Project Assitive robotics presentation YK.pptx

Project Assitive robotics presentation YK.pptx

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  12/17/2025 1 Project reporton Assistive Robotics Hardware Setup & MATLAB Simulation of Z-SG Strain Gauge Converter with Load Cell By: Yonas Kebede MAMO Supervisor: Luca Cavanini (Ph.D.) December15, 2025
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  12/17/2025 2 Table ofContents 1 Introduction 2 Significance of the Study 3 Objectives 4 Materials and Methods 5 Results & Discussion 6 Conclusion 7. Future work
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  12/17/2025 3 Acronyms • MATLAB– Matrix Laboratory • mA – Milliampere • Vdc – DC Voltage • N – Newton
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  12/17/2025 4 Introduction  ALoad Cell is a force/weight sensor that converts applied mechanical force into an electrical signal using a strain gauge bridge.  The Z-SG Strain Gauge Converter conditions this weak millivolt signal and converts it into a usable analog/digital output (e.g., 0–5V, ±10V, RS485 Modbus).  MATLAB is used for calibration, scaling, tare correction, visualization, and real-time monitoring.  This setup forms the core of an efficient force measurement system suitable for laboratory and IoT applications.
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  12/17/2025 5 Introduction
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  12/17/2025 6 Significance ofStudy  Improved Measurement Accuracy Essential for experiments requiring high sensitivity, such as tissue testing and micro-force analysis.  MATLAB Simulation for Biomedical Force Sensing To simulate force measurement from a strain gauge-based load cell (S-AL 250N) using MATLAB, enabling biomechanical signal processing for medical applications such as prosthetics, rehabilitation, and tissue testing.  The project successfully developed a precise force measurement system using a load cell and Z-SG converter with MATLAB. By applying scaling, tare correction, and calibration, the system delivered accurate real-time force data in Newtons and millivolts with minimal error. These improvements make the setup suitable for industrial weighing systems, robotics force sensing, material testing, and automation applications.
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  12/17/2025 7 Objectives General Objective Todevelop a hardware setup and MATLAB simulation of a Z-SG strain gauge converter integrated with a load cell. Specific Objectives To scale Modbus-acquired data for accurate measurement in Newtons (N) and millivolts (mV). To implement tare correction in MATLAB for baseline adjustment. To perform calibration in MATLAB to ensure measurement accuracy and reliability.
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  8 Materials & Methods Hardware component • PS28DC Powe supply • Z-SG strain-gauge converter • Load cell • USB Cable  Software Components • MAT LAB 12/17/2025
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  9 Result and discussion •Experiment I. Scale the data collected via Modbus to obtain the correct values of N and mV • I attached below the real MAT LAB graph which shows the direct measurement with their description. 12/17/2025
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  12/17/2025 10 Experiment I. WeightStable, Signals Spiking Consistent Readings, Minor Surges Steady Weight, Brief Excursions Stable Output, Transient Spikes Reliable Measurements, Momentary Fluctuations
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  12/17/2025 11 Experiment II:Tare Correction Implement tare correction in MATLAB.
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  12/17/2025 12 Experiment II:Tare Correction Tare Restores Stable Weight. Transient Spikes, Weight Stabilized. Weight Baseline Achieved Quickly. Tare Corrects, Disturbances Brief. Stable Readings, Momentary Fluctuations.
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  12/17/2025 13 Experiment III:Calibration • Implement the calibration phase in MATLAB
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  12/17/2025 14 Experiment III:Calibration Calibrated force tracks events. Raw input converted accurately. Transient loads clearly detected. Calibration ensures precise force. Force readings reflect fluctuation.
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  12/17/2025 15 DIP Switchstatus
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  12/17/2025 16 Conclusion MATLAB-based experiments successfullydemonstrated a complete workflow for sensor data processing—from raw signal acquisition to calibrated force measurement. Sequential implementation of scaling, tare correction, and calibration progressively refined the system, achieving accurate and reliable output. • Scaling: Modbus data conversion into N and mV produced stable measurements with minor transient disturbances, confirming sensor consistency. • Tare Correction: Effectively removed offsets, stabilizing net weight signals around a baseline even during input spikes, ensuring precise repeated measurements. • Calibration: Converted raw millivolt readings into accurate force values, capturing sensor response to applied loads, particularly during transient events • .
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  12/17/2025 17 Future work •Noise reduction: Apply advanced filtering for cleaner signals. • Biomedical applications: Adapt system for monitoring physiological forces or prosthetic load measurements. • Automation: Integrate hardware and MATLAB into automated measurement systems.
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