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Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

The present study investigates the impact of managerial psychological characteristics on the supply chain management efficiency (SCME) of companies listed in Tehran Stock Exchange.

To this aim, information about 215 companies was analyzed during 2014–2021. The sales per inventory ratio was used to calculate SCME. In the present study, the focus is on characteristics such as managerial entrenchment, managerial myopia, managerial overconfidence (MOC) and managerial narcissism, all considered as managerial attributes.

The present findings showed that managerial myopia/managerial entrenchment (MOC/managerial narcissism) have a negative (positive) effect on SCME. Hypothesis testing based on robustness checks confirmed these results. Moreover, the findings are presented separately for companies with high business strategy (first quarter) and low business strategy (third quarter). The results show that at low levels of differentiation strategy, managerial entrenchment does not have a significant effect on SCME while other managerial attributes have a significant effect on both high and low business strategy.

The present study contributes to the identification of managerial psychological characteristics influencing SCME to advance future studies and support practical efforts. The present findings can prove the significance of this research and fill the existing gap in research.