Pickleball Simulator

Jan 2021 - April 2023

Abstract

I designed a 3D-printed pickleball equipped with an innovative impact localization system using five strategically placed load sensors. The system accurately detects the force and point of impact when the ball is struck, leveraging real-time data processing to map contact locations. This project showcases expertise in sensor integration, 3D printing, and impact analysis, blending mechanical design and electronics to enhance gameplay insights and training tools.

Goals

  • Design an accurate 3D-printed pickleball.
  • Fabricate all components.
  • Integrate load sensors for force data and positioning.

Targets

  • Create a graphical interface.
  • Create a localization python script to determine where the ball hit.
  • Record accuracy testing.

Project Features

Solidworks
Sensor Integration
Hardware Selection
PCB design and wiring
Laser cutting
3D-printing

Load Sensor Selection

The main feedback of the pickleball “coach” came from five FX292X-100A-0010-L, 10 lbf, 6V max input, 20mV max output compression load cells. Load cells encompass a wide range of electrical sensors that utilize internal strain gauges to measure the force applied to a test object.

FX292 load cells were chosen as their measure of compressive forces ranging from 10-100 lbf normal to the top of the load cells matched the direction and magnitude of force that we hypothesized a pickleball would act on a paddle. Online reference of a pickleball serve generating 24 lbf also supported our choice of load cell.

Design

The shaft, paddle, and electronics housing were 3D printed, the top plate was laser cut with acrylic, and the user interface software of choice was Arduino IDE’s Serial Monitor. To amplify the signals of the load cells above the possible noise, I bought and installed 5 INA1125P amplifiers. I began by taking the height, width, and depth of a pickleball paddle as well as the depth of the load cells to extrude cut into the pickleball paddle face. To ensure the force of the pickleball compressed the load cell and distributed the force of pickleball hit, I laser cut an acrylic top to fit over the five load cells. On the electronics side, I soldered the exposed ends of each load cell to male Arduino-compatible wire to increase the range of the load cells over the entire pickleball paddle face. Additionally, four holes were cut extruded through the entire depth of the paddle to manage the wires coming from the 5 load cells. The electronics housing were dimensioned to be as small as possible to avoid interference with the use of the paddle.

Sensor Calibration

To ensure the accuracy of the load cells, I had to calibrate each to known loads. The calibration process included weighing two known masses, setting the masses on a load cell, entering the pound values into the load lines of code, and entering the reading outputs from the load cell into the reading lines of code after the first initial run. Accuracy tests showed consistent readings with the mean and standard deviations demonstrating high accuracy defined by our repeatability test over the 3 different trials. The extremely low standard deviation(<0.002) values and stable mean values in each accuracy trial indicate that the load cells can provide accurate and repeatable measurements, which is essential for various applications, including quality control, research, and industrial processes.

End Results

I successfully developed a pickleball tool utilizing load cells and 3D-printed components. While this project began as an exercise to explore the applications of strain gauges and load cells, it highlighted the potential for sports-related training tools in a feasible market. Future enhancements could focus on refining the GUI, increasing accuracy with additional load cells, and incorporating localization features to provide more precise insights into where players make contact with the ball.