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Project Overview

This project involves the design and development of an advanced food processing system with multiple embedded functionalities. The system features a master-slave board architecture, where both master and slave boards use TM4C1294 microcontrollers, and the slave boards incorporate MT9M114 image sensors for image processing tasks. The system captures images at various processing stages using onboard cameras and performs real-time object detection using Time of Flight (ToF) sensors. Data is transferred between master and slave boards via a CAN mesh network, and motor control is achieved using Modbus protocol to interface with VFDs and servo drives. An HMI (Human-Machine Interface) on a touch display manages high-level operations, serves as a gateway for cloud connectivity, and facilitates machine learning and system optimization.

Technical Description

Hardware Arrangement: Master-Slave Architecture and Sensor Integration

1. Master and Slave Boards

  • Master Board (TM4C1294): The master board controls the food processing system, handling high-level commands and managing communication. It coordinates operations like image processing, motor control, and sensor input handling.
  • Slave Board (TM4C1294 with MT9M114 Image Sensor): Equipped with MT9M114 image sensors, slave boards capture images during the food processing workflow. These boards process images for food quality, shape, and size, sending data to the master board via the CAN network.

2. Time of Flight (ToF) Sensors

  • Object Detection: VL53L5CX ToF sensors with an STM32F446RE microcontroller detect objects and measure distances. These sensors trigger actions, like controlling pneumatic systems, to sort or manipulate food items based on position and size.

The ToF sensors enable real-time object detection, enhancing the system’s precision in sorting and handling food items.

3. Motor and Pneumatic Control

  • Modbus Protocol: The system uses the Modbus protocol to control motors via VFDs and servo drives, enabling precise control of motor speed and torque for cutting, sorting, and conveying tasks.
  • Pneumatic Operations: Pneumatic actuators respond to triggers from ToF sensors and the image processing system, controlled by the master board to ensure accurate handling of food items.

The motor and pneumatic control system allows for precise mechanical manipulation, adapting dynamically to different food processing stages.

4. CAN Mesh Network

  • Data Transfer: A CAN mesh network connects the master and slave boards, allowing real-time communication. Slave boards send image data to the master board, which processes it to make control decisions.

The CAN mesh network ensures efficient, real-time data flow, enabling coordinated processing and control across the system.

5. HMI (Human Machine Interface)

  • Touch Display: A touch display provides an interface for operators to control system operations, monitor performance, adjust parameters, and trigger tasks.
  • Cloud Connectivity: The HMI uploads data to the cloud for machine learning and optimization, improving efficiency, quality control, and predictive maintenance.

Design Approach

1. Hardware

  • The system uses TM4C1294 microcontrollers on both the master and slave boards. The master board controls the overall process, while the slave boards, equipped with MT9M114 image sensors, handle image capture and processing. ToF sensors with STM32F446RE microcontrollers detect objects and trigger pneumatic controls.
  • A CAN mesh network facilitates communication between the boards, while Modbus protocol is used to control motors through VFDs and servo drives.

2. Sensors and Image Processing:

  • MT9M114 image sensors on the slave boards capture images at various stages of the food processing line. These images are processed on the board to detect attributes like food quality and size, and the processed data is sent to the master board for further action.
  • VL53L5CX Time of Flight (ToF) sensors detect objects and provide distance measurements, which are critical for real-time decision-making in the system. These sensors are used to control pneumatic actuators that manipulate food items as they move through the processing stages.

3. Motor and Pneumatic Control:

  • The Modbus protocol interfaces with VFDs and servo drives to control motor operations, including conveyor belts, cutting mechanisms, and other moving parts of the food processing system. Pneumatic actuators are triggered by signals from the ToF sensors and image processing outputs.

4. HMI and Cloud Integration:

  • The HMI provides a user-friendly interface for operators to monitor and control the system. It offers real-time insights into system performance and operational parameters, making it easier to manage the complex workflow.
  • The HMI is also integrated with a cloud platform for data upload. This data is used for machine learning models to optimize system performance, detect anomalies, and improve food processing efficiency over time.

Technology Used

  • TM4C1294 Microcontroller: Manages both the master and slave boards, handling communication, sensor data processing, and motor control.
  • MT9M114 Image Sensor: Captures images at various stages of the food processing workflow, enabling real-time image processing and quality assessment.
  • VL53L5CX Time of Flight Sensor: Detects objects and measures distances, crucial for triggering pneumatic operations with high accuracy.
  • STM32F446RE: Used in the ToF sensor board, providing precise control and data collection for real-time decision-making.
  • CAN Protocol: Establishes a mesh network between the master and slave boards for efficient and reliable data communication.
  • Modbus Protocol: Controls motors via VFDs and servo drives, allowing for precise and synchronized movement of food processing machinery.
  • SPI, I2C, UART: These protocols enable seamless communication between various system components, ensuring efficient and coordinated operation.
  • HMI (Human-Machine Interface): A touch display that allows operators to control and monitor the machine, and provides a gateway for data upload to the cloud for machine learning insights and system optimization.

Key Features

  • Automated Axle Alignment: The system provides fully automated axle alignment, requiring minimal operator intervention. The C# application controls the process, ensuring precise calibration and alignment.
  • High-Precision ILR Sensors: The ILR sensors provide accurate deviation measurements, ensuring that the axles are aligned to the necessary tolerances for safe and efficient truck operation.
  • Hydraulic Pressure Application: The system uses hydraulics to apply corrective pressure to both dead and drive axles, with the ability to adapt to different vehicle chassis configurations.
  • Dual Synchronous Mode: The system operates in a dual synchronous mode, allowing for the simultaneous alignment of multiple axles with a single button press, reducing the time required for alignment tasks.
  • Intelligent C# Application: The C# application provides advanced features for managing the alignment process, including real-time control, calibration, alignment, and report generation. Its intuitive interface makes it easy for operators to control the system.

Design Outcome :

The Multi-Axle Alignment System provides a fully automated, high-precision solution for aligning the axles of multi-axle trucks. The combination of PLC control, ILR sensor feedback, hydraulic correction, and an intelligent C# application ensures that the system is both efficient and accurate. With its ability to operate in dual synchronous mode, the system significantly reduces the time required for axle alignment while maintaining the precision needed for the manufacturing of heavy-duty trucks. The detailed reporting feature further ensures compliance with quality standards, making this system an essential tool in the production of multi-axle vehicles.

Technology Used

  • TM4C1294 Microcontroller: Manages both the master and slave boards, handling communication, sensor data processing, and motor control.
  • MT9M114 Image Sensor: Captures images at various stages of the food processing workflow, enabling real-time image processing and quality assessment.
  • VL53L5CX Time of Flight Sensor: Detects objects and measures distances, crucial for triggering pneumatic operations with high accuracy.
  • STM32F446RE: Used in the ToF sensor board, providing precise control and data collection for real-time decision-making.
  • CAN Protocol: Establishes a mesh network between the master and slave boards for efficient and reliable data communication.
  • Modbus Protocol: Controls motors via VFDs and servo drives, allowing for precise and synchronized movement of food processing machinery.
  • SPI, I2C, UART: These protocols enable seamless communication between various system components, ensuring efficient and coordinated operation.
  • HMI (Human-Machine Interface): A touch display that allows operators to control and monitor the machine, and provides a gateway for data upload to the cloud for machine learning insights and system optimization.

Key Features

  • Master-Slave Architecture: The system is designed with a master-slave configuration, where the TM4C1294 microcontroller controls multiple slave boards, each equipped with image sensors and other peripherals.
  • Real-Time Image Processing: MT9M114 image sensors are used to capture images at different processing stages. These images are processed locally on the slave boards and transmitted to the master for further analysis and action.
  • Object Detection and Pneumatics: The system uses VL53L5CX ToF sensors to detect objects in real time and control pneumatic actuators that handle food items on the processing line.
  • Motor Control with VFDs and Servo Drives: The Modbus protocol controls motors via VFDs and servo drives, allowing precise adjustment of speed, torque, and position in various parts of the system.
  • HMI and Cloud Connectivity: The HMI provides operators with a user-friendly interface to monitor and control the system. It also connects to the cloud for data upload, enabling machine learning-based optimization of the food processing operations.
  • Multi-Protocol Communication: The system uses multiple communication protocols, including CAN, SPI, I2C, and UART, to ensure efficient data transfer between components and control boards.

Design Outcome :

The food processing system is a fully automated solution designed for precision and efficiency. The use of master-slave architecture, image processing, and object detection through ToF sensors ensures that the system operates seamlessly in real-time. The integration of Modbus-based motor control and pneumatic operations further enhances its versatility. With an intuitive HMI for high-level control and cloud connectivity for data analytics and optimization, the system offers a state-of-the-art solution for modern food processing plants.

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