Introduction
The identification and sorting process, with critical emphasis during manufacturing, is very
sensitive to critical modernity. This report introduces a Simulink model for the identification of
different products moving along a conveyor belt by their physical characteristics. It is equipped
with sensory data in helping it distinguish three diverse types of product and, accordingly, make
use of LED indicators in reflecting upon the product detected.
It mainly incorporates two major sensors: an LED sensor for the detection of the product presence
and a strain gauge for weighing the product. With this input, thejson system determines the product
type and hence illuminates the respective LED to indicate product type. The model is developed
based on diameter and weight to meet accurate identification and sorting of products.
Product Types:
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Round metal product: 20mm diameter, 60g weight.
Round plastic product: 20mm diameter, 30g weight.
Round hollow plastic product: 30mm diameter, 30g weight.
Sensors:
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Sensor 1: An LED sensor that detects the presence of a product.
Sensor 2: A strain gauge measuring the weight of the product.
Controller:
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Accepts +5V digital inputs.
Uses two inverting amplifiers to get +5V inputs.
Output:
Three LED lamps that indicate the product type.
Design and implementation
Input Blocks
So in this , constant blocks to simulate sensor readings for diameter and weight are being used.
Prejson," are written in the constant blocks, which has predefined example values written inside
the const blocks.
Logic Blocks
The "Compare To Constant" blocks evaluate the sensor readings in a humanly way. Those compare
the diameter and weight with given product thresholds that are configured. The kind of product is
determined using "Logical Operator" blocks that combine the outputs of the blocks, which are true
or false types.
Blocks of Output
LED indicator blocks from the "Simulink Dashboard" library indicate which type of product is
being detected. The blocks are set for the indication in case of the detection of any given type of
product.
System Design The next blocks are joined:
The inputs of diameter and weight are fed through into "Compare to Constant" blocks. Their
outputs are combined together using "Logical Operator" blocks to get the logic for each of the
products. The logical outputs of the blocks "Logical Operator" are connected to the LEDs to signal
the detected product.
clc;
clear;
% Initialize constants
METAL_WEIGHT = 60;
PLASTIC_WEIGHT = 30;
HOLLOW_PLASTIC_WEIGHT = 30;
METAL_DIAMETER = 20;
PLASTIC_DIAMETER = 20;
HOLLOW_PLASTIC_DIAMETER = 30;
diameter = 20; % Example: Replace this with actual sensor reading
weight = 60; % Example: Replace this with actual sensor reading
LED_Metal = 0;
LED_Plastic = 0;
LED_HollowPlastic = 0;
if diameter == METAL_DIAMETER && weight == METAL_WEIGHT
disp('Metal Product Detected');
LED_Metal = 1;
elseif diameter == PLASTIC_DIAMETER && weight == PLASTIC_WEIGHT
disp('Plastic Product Detected');
LED_Plastic = 1;
elseif diameter == HOLLOW_PLASTIC_DIAMETER && weight ==
HOLLOW_PLASTIC_WEIGHT
disp('Hollow Plastic Product Detected');
LED_HollowPlastic = 1;
else
disp('Unknown Product');
end
% Display LED states
disp(['LED for Metal: ', num2str(LED_Metal)]);
disp(['LED for Plastic: ', num2str(LED_Plastic)]);
disp(['LED for Hollow Plastic: ', num2str(LED_HollowPlastic)]);
Result and discussion
It shows further that the Simulink model was effective in categorizing the three products
effectively by their diameter and weight and also light an LED indicator for the respective product.
In the testing phase, the system could recognize each of the different products, as per the predefined
threshold. Logical operations, "AND" between different queries, and a "Compare To Constant"
worked fine in practice since the product had been properly classified.
The reflected output indicators, LED, and display blocks effectively indicated the type of product
under consideration. The logic the system followed, based on elementary comparisons, was
effective with least computational overhead. Further improvements include integrating dynamic
inputs that allow for real-time testing and increase the capacity of the system to handle more
product types.
This serves as a basic example for the development of more advanced systems of product
categorization and can be connected to actual sensors with actuators. In general, the requirements
for the created model are fulfilled since the model is accurate and efficient, and therefore it lays a
good basis for further development of the system in the area of mechatronic systems.
Conclusion
In this project, a Simulink model was developed to identify and categorize products based on their
physical characteristics, specifically diameter and weight. The system successfully distinguished
between three types of products and used LED indicators to signal the detected type. The
implementation, which utilized logical operations and comparative blocks, effectively met the
design requirements.
The simulation results demonstrated the model's accuracy, confirming its ability to function as
intended. The straightforward design, based on logical operations and constant comparisons,
ensured efficiency while maintaining simplicity. This project showcased the potential for such a
system to be used in practical applications, such as manufacturing or quality control, where product
identification and sorting are crucial.
Overall, the model provides a robust foundation for further enhancements or integration into more
complex mechatronic systems. The successful completion of this project underscores the value of
Simulink in designing and testing control systems for real-world applications.