Optimized Robotic WAAM

Background

AM (additive manufacturing) idea has evolved about 29 years ago different materials and different power sources have been used for it (Salonitis, 2016). WAAM (wire and arc additive manufacturing) has evolved few years ago. The most popular wire arc method used for WAAM is MIG welding / GMAW (gas metal arc welding). One of the main challenges facing WAAM is obtaining a product with sufficient mechanical properties to be functional, require less machining after deposition and need no heat treatment to be able to merge the technique with regular production methods.

To achieve this target it was necessary to model the MIG WAAM process and find the optimal MIG parameters. Optimization target was to be smallest homogeneous width, homogenous height and suitable hardness values to avoid cracks at all zones. For such a complicated MIMO (multi input multi output) highly nonlinear process like that NN (neural networks) is the best choice to model and understand relation between inputs and outputs. Parameters to be measured and modelled were selected depending of previous work of a lot of researchers, information provided by ASM handbook and observation of experimental specimens. Parameters and their effect on the resulting line are summarized in table.1.

A.Sumesh et al (Sumesh et al., 2017) sensed voltage and current of defected and non-defected welds to make a statistical model enabling defects avoidance in industry. Jorge Giron Cruz et al (Cruz et al., 2015) used NN to model GMAW process taking welding speed; wire feed velocity and arc voltage as inputs and width obtained through image processing system as output. Fuzzy controller is then used to control the weld quality and weld width. It was found that welding speed is the most preferred and affecting parameter to be controlled without affecting metal transfer behavior.

Donghong Ding et al (Ding et al., 2016) developed automated manufacturing system of WAAM (wire and arc additive manufacturing) to automatically design deposition path. First an NN model was developed then used MAT (medial axis transformation) algorithm. Results of this research were combined with a previous research which developed a model of multi bead overlapping. According to this research the best overlap between deposited lines to get nearly flat surface of the deposited layer is 0.738*w, where w is the single width and the distance is measured between centerlines of the sided lines. Also this research confirmed that the contour path starting from outside to inside is the best deposition pattern.

L. Nele et al (Nele et al., 2013) used neuro-fuzzy to model and adaptively control arc welding parameters to get sound weld bead. Wire feed speed, welding gap and torch speed were considered as inputs and welding current was considered as output this model was used for controlling process parameters. The same modeled to predict final weld joint characteristics like dilution ratio, hardness of weld bead, hardness of fused zone and bead width. Other models developed using multiple regressions were developed to be compared with neuro-fuzzy models. Each output variable had its own models developed by neuro-fuzzy and multiple regressions.