振荡器速度可以提高氧化铝坯体的绿色密度。高斯过程回归模型进一步表明，绿色密度具有非线性依赖于其余的工艺参数。在不同于参数研究的工艺输入组合下进行单独打印，以验证绿色密度模型。所产生的模型可以帮助操作员选择过程输入，这将导致所需的绿色密度，允许以高精度控制打印部件中的孔隙率。本研究中报告的方法可用于其他粉末系统和机器，以预测和控制用于过滤器、轴承、电子器件和医疗植入物等应用的粘合剂喷射部件的孔隙率。
Binder jetting, a commercial additive manufacturing process that selectively deposits a liquid binder onto a powder bed, can become a viable method to additively manufacture ceramics. However, the effects of process parameters/inputs on printing outputs (e.g. part density and geometric resolution) have not been investigated and no methodical approach exists for the process development of new materials. In this work, a parametric study consisting of 18 experiments with unique process input combinations explores the influence of seven process inputs on the relative densities of as-printed (green) alumina (Al2O3) parts. Sensitivity analyses compare the influence of each input on green densities. Multivariable linear and Gaussian process regressions provide models for predicting green densities as a function of binder jetting process inputs. The parametric study reveals that two process inputs, namely recoat speed and oscillator speed, significantly influence green densities. The multivariable linear and Gaussian process regression models indicate that the green densities of alumina builds can be increased by decreasing the recoat speed and increasing the oscillator speed. The Gaussian process regression model further suggests that the green densities have nonlinear dependence on the rest of the process parameters. Separate prints were performed at process input combinations different than those of the parametric study to validate the green density models. The models produced can assist operators in selecting process inputs that will result in a desired green density, allowing for the control of porosity in printed parts with a high degree of accuracy. The methodology reported in this study can be leveraged for other powder systems and machines to predict and control the porosity of binder jetted parts for applications such as filters, bearings, electronics, and medical implants.