Monitoring and controlling 3D printing process

不确定的制造过程，如3D打印、添加制造(AM)过程和微/纳米加工过程，对随机变量非常敏感，例如环境因素(温度、湿度、PM2.5颗粒、振动)、材料密度分布、电源稳定性以及机器退化/疲劳和误差。使用不确定的制造过程来有效且经济地批量生产产品是困难的。此外，由于控制模型随着随机变量的变化而不断变化，用传统的确定性闭环控制模型来控制不确定过程是无效的。为了解决这个问题，这个项目将使用克里金模型(受制于可信区间的非确定性模型)来控制3D打印机。为了检测错误，3D打印过程的数字双胞胎将与使用图像处理和机器学习技术从相机收集的在线测量质量数据(打印层几何形状和表面抛光参数)进行比较。在误差方面，贝叶斯模型可以对随机变量进行统计学习，并更新克立格模型的可信区间。因此，控制器参数被迭代地修改并自适应地调整到随机变量。该项目的成功可能为3D打印公司提供新颖的云监控服务，并导致新一代AM技术的出现，该技术能够智能地适应随机的环境、材料和机器的冲击或扰动，以保持流程的稳定。显然，这一新一代AM技术可以广泛应用于各种企业，以经济的方式批量生产定制产品。这将使加拿大和艾伯塔省在经济上具有竞争力的添加剂制造方面处于领先地位，这不仅为制造公司创造了巨大的经济潜力，也为其他企业创造了巨大的经济潜力。为了方便地实施这项新技术，我们还将通过应用图像处理和机器学习技术来设计一种软测量技术，使用相机来测量质量数据和工艺故障，例如灯丝供应损失和喷嘴堵塞。

Uncertain manufacturing processes, like 3D printing, additive manufacturing (AM) processes and micro/nano-machining processes, are sensitive to random variables, such as environmental factors (temperature, humidity, PM2.5 particles, vibrations), material density distribution, power supply stability, and machine degeneration/fatigue and errors. It is difficult to use an uncertain manufacturing process to effectively and economically mass produce products. Furthermore, it is ineffective to use a traditional deterministic closed loop control model to control an uncertain process since the control model keeps changing with the random variables. To solve the problem, this project will employ a Kriging model (a non-deterministic model subject to a confidence interval) to control a 3D printer. To detect errors, digital twins of the 3D printing process will be compared with on-line measuring quality data (printing layer geometric shape and surface finishing parameters) that has been gathered from a camera by using image processing and machine learning techniques. In terms of the errors, the Bayes model can statistically learn the random variables and update the confidence interval of the Kriging model. Consequently, the controller parameters are modified and adaptively adjusted to the random variables iteratively. The success of project may provide novel cloud monitoring and controlling services for 3D printing companies and leads to a new generation of AM technology, which is able to intelligently adapt to the random environmental, material and machine impacts or disturbances to keep the process stable. Obviously, this new generation of AM technology can be widely applied in various businesses to economically mass produce customized products. This will make Canada and Alberta leaders in economically competitive additive manufacturing, which creates great economic potentials not only for manufacturing companies but also for other businesses. To easily implement this new technology, we will also devise a soft sensor technology through applications of image processing and machine learning techniques to use a camera to measure the quality data and process faults, e.g., filament supply loss and nozzle clogging.