This opportunity is now closed.

Funded PhD Opportunity

Machine Vision for Automated Inspection of Hard Drive Components

This project is funded by: Seagate Technology (Ireland)

Subject: Computer Science and Informatics


Summary

Many machine vision systems today are more than pure inspection systems, as they make it possible to recognise trends in production processes early on often using intelligent reasoning of the complete production process. Intelligent reasoning is achieved via computational intelligence which deals with the development of algorithms inspired by human cognition. In simple terms, computational intelligence seeks to develop models that can reason, understand or learn like a human. The ability to spot patterns, adapt to new and unusual data, and to be robust to non-perfect data are hallmarks of computational intelligence methods and are all issues encountered in current industrial processes.

Computational intelligence alongside machine vision can deliver both the primary inspection information in early stage manufacturing and also comprehensive and important secondary information relating to the production process. This combination of machine vision and computational intelligence dovetails very nicely with the requirements of an automated inspection system - to spot trends in past data and identify changes in the future; automated inspection systems require a model which can adapt to changing product designs in a controlled, easy to understand fashion; and a model which will not completely fail due to potentially noisy data/outliers.

Machine vision can include many methods however the dominant approach found within industry but often a template matching approach where a pixel-to-pixel comparison of two images is performed is applied. This technique is especially useful when the object's surface or object's shape is very complex and when finding defects like smudges, scratches etc. The reference image is a previously prepared image which is used to compare with images from the camera. This technique allows quick comparison inspection but some specific conditions must be met such as stable light conditions, position of the camera and the object must be static, precise object positioning, and camera calibration. Additionally a significant problem with this technique is that even small changes in product design require the preparation of an updated reference image which takes a significant amount of time given frequent product revisions.

This project will develop an improved suite of machine vision technologies suitable for the automated inspection of wafers. The reference image approach will be replaced by alternative image processing techniques based on local features combined with computational intelligence techniques so that changes in the product design will not require an updated template for the detection of defects. Rather than performing pixel by pixel comparisons between complete images, the system will be able to detect localised image features that correspond to a particular defect type. In addition, the intelligent reasoning system will be capable of monitoring existing and detecting new defects and image changes that occur as a result of process design changes, equipment wear or which are a result of earlier processes.  This will enable the system to maintain continuous service even new or update products are developed.


Essential criteria

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • First Class Honours (1st) Degree
  • Masters at 70%
  • For VCRS Awards, Masters at 75%
  • Publications - peer-reviewed

Funding

This project is funded by: Seagate Technology (Ireland)

The scholarship will cover tuition fees at the home rate and, for applicants with UK residence only, a maintenance allowance of not less than £15,480 per annum for three years. EU residents may also apply but if successful will receive fees only.


Other information


The Doctoral College at Ulster University


Reviews

Profile picture of Adrian Johnston

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

Watch Video  

Key dates

Submission deadline
Monday 19 February 2018

Interview Date
March 2018


Applying

Apply Online  


Contact supervisor

Professor Sonya Coleman


Other supervisors

Related Funded Opportunities

Context aware Brain Computer Interfaces and Internet of Things (IoT)

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Autonomous Object Recognition for Robots

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Neural Data Science, Computational Neuromodulation, and Metalearning

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Augmented Reality Brain-computer Interface

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Gaining a better understanding of our Planet through Deep Learning-based Data Analytics

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Data Analytic Technologies to Combat Human Trafficking

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Adaptive Learning for Modelling Non-stationary Dynamical Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Computational and Mathematical Modelling of Predator-Prey Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Computing

Subject: Computer Science and Informatics

 View details

Applying Natural Language Processing to the automated fact checking of legal documents

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Deep-learning assisted tele-medicine for the delivery of diabetic retinopathy screening in low- and middle-income countries

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Intelligent Systems

Subject: Computer Science and Informatics

 View details

The impact of the analytical performance of laboratory tests on clinical decision making

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Sensing human emotion within pervasive environments

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Magic Hands: Non-invasive hand tracking for virtual reality and game based stroke rehabilitation.

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Intelligent Mobility Aids to Promote Physical Activity in Children with Cerebral Palsy

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Nursing and Health

 View details

Network Machine Learning Approach to Financial Crime Detection

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Trusting the uncertainty in machine learning predictions

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Mapping the Brain with Zero Knowledge using advanced AI and Data Analytics

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Neuro Sense: serious games for in-situ autism assessment

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Exploiting Brain Inspired Information Processing in Hardware to Develop Highly Reliable, Always-on Smart Sensor Systems.

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Autonomous Decision Analytics by Integrating Machine Learning and Symbolic Reasoning

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

An Approach for Constructing and Sharing Open Data Sets in Experimental Pervasive Computing

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

RECASE: Robot-Enabled Care system in Smart Environments

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Sensing and Modelling Air Quality for Healthy Living (SMAQ)

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Chatbots for decision support and reporting in healthcare

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

PostCrypt: Data Security for the future

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Enhanced Augmented Reality with Data Engineering and AI for Smart Digital Education

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Education

 View details

AI-enabled Automated Behaviour Analysis for User-centric Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Quantitatively assessment of limb motion utilising wearable sensors in remote rehabilitation

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Towards Trusted Cognitive Intelligence for User-centric Smart Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details