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<h2>Problem Statement (Header 2)</h2>The conventional manufacturing processes using assembly fixtures&nbsp; lack the precision required for manufacturing miniaturized ultrasound devices at high-volume production. This is required to fit inside a small catheter. Achieving the necessary +/- 15 micron accuracy for critical assembly steps, including UV adhesive dispensing, part alignment, and clamping, often relies on manual or semi-automated methods. These methods are prone to human error, time-consuming, and inconsistent, leading to increased scrap rates, higher production costs, and limitations in scaling manufacturing output. Therefore, there is a critical need for an automated solution that can consistently deliver ultra-high precision across multiple assembly stages to enable reliable and efficient manufacturing of these advanced miniaturized components.<ol><li>Ordered List 1 </li><li>Ordered List 2</li><li>Ordered List 3</li></ol><h2>Motor Review</h2><div class="video-wrapper" style="width: 100%; max-width: 800px; margin: 0px;"><div class="video-container" style="position: relative; width: 100%; padding-bottom: 56.25%; height: 0px; overflow: hidden;"><iframe src="https://www.youtube.com/embed/cmRGhoF_ZEM" frameborder="0" allowfullscreen="" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div><h2></h2>

<h2>Bill of Materials (BOM)</h2>The following table lists the components used in the prototype, including part numbers, quantities, materials, estimated costs, and potential suppliers.<div><table><tbody><tr><td>Item</td><td>Component</td><td>Part Number</td><td>Qty</td><td>Material</td><td>Cost ($)</td><td>Supplier</td><td>Notes</td></tr><tr><td>1</td><td>Heat Sink</td><td>637-20ABPE</td><td>1</td><td>Aluminum</td><td>25.00</td><td>McMaster-Carr</td><td>100x100x50 mm, extruded aluminum</td></tr><tr><td>2</td><td>Cooling Fan (12V, 40 mm)</td><td>AFB0412SHB</td><td>1</td><td>Plastic/Metal</td><td>10.00</td><td>DigiKey</td><td>Low-noise, 35 dB max, 12V DC</td></tr><tr><td>3</td><td>Device Housing</td><td>Custom (3D-printed)</td><td>1</td><td>PLA</td><td>15.00</td><td>University 3D Print Lab</td><td>FDM-printed, 200x150x100 mm</td></tr><tr><td>4</td><td>Thermal Insulation Foam</td><td>851-074</td><td>0.5 m²</td><td>Polyurethane Foam</td><td>8.00</td><td>Amazon</td><td>Cut to fit optics compartment</td></tr><tr><td>5</td><td>Temperature Sensor</td><td>DS18B20</td><td>1</td><td>N/A</td><td>12.00</td><td>Adafruit</td><td>±0.5°C accuracy, digital output</td></tr><tr><td>6</td><td>Arduino Uno</td><td>A000066</td><td>1</td><td>N/A</td><td>25.00</td><td>Arduino Store</td><td>Runs Python PID via serial interface</td></tr><tr><td>7</td><td>Fan Muffler</td><td>Custom (3D-printed)</td><td>1</td><td>PLA</td><td>5.00</td><td>University 3D Print Lab</td><td>Reduces fan noise</td></tr><tr><td>8</td><td>Fasteners (Screws, M3)</td><td>91292A112</td><td>10</td><td>Stainless Steel</td><td>3.00</td><td>McMaster-Carr</td><td>M3x10 mm, for securing components</td></tr><tr><td>9</td><td>Thermal Paste</td><td>AS5-3.5G</td><td>1</td><td>Silicone-based</td><td>5.00</td><td>Amazon</td><td>Improves heat transfer to sink</td></tr></tbody></table></div><h2></h2>

<h2>Basic PID Controller Script </h2><pre class="language-python" tabindex="0" contenteditable="false" data-language="python"><code class="language-python">import time


class PIDController:
 """A PID controller for precise control in robotic systems.
 Attributes:
 kp (float): Proportional gain.
 ki (float): Integral gain.
 kd (float): Derivative gain.
 setpoint (float): Desired target value.
 prev_error (float): Previous error for derivative calculation.
 integral (float): Accumulated integral term.
 dt (float): Time step in seconds.
 """

 def __init__(self, kp: float, ki: float, kd: float, setpoint: float = 0.0):
 """Initialize PID controller with gains and setpoint.
 Args:
 kp: Proportional gain for error response.
 ki: Integral gain for accumulated error.
 kd: Derivative gain for error rate of change.
 setpoint: Desired target value (default: 0.0).
 """

 self.kp = kp
 self.ki = ki
 self.kd = kd
 self.setpoint = setpoint
 self.prev_error = 0.0
 self.integral = 0.0
 self.dt = 0.01
 
 def compute(self, current_value: float) -&gt; float:
 """Compute PID output based on current system value.
 Args:
 current_value: Current measured value of the system.
 Returns:
 float: Control signal to adjust the system.
 """
 # Calculate error
 error = self.setpoint - current_value
 # Proportional term
 p_term = self.kp * error
 # Integral term
 self.integral += error * self.dt
 i_term = self.ki * self.integral
 # Derivative term
 derivative = (error - self.prev_error) / self.dt
 d_term = self.kd * derivative
 # Calculate total output
 output = p_term + i_term + d_term
 # Update previous error
 self.prev_error = error
 return output


if __name__ == "__main__":
 # Initialize PID controller
 pid = PIDController(kp=1.0, ki=0.1, kd=0.05, setpoint=10.0)
 # Simulate mode (e.g., motor position)
 current_value = 0.0
 for _ in range(100):
 control_signal = pid.compute(current_value)
 # Simulate system response: position updates based on control signal
 current_value += control_signal * 0.1
 print(f"Current Value: {current_value:.2f}, "
 f"Control Signal: {control_signal:.2f}")
 time.sleep(pid.dt)</code></pre>

Design Review

<h2>Problem Statement</h2>The conventional manufacturing processes using assembly fixtures&nbsp; lack the precision required for manufacturing miniaturized ultrasound devices at high-volume production. This is required to fit inside a small catheter. Achieving the necessary +/- 15 micron accuracy for critical assembly steps, including UV adhesive dispensing, part alignment, and clamping, often relies on manual or semi-automated methods. These methods are prone to human error, time-consuming, and inconsistent, leading to increased scrap rates, higher production costs, and limitations in scaling manufacturing output. Therefore, there is a critical need for an automated solution that can consistently deliver ultra-high precision across multiple assembly stages to enable reliable and efficient manufacturing of these advanced miniaturized components.<h2>Bill of Materials (BOM)</h2>The following table lists the components used in the prototype, including part numbers, quantities, materials, estimated costs, and potential suppliers.<div><table><tbody><tr><td>Item</td><td>Component</td><td>Part Number</td><td>Qty</td><td>Material</td><td>Cost ($)</td><td>Supplier</td><td>Notes</td></tr><tr><td>1</td><td>Heat Sink</td><td>637-20ABPE</td><td>1</td><td>Aluminum</td><td>25.00</td><td>McMaster-Carr</td><td>100x100x50 mm, extruded aluminum</td></tr><tr><td>2</td><td>Cooling Fan (12V, 40 mm)</td><td>AFB0412SHB</td><td>1</td><td>Plastic/Metal</td><td>10.00</td><td>DigiKey</td><td>Low-noise, 35 dB max, 12V DC</td></tr><tr><td>3</td><td>Device Housing</td><td>Custom (3D-printed)</td><td>1</td><td>PLA</td><td>15.00</td><td>University 3D Print Lab</td><td>FDM-printed, 200x150x100 mm</td></tr><tr><td>4</td><td>Thermal Insulation Foam</td><td>851-074</td><td>0.5 m²</td><td>Polyurethane Foam</td><td>8.00</td><td>Amazon</td><td>Cut to fit optics compartment</td></tr><tr><td>5</td><td>Temperature Sensor</td><td>DS18B20</td><td>1</td><td>N/A</td><td>12.00</td><td>Adafruit</td><td>±0.5°C accuracy, digital output</td></tr><tr><td>6</td><td>Arduino Uno</td><td>A000066</td><td>1</td><td>N/A</td><td>25.00</td><td>Arduino Store</td><td>Runs Python PID via serial interface</td></tr><tr><td>7</td><td>Fan Muffler</td><td>Custom (3D-printed)</td><td>1</td><td>PLA</td><td>5.00</td><td>University 3D Print Lab</td><td>Reduces fan noise</td></tr><tr><td>8</td><td>Fasteners (Screws, M3)</td><td>91292A112</td><td>10</td><td>Stainless Steel</td><td>3.00</td><td>McMaster-Carr</td><td>M3x10 mm, for securing components</td></tr><tr><td>9</td><td>Thermal Paste</td><td>AS5-3.5G</td><td>1</td><td>Silicone-based</td><td>5.00</td><td>Amazon</td><td>Improves heat transfer to sink</td></tr></tbody></table></div><h2>Motor Review</h2><div class="video-wrapper" style="width: 100%; max-width: 800px; margin: 0px;"><div class="video-container" style="position: relative; width: 100%; padding-bottom: 56.25%; height: 0px; overflow: hidden;"><iframe src="https://www.youtube.com/embed/cmRGhoF_ZEM" frameborder="0" allowfullscreen="" style="position: absolute; top: 0px; left: 0px; width: 100%; height: 100%; border: none;"></iframe></div></div><h2>Basic PID Controller Script</h2><pre data-language="python" style="background-color: rgb(248, 249, 250); border: 1px solid rgb(233, 236, 239); border-radius: 6px; padding: 1rem; margin: 1rem 0px; overflow: auto; font-family: Consolas, Monaco, &quot;Courier New&quot;, monospace; line-height: 1.4; color: rgb(0, 0, 0); white-space: pre-wrap; font-size: 14px;"><div style="color: rgb(108, 117, 125); margin-bottom: 8px; font-weight: bold; font-size: 12px;">PYTHON</div><code class="language-python">import time

class PIDController:
 """A PID controller for precise control in robotic systems.

 Attributes:
 kp (float): Proportional gain.
 ki (float): Integral gain.
 kd (float): Derivative gain.
 setpoint (float): Desired target value.
 prev_error (float): Previous error for derivative calculation.
 integral (float): Accumulated integral term.
 dt (float): Time step in seconds.
 """

 def __init__(self, kp: float, ki: float, kd: float, setpoint: float = 0.0):
 """Initialize PID controller with gains and setpoint.

 Args:
 kp: Proportional gain for error response.
 ki: Integral gain for accumulated error.
 kd: Derivative gain for error rate of change.
 setpoint: Desired target value (default: 0.0).
 """
 self.kp = kp
 self.ki = ki
 self.kd = kd
 self.setpoint = setpoint
 self.prev_error = 0.0
 self.integral = 0.0
 self.dt = 0.01

 def compute(self, current_value: float) -&gt; float:
 """Compute PID output based on current system value.

 Args:
 current_value: Current measured value of the system.

 Returns:
 float: Control signal to adjust the system.
 """
 # Calculate error
 error = self.setpoint - current_value

 # Proportional term
 p_term = self.kp * error

 # Integral term
 self.integral += error * self.dt
 i_term = self.ki * self.integral

 # Derivative term
 derivative = (error - self.prev_error) / self.dt
 d_term = self.kd * derivative

 # Calculate total output
 output = p_term + i_term + d_term

 # Update previous error
 self.prev_error = error

 return output


if __name__ == "__main__":
 

 # Initialize PID controller
 pid = PIDController(kp=1.0, ki=0.1, kd=0.05, setpoint=10.0)

 # Simulate mode (e.g., motor position)
 current_value = 0.0
 for _ in range(100):
 control_signal = pid.compute(current_value)
 # Simulate system response: position updates based on control signal
 current_value += control_signal * 0.1
 print(f"Current Value: {current_value:.2f}, "
 f"Control Signal: {control_signal:.2f}")
 time.sleep(pid.dt)
</code></pre>

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I am a diligent mechanical engineering graduate student specializing in product design, manufacturing, and autonomous solutions with robotics experience.

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