Investigations into the Design Rules for the Control of Wire Arc Additive Manufacturing

该奖项促进了对电弧增材制造中缺陷形成的理解，从而改进了加工和更好的过程控制。这项研究解决了电弧增材制造中的表面波纹度和壁厚不均匀的挑战，使其能够为许多美国行业所接受，包括航空航天，国防和汽车，从而有利于国家的经济和福祉。 丝弧增材制造使用焊接电弧作为能源，以高产量制造三维物体。这个过程可以很容易地与现有的计算机数控路由器或机器人手臂集成。因此，通过该项目开发的知识和方法可以直接转让给对制造或修理金属零件感兴趣的中小型企业。此外，该项目开发K-12，本科生和研究生的专业技能，包括妇女和代表性不足的少数民族，通过一套独特的综合教育和多学科研究机会，在线弧增材制造和数据分析领域进行培训。因此，学生熟悉新兴的技术密集型制造和数据科学学科，从而为未来的劳动力配备这些新技能和知识做好准备。该项目的两个首要研究目标是（1）获得关于电弧增材制造中表面波纹度和有效壁厚形成机制的基础知识，以及（2）研究制造过程监测和控制的设计规则。一个综合的实验表征和理论建模框架被认为是实现这些目标。丝弧增材制造结构的表面质量通过针对不同制造条件微调和平衡表面张力、电弧力、液滴冲击、重力、浮力和摩擦来控制。该项目框架由四个部分组成：（1）实时过程特征（例如，电压和电流）和电弧、熔滴和焊池特征的可视化数据;（2）缺陷的数据测量和分析（例如，球化效应和空隙）作为工艺参数、壁厚和倾斜度以及多珠/多层沉积的函数的产生和传播;（3）基于表面张力的计算流体动力学模型的表征及其通过实验的验证和确认;以及（4）使用数据驱动的低保真度替代模型建立设计规则。机器学习算法（例如，卷积神经网络，AnoGAN，转移学习和强化学习）详细开发了缺陷检测和分类以及过程控制。该综合框架为协同了解电弧、熔滴和焊池特性以及焊丝电弧增材制造中的缺陷形成提供了独特、变革性和高效的机会。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award advances the understanding of defect formation in wire arc additive manufacturing, leading to improved processing and greater process control. This research addresses surface waviness and nonuniform wall thickness challenges in wire arc additive manufacturing, making it acceptable for many U.S. industries, including aerospace, defense, and automotive, and thereby benefiting the nation’s economy and well-being. Wire arc additive manufacturing uses a welding arc as the energy source in making three-dimensional objects with high throughput. This process can be easily integrated with existing computer-numerical-control routers, or robot arms. Thus, the knowledge and methodology developed through this project can be directly transferred to small- and medium-sized enterprises interested in making or repairing metallic parts. Additionally, this project develops the professional skills of K-12, undergraduate, and graduate students, including women and underrepresented minorities, by training them through a unique set of integrated education and multidisciplinary research opportunities in the areas of wire arc additive manufacturing and data analytics. Accordingly, students are familiarized with emerging technology-intensive manufacturing and data science disciplines, thereby preparing a future workforce equipped with these new skills and knowledge. The two overarching research goals of this project are to (1) gain fundamental knowledge about surface waviness and effective wall thickness formation mechanisms in wire arc additive manufacturing and (2) investigate the design rules for the monitoring and control of the manufacturing process. An integrated experimental-characterization and theoretical-modeling framework are considered to achieve these goals. The surface quality of wire arc additively manufactured structures is controlled by fine-tuning and balancing the surface tension, arc force, droplet impact, gravity, buoyancy, and friction for different manufacturing conditions. The project framework consists of four components: (1) measurement and analysis of real-time process signatures (e.g., voltage and current) and visualization data of the arc, droplet, and weld pool features; (2) data measurements and analysis of defect (e.g., balling effect and voids) generation and propagation as a function of process parameters, wall thickness and inclination, and multi-bead/multilayer deposition; (3) characterization of the surface tension-based computational fluid dynamics model and its verification and validation through experiments; and (4) establishment of design rules using data-driven, low-fidelity surrogate models. Machine learning algorithms (e.g., convolutional neural network, AnoGAN, transfer learning, and reinforcement learning) are developed in detail for defect detections and classifications as well as process control. This integrated framework provides unique, transformative, and efficient opportunities to synergistically understand the arc, droplet, and weld pool characteristics and defect formation in wire arc additive manufacturing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.