This study aimed to understand the effect of heat accumulation on microstructure formation on STS 316L during multilayer deposition by a laser metal deposition (LMD) process and to predict the microstructure morphology. A comprehensive experimental and numerical study was conducted to quantify the solidification parameters (temperature gradient (G) and growth rate (R)) in the LMD multilayer deposition process. During deposition, the temperature profile at a fixed point in the deposit was measured to validate the numerical model, and then the solidification parameters were quantified using the model. Simultaneously, the microstructure of the deposit was investigated to confirm the microstructure morphology. Then, a relationship between the microstructure morphology and the G/R was proposed using a solidification map. The findings of this study can guide the design of scanning paths to produce deposits with a uniform structure.

In this paper, an additional filler wire with opposite polarity was inserted in tandem flux cored arc welding process to increase the welding speed and deposition rate. In this hybrid welding, the optimisation of welding parameters is required to improve the bead geometry which directly indicates the welding quality. However, the correlation between the parameters and the bead geometry is hard to identify, so the process parameters are usually selected intuitively by the experienced engineers. Therefore, welding process modelling is constructed with the Gaussian process regression model, and parameter optimisation is performed with sequential quadratic programming optimisation algorithm. The proposed modelling optimisation process is verified by performing the welding experiment using the parameters that are optimised by the proposed process.

In this paper, the parameter optimization of the hybrid-tandem gas metal arc welding (GMAW) process was studied. The hybrid-tandem GMAW process uses an additional filler-wire with opposite polarity in contrast to the conventional tandem process. In this process, more process parameters and the relationship between the parameters causing strong nonlinearity should be considered. The analysis of variance-based Gaussian process regression (ANOVA-GPR) method was implemented to construct surrogate modeling, which can express nonlinearity including uncertainty of weld quality. Major parameters among several process parameters in this welding process can be extracted by use of this novel method. The weld quality used as a cost function in the optimization of process parameters is defined by characteristics related to penetration and bead shape, and the sequential quadratic programming (SQP) method was used to determine the optimal welding condition. This approach enabled sound weld quality at a high travel speed of 1.9 m/min, which is difficult to achieve in the hybrid-tandem GMAW process.

In this paper, the tandem flux-cored arc welding process was enhanced to improve weld productivity. Additional filler-wire of opposite polarity was used to prevent deterioration of weld quality that occurred due to arc interaction at two electrodes. Droplet transfer is one of the main factors which determine the quality of the weldment. In the tandem process, it is difficult to control droplet transfer due to arc interaction. In the hybrid welding process developed for stabilising the molten pool, the arc interaction and droplet transfers of the two electrodes in the hybrid process were investigated according to the feeding location and applied current of the filler-wire. This presented a method for improving the penetration depth and bead appearance of the weldment.

A monitoring technique for detecting changes in the root gap of butt joints during the flux-cored arc welding (FCAW) was proposed. FCAW experiments were conducted for both increasing and decreasing root gap conditions, and current and voltage were measured during the root-pass welding. The measured time series signals were used as input data for training Random Convolution Kernel Transform (ROCKET) algorithm, which consists of a feature extractor with multiple random kernels, and a linear classifier. A univariate model using current and voltage, respectively, and a multivariate model using both were compared, and the multivariate model showed the highest classification accuracy of 96.2%. Moreover, the classification errors were investigated by correlating the geometry of the root bead with the measured signals.