Using Faster R-CNN for intelligent fault diagnosis and correction in additive manufacturing

本研究建立了一个创新的基于运行时的闭环系统，该系统使用深度卷积神经网络（CNN）技术来监督和纠正增材制造（AM）中的错误，从而提高生产率和质量。目前，增材制造机器采用熔融沉积建模（FDM）来打印3D物体，并提供具有成本效益和快速的原型设计和批量生产。然而，由于无法控制打印路径偏差，操作人员经历了不可预测的打印成功率和高故障率。这是由于不准确和不稳定的打印机产生不一致和经常不可接受的打印质量的项目。本研究提出采用先进的Faster R-CNN算法技术来开发一种新的目标检测程序来解决这一挑战。该算法将使用与检测网络模块具有联合卷积特征的区域建议网络（RPN）来提供几乎无成本的区域建议。使用RPN将有助于确定图像中可能包含打印故障的有趣区域。之后，算法将继续确定区域内的故障类别，并有针对性地修改区域的大小，以检测准确的位置。最后，自动控制系统将根据Faster R-CNN预测的错误信息调整和纠正打印参数，从而使被打印的3D物体被回收。在本研究工作中，该算法的全面发展包括参数初始化、网络权值和偏置的调整以及打印状态的优化。最后，本研究成果将显著减少线材浪费，提高FDM打印机的生产率。合作伙伴组织将通过提高销售、利润、收入和向客户提供的服务质量，从这种自我诊断系统中受益。研究结果还将有助于3D打印技术扩展到更广泛的工业和商业应用中，对加拿大的经济和商业环境产生积极影响。

This research establishes an innovative runtime-based closed-loop system that uses a deep convolution neural network (CNN) technology to supervise and correct errors in additive manufacturing (AM), boosting both productivity and quality. Currently, AM machines employ fused deposition modelling (FDM) to print 3D objects and provide cost-effective and rapid prototyping and mass production. However, operators have experienced unpredictable levels of printing success and high malfunction rates because of their inability to control printing path deviations. This is due to inaccurate and unstable printers that produce items of inconsistent and often unacceptable printing quality. This research proposes to adopt advanced Faster R-CNN algorithm technology to develop a novel object detection program to address this challenge. This algorithm will use the region proposal network (RPN), which has joint convolutional features with the detection network module, to provide nearly costless region proposals. The use of the RPN will help to determine the interesting areas in the image that may contain printing malfunctions. After that, the algorithm will continue to determine the failure category in the area and specifically modify the size of areas to detect the accurate location. Finally, the automatic control system will adjust and correct printing parameters based on the error information the Faster R-CNN predicted, allowing for the 3D objects being printed to be salvaged. The full development of this algorithm in this research work includes parameters initialization, adjusting the weights and bias of the network and optimizations of printing status. In the end, the outcomes of this research will significantly reduce filament waste and improve FDM printer productivity. The partner organization will benefit from this self-diagnosed system through improved sales, profits, revenue, and the service quality provided to its clients. The results will also help 3D printing technology expand into a wider range of industrial and commercial applications, generating a positive influence on the Canadian economy and business environment.