The paper "Development of Weighted Ensemble Deep Learning Network for Surface Roughness Prediction and Flank Wear Measurement" by Ahmed Alhussen et al. addresses the challenges in predicting surface roughness (SR) and flank wear (FW) in modern manufacturing industries. The authors propose a novel ensemble deep learning model that integrates one-dimensional convolution neural network (1DCNN), deep temporal convolution network (DTCN), deep belief network (DBN), and long short-term memory (LSTM). This ensemble technique is used to predict SR, and the outputs are fused using a weighted average fusion method. The model employs the opposition squid game optimizer (OSGO) to optimize the weights, enhancing the convergence performance. The performance of the model is evaluated using various metrics, showing a mean absolute error (MAE) of 1.8% and a root mean square error (RMSE) of 10.36%, outperforming existing approaches. The study aims to improve the surface quality of machine parts by providing accurate predictions of SR and FW, which are crucial for maintaining product quality and reducing costs.The paper "Development of Weighted Ensemble Deep Learning Network for Surface Roughness Prediction and Flank Wear Measurement" by Ahmed Alhussen et al. addresses the challenges in predicting surface roughness (SR) and flank wear (FW) in modern manufacturing industries. The authors propose a novel ensemble deep learning model that integrates one-dimensional convolution neural network (1DCNN), deep temporal convolution network (DTCN), deep belief network (DBN), and long short-term memory (LSTM). This ensemble technique is used to predict SR, and the outputs are fused using a weighted average fusion method. The model employs the opposition squid game optimizer (OSGO) to optimize the weights, enhancing the convergence performance. The performance of the model is evaluated using various metrics, showing a mean absolute error (MAE) of 1.8% and a root mean square error (RMSE) of 10.36%, outperforming existing approaches. The study aims to improve the surface quality of machine parts by providing accurate predictions of SR and FW, which are crucial for maintaining product quality and reducing costs.