A review on wire arc additive manufacturing: Monitoring, control and a framework of automated system

Additive manufacturing (AM) is poised to bring a revolution due to its unique production paradigm. It offers the prospect of mass customization, flexible production, on-demand and decentralized manufacturing. However, a number of challenges stem from not only the complexity of manufacturing systems but the demand for increasingly complex and high-quality products, in terms of design principles, standardization and quality control. These challenges build up barriers to the widespread adoption of AM in the industry and the in-depth research of AM in academia. To tackle the challenges, machine learning (ML) technologies rise to play a critical role as they are able to provide effective ways to quality control, process optimization, modelling of complex systems, and energy management. Hence, this paper employs a systematic literature review method as it is a defined and methodical way of identifying, assessing, and analysing published literature. Then, a keyword co-occurrence and cluster analysis are employed for analysing relevant literature. Several aspects of AM, including Design for AM (DfAM), material analytics, in situ monitoring and defect detection, property prediction and sustainability, have been clustered and summarized to present state-of-the-art research in the scope of ML for AM. Finally, the challenges and opportunities of ML for AM are uncovered and discussed. 

Research and Application of Machine Learning for Additive Manufacturing

Wire arc additive manufacturing is currently rising as the main focus of research groups around the world. This is directly visible in the huge number of new papers published in recent years concerning a lot of different topics. This review is intended to give a proper summary of the international state of research in the area of wire arc additive manufacturing. The addressed topics in this review include but are not limited to materials (e.g., steels, aluminum, copper and titanium), the processes and methods of WAAM, process surveillance and the path planning and modeling of WAAM. The consolidation of the findings of various authors into a unified picture is a core aspect of this review. Furthermore, it intends to identify areas in which work is missing and how different topics can be synergetically combined. A critical evaluation of the presented research with a focus on commonly known mechanisms in welding research and without a focus on additive manufacturing will complete the review.

The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review

A review of WAAM for steel construction – Manufacturing, material and geometric properties, design, and future directions

In the present study, the Gas metal arc welding (GMAW) based Wire-arc additive manufacturing (WAAM) process was used to fabricate a multi-layered structure at optimized process parameters on SS316L using metal wire of SS316L. The multi-layered structure's microstructure, macrostructure, and mechanical properties (tensile test, impact test, microhardness, and fractography) were examined at three locations at the top, middle, and bottom sides of the structure. Macrostructure at different zones has confirmed an appropriate bonding between the two layers, complete fusion without oxidation, and free from defects and unwanted geometries. Microstructure results have observed a colony of columnar dendrites in the bottom zone, coarser grains with vertical growth along with the residual ferrite in the middle zone, and vertical dendritic structure with residual ferrite in skeletal shape in the top zone. Results of all tensile properties for top, middle and bottom zone developed by the WAAM process fall in the range values of wrought SS 316 L. The microhardness values were shown a consistent behavior across the built structure in all three zones. The obtained average value for the impact test has shown better strength than commercially used wrought SS 316 L. The results of fractured tensile and fracture impact test specimens revealed many dimples, which suggests a good ductility of the as-built structure. Thus, the obtained results have shown that the built structure using the GMAW-based WAAM process matches the standards for industrial applications. 

Experimental investigations on mechanical properties of multi-layered structure fabricated by GMAW-based WAAM of SS316L

Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process

Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning

Wire Arc Additive Manufacturing (WAAM) has developed rapidly in recent years and has been widely used in industry. Cold Metal Transfer (CMT), a kind of Gas Metal Arc Welding (GMAW), is widely used in the modeling of thin parts. In the monitoring of CMT process, it is necessary to monitor the weld reinforcement of the deposited layer. It can provide effective alerting for welding and help to improve welding quality. In this paper, the weld reinforcement and the position of molten pool image on the deposited layer are determined by the molten pool visual acquisition system. According to the data of the same direction additive manufacturing experiment, the future molten pool image is predicted by the improved prediction network (PredNet). Meanwhile through a regression network (SERes), the predicted results are regressed to the accurate weld reinforcement information of the deposited layer in advance. The experimental results show that the monitoring system designed in this paper can predict the change of molten pool shape 140ms in advance and the average precision of the weld reinforcement regression is better than 0.3mm. This paper provides the necessary basis for the shape online monitoring and controlling in WAAM process.

Yiming Wang

Papers

Coordinated monitoring and control method of deposited layer width and reinforcement in WAAM process

Active disturbance rejection control of layer width in wire arc additive manufacturing based on deep learning

Weld Reinforcement Analysis Based on Long-Term Prediction of Molten Pool Image in Additive Manufacturing

Multimodal-based weld reinforcement monitoring system for wire arc additive manufacturing

The 3D narrow butt weld seam detection system based on the binocular consistency correction