Metal AM Research

Analyzing 3D-printed aluminum samples from a Laser Powder Bed Fusion (LPBF) process to develop techniques for defect reduction and detection.

  • 1. Top Surface Image

    Showing the top surface of an aluminum 3D-printed sample using Laser Powder Bed Fusion.

  • 2. Surface Roughness

    Heat map showing variations in height.

  • 3. Calculating Surface Roughness

    Analyzing the image using Fiji (ImageJ2) yields a height value for each pixel.

  • 4. Internal Microstructure

    Showing crack and pore defects in the cross-section of the aluminum sample. Image taken with a microscope.

  • 5. Deep Learning Classification

    By training Fiji’s (ImageJ2) deep learning algorithms, the defects can be segregated into binary classes.

  • 6. Threshold Classification

    Thresholding is another way to transform an image into binary classes. It works by filtering based on the intensity value of each pixel.

  • 7. Results Example

    Results demonstrate that surface roughness is proportional to the number of internal defects. This means that by looking at the top surface alone, the number of internal defects can be accurately predicted!

Design

Designing a sensor package to be buried under the ground at the base of dams and levees for monitoring and safety.

RESEARCH - NEW DESIGN

  • 1. The Smooth Stone

    The “Smooth Stone” is a new iteration of the design that takes into further account the applications and feasibility of the package. This design was waterproofed, and the cavity from the original design was replaced to improve accessibility. (See image 6 “Waterproofing” for design changes.)

  • 2. The Circuit

    The bread and butter of our sensor package. Uses an accelerometer (MPU-6050), real-time clock, and an SD card to measure vibration and data log.

  • 3. Data Logger

    Data is written on the SD card in CSV format. Writing begins with the date and time, followed by x-raw, y-raw, and z-raw values from the accelerometer.

  • 4. Does It Work?

    Plotting the data demonstrates a working system. Spikes in the graph occur when vibrating the sensor.

  • 5. Application

    Tests with the package in practical applications are coming soon... For now, the next step is to work on strictly defining parameters so that both the design, and the sensors used, can be optimized. This will likely include adding a variety of other sensors.

  • 6. Waterproofing

    O-rings are used to seal the gap between the top and bottom pieces. A tight fit creates a waterproof seal. The cavity of the original design is replaced with a ridge to hold a dividing plexiglass layer. This allows easy access to sensors stored below the plexiglass.

RESEARCH - ORIGINAL DESIGN

  • 1. The Pop-In Stone

    The initial design. Nicknamed the ‘Pop-In Stone’ for its stone-shaped figure and its pop-in locking feature.

  • 2. The pop-in locking mechanism

    With the flexibility of the material in mind, the round ridge of the bottom piece should snuggly pop into place, locking the top and bottom together without the use of screws, nuts, or bolts.

  • 3. Confirmation and application

    Here the model includes a new piece (on the left) used to mount the accelerometer (it will be fixed inside the bottom piece). This also includes two (2) boxes used to confirm that the Arduino and battery will fit as intended.

PROJECTS

An introduction to my passion project, “Exploring Electromagnets”. Here I demonstrate how I began creating electromagnets and how I have improved the process to make the electromagnets within seconds.

P1. Exploring Electromagnets

P2. Putting Electromagnets to good use.

A timeline which demonstrates the exploration of different applications for the electromagnets.

C.A.D. Creations