Application of Artificial Intelligence in Predicting Machining Surface Quality

Abstract

Achieving high-quality machining surface finishes is crucial in numerous manufacturing industries, as it directly impacts the performance and reliability of machined components. Traditionally, predicting machining surface quality involves extensive trial-and-error experiments, which are time-consuming, costly, and often impractical for complex machining processes. In recent years, the application of artificial intelligence (AI) techniques, particularly machine learning algorithms, has emerged as a promising approach for predicting machining surface quality accurately and efficiently. This abstract provides an overview of the application of AI in predicting machining surface quality, highlighting its benefits, challenges, and future prospects. The adoption of AI in predicting machining surface quality involves various stages. Firstly, a comprehensive dataset is collected, comprising machining parameters, tooling characteristics, and corresponding surface quality measurements. Next, pre-processing techniques are applied to clean and normalize the dataset, ensuring its suitability for training AI models. Subsequently, machine learning algorithms, such as support vector machines, neural networks, and random forests, are trained using the pre-processed data to develop predictive models. These models can capture complex relationships between machining parameters and surface quality, enabling accurate predictions.

The performance of the trained models is assessed using appropriate evaluation metrics, such as root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R-squared). Comparative analyses are conducted to identify the most effective AI model for predicting machining surface quality. Additionally, sensitivity analyses and feature selection techniques can be applied to identify the critical machining parameters that significantly impact surface quality. The application of AI in predicting machining surface quality offers several practical implications. It enables manufacturers to optimize machining processes, reduce scrap rates, and enhance product quality. By accurately predicting surface quality, manufacturers can make informed decisions regarding machining parameters, tooling selection, and process optimization, resulting in improved efficiency and cost-effectiveness. Furthermore, AI-based prediction models can be integrated into real-time monitoring systems, enabling continuous quality control and immediate adjustments to machining processes.

Despite the benefits, challenges exist in the application of AI for predicting machining surface quality. These include the availability and quality of training data, selection of appropriate features, and the interpretability of AI models. Overcoming these challenges requires continuous research and development efforts, such as the collection of large and diverse datasets, advanced feature engineering techniques, and the exploration of explainable AI methodologies.