METHODOLOGY

The Tool Wear Setup block initiates the simulation by generating signals related to the machining process, such as tool force, speed, wear depth, or vibration input. These signals emulate real-time parameters involved in metal cutting operations. The block is configured to output these parameters in the form of a structured bus signal (OutBus) which can be consumed downstream by other blocks in the system. It forms the foundational input stage for simulating how the machining environment evolves with tool wear. 

The Sensor Module represents a virtual sensor system, particularly focusing on capturing vibration data. It receives the input bus (InBus) from the Tool Wear Setup block, processes relevant signals (e.g., wear-induced vibration or force), and may add noise to simulate real-world imperfections in sensing. This block may also include signal conditioning elements like filters or gain blocks. The processed output is packed into another bus (OutBus) to be sent to the microcontroller block for further interpretation. 

The Microcontroller block acts as the central control unit, receiving the bus output from the Sensor Module and interpreting physical parameters such as force and vibration. It processes these inputs, typically extracting meaningful control values like peak force or signal thresholds, and forwards these as individual outputs (like Force) to the FEA and vibration analysis engine. This block essentially emulates how a microcontroller or edge device would read sensor data and prepare it for deeper analysis. 

The FEA + Vibration Analysis Engine simulates the structural behavior of the tool under the effect of applied force. It receives inputs like Force from the Microcontroller and performs simplified finite element analysis and vibration response calculations. The key outputs include Stress Estimate, Severity Index, and Mode Shape Shift Tag, which are crucial indicators of tool wear and structural integrity. These are compiled into a bus output that feeds into the decision-making system. This block is central to combining mechanical simulation with signal analytics. 

The Predictive Model block is the final decision-making and monitoring unit of the system. It takes as input the comprehensive bus output from the FEA + Vibration Analysis Engine and analyzes the data using logic, thresholds, or machine learning models to determine the condition of the cutting tool. This block also includes a graphical user interface (GUI) for visual alerts, condition status, and logging mechanisms to track wear over time. It acts as the user-facing system that provides actionable insights based on sensor and simulation data.