Monitoring and controlling 3D printing process

监控和控制 3D 打印过程

• 批准号: 580247-2022
• 负责人: Tu, YiliuYL
• 金额: $ 3.64万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759750
• 关键词: Monitoring; controlling; 3D; printing; process

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.

不确定的制造工艺，如3D打印，增材制造（AM）工艺和微/纳米加工工艺，对随机变量敏感，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/疲劳和错误。很难使用不确定的制造过程来有效和经济地批量生产产品。此外，由于控制模型随随机变量不断变化，传统的确定性闭环控制模型对不确定过程的控制是无效的。为了解决这个问题，该项目将采用Kriging模型（受置信区间影响的非确定性模型）来控制3D打印机。为了检测错误，3D打印过程的数字孪生将与通过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处理参数）进行比较。在误差方面，贝叶斯模型可以统计学习随机变量并更新克里格模型的置信区间。因此，控制器的参数修改和自适应调整的随机变量迭代。该项目的成功可能为3D打印公司提供新的云监控和控制服务，并导致新一代AM技术，该技术能够智能地适应随机环境，材料和机器的影响或干扰，以保持过程稳定。显然，这种新一代AM技术可以广泛应用于各种业务，以经济地批量生产定制产品。这将使加拿大和阿尔伯塔成为具有经济竞争力的增材制造的领导者，这不仅为制造公司，而且为其他企业创造了巨大的经济潜力。为了方便实施这项新技术，我们还将设计一种软传感器技术，通过应用图像处理和机器学习技术，使用相机测量质量数据和过程故障，例如，长丝供应损失和喷嘴堵塞。