Theoretical background of using AI in process preparation and evaluation

Learning outcomes

1. The learner compares and contrasts different AI approaches and selects the most suitable approach for machining process planning and evaluation. They justify their selection based on the specific requirements and characteristics of the machining domain. Moreover, they provide a comprehensive explanation of the chosen approach, considering its benefits and limitations after completing this learning path.
2. The learner demonstrates proficiency in each stage of AI model creation for machining. They accurately describe and apply the step-by-step procedure, including data acquisition, signal processing, feature extraction and selection, and data structure design. They select and utilize appropriate methods and techniques at each stage to create an effective AI model for machining process planning and evaluation after completing this learning path.
3. The learner explains the fundamental functionality of the AI model used in machining process planning and evaluation. They interpret the results generated by the AI model while considering its limitations and potential biases. They articulate the implications and practical significance of the AI model's outputs and provide insights into its usage in the machining domain after completing this learning path.