Description:

Ensuring consistent quality in manufacturing lines can be a challenge. This is where vision systems with AI come in, offering a potential solution to streamline quality control and improve overall consistency.

Fisher & Paykel Healthcare is looking to conduct research into the viability of using development of a vision system that can be used in line with a manufacturing process in the OSA department.

The desired output is a self-contained system that will output a pass/fail result and categorize the failure condition as components are presented to the system within a 10-second cycle time.

You will be provided with predefined pass and fail component samples and a document that defines the failure categories.

You will have access to an engineering manager and process development engineer for guidance on a regular basis. You will also have supervised access to Fisher & Paykel Healthcare’s manufacturing plant, workshops and labs as required.

Type:

Undergraduate

Outcome:

The output is:

• Design imaging module: Design and implement an imaging module to efficiently capture images of component samples for training and testing the AI model.
• Dataset Creation: Build a comprehensive dataset of component samples, categorized into "pas" and "fail" with subcategories for specific failure types.
• Detection Module: Build and train a deep learning neural network capable of accurately classifying component samples as "pass" or "fail" based on the collected image data.
• Frequency and Time: Extract insights into the frequency and timing of different types of failures, potentially revealing underlying process issues.

Prerequisites

None

Specialisations

• Computer Systems Engineering
• Software Engineering

Categories

• AI & Machine learning
• Image & Voice Processing
• Intelligent Systems & Industrial Informatics

Supervisor

• Mahla Nejati

Co-supervisor

• Trevor Gee

Team

• Qi Qin
• Kylyn Wu

Lab

Robotics (405.652, Lab)