In this paper a new method called SCLA which stands for Spiking based Cellular Learning Automata is proposed for a mobile robot to get to the target from any random initial point. The proposed method is a result of the integration of both cellular automata and spiking neural networks. The environment consists of multiple squares of the same size and the robot only observes the neighboring squares of its current square. It should be stated that the robot only moves either up and down or right and left. The environment returns feedback to the learning automata to optimize its decision making in the next steps resulting in cellular automata training. Simultaneously a spiking neural network is trained to implement long term improvements and reductions on the paths. The results show that the integration of both cellular automata and spiking neural network ends up in reinforcing the proper paths and training time reduction at the same time.

This research aims develop an Explainable Artificial Intelligence (XAI) framework to facilitate human-understandable solutions for tool wear prediction during turning. A random forest algorithm was used as the supervised Machine Learning (ML) classifier for training and binary classification using acceleration, acoustics, temperature, and spindle speed during the orthogonal tube turning process as input features. The ML classifier was used to predict the condition of the tool after the cutting process, which was determined in a binary class form indicating if the cutting tool was available or failed. After the training process, the Shapley criterion was used to explain the predictions of the trained ML classifier. Specifically, the significance of each input feature in the decision-making and classification was identified to explain the reasoning of the ML classifier predictions. After implementing the Shapley criterion on all testing datasets, the tool temperature was identified as the most significant feature in determining the classification of available versus failed cutting tools. Hence, this research demonstrates capability of XAI to provide machining operators the ability to diagnose and understand complex ML classifiers in prediction of tool wear.