# Quality Prediction and Parameter Optimisation of Resistance Spot Welding Using Machine Learning
## 5 Conclusions  
The relationship between the welding process parameters (WV1, WT1, CT, WV2, WT2, SRT, and SDT) and welding quality (PEND, NEND, and TSL) was established through the CGO-MLSSVR model. The experimental results show that the positive electrical and  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Prediction** \\ **Model** \\ \end{tabular} } & \multicolumn{3}{c}{**Positive Electrode Nugget**} & \multicolumn{3}{c}{**Negative Electrode Nugget**} & \multicolumn{3}{c}{**Tensile Shear Load**} \\ \cline{2-10}  & & **Diameter** & & & **Diameter** & & & **Tensile Shear Load** \\ \cline{2-10}  & **RMSE** & **MAE** & **MAPE (\%)** & **RMSE** & **MAE** & **MAPE (\%)** & **RMSE** & **MAE** & **MAPE (\%)** \\ \hline CGO–MLSSVR & 0.024 & 0.019 & 1.729 & 0.039 & 0.031 & 3.510 & 5.379 & 4.564 & 2.341 \\ MLSSVR & 0.025 & 0.021 & 1.952 & 0.058 & 0.044 & 4.864 & 7.347 & 6.154 & 3.216 \\ GRNN & 0.099 & 0.085 & 8.055 & 0.067 & 0.064 & 7.017 & 11.231 & 10.34 & 5.264 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison results for different models.  
negative electrode nugget diameters and tensile shear load RMSE of the constructed CGO-MLSSVR prediction model are 0.024, 0.039, and 5.379, respectively, which demonstrates high prediction accuracy. The proposed method solves the problems of accurate prediction and the generalisation ability of multivariable outputs in small samples. Compared to similar prediction models, the prediction performance of the CGO-MLSSVR model is comparable to others.  
In the best process parameters obtained in the copper-nickel sheet spot welding test, under Method I conditions for tensile shear load and nugget diameter, the average relative errors were 1.9% and 3%, respectively. Under the conditions of Method II for tensile shear load and nugget diameter, the average relative errors were 1.05% and 3.225%, respectively. The results show that the welded-joint nugget diameter and tensile shear load, and the best results under the best process parameters, are in good agreement.  
Conceptualisation, K.Y.; methodology, Y.H. and K.Y.; software, Y.H.; validation, K.Y.; investigation, Y.H. and K.Y.; resources, K.Y.; data curation, Y.H. and K.Y.; writing--original draft, Y.H.; writing--review and editing, Y.H., K.Y., X.W., H.H. and J.C.; supervision, X.W.; project administration, K.Y.; funding acquisition, K.Y. and H.H. All authors have read and agreed to the published version of the manuscript.  
The work is supported by National Natural Science Foundation of China(52265062); Guizhou Provincial Science and Technology Projects (Grants No. Qankehejichu [2020] 1Y232, Qankehejichu ZK [2022]-General131, Qankehe Support Normal [2021] 445 and [2021] 172); the Research Foundation for Introduced Talent, Guizhou University (Grant No. Guidarenijhezi [2019] 07); and the Growth Project for Young Scientific and Technological Talents in General Colleges and Universities in Guizhou Province (Grant No. Qianjiaohe KY Zi [2021] 096).  
Not applicable.  
The datasets analysed or generated during the study can be obtained from the corresponding author.  
The authors declare no conflict of interest.