Advanced Manufacturing and Adaptive Processes

The UK remains a forerunner in manufacturing technology with over 605,000 engineering enterprises, many of which specialise in these disciplines. As technology continues to progress, it is imperative that the UK maintains this position.

One manner in which this is possible is through the use of adaptive and intelligent manufacturing processes; utilising state of the art technological solutions alongside skilled human operators for novel and innovative solutions. We work alongside the Digital Automation Systems Design Lab to understand the complex relationship between the manufacturing process and human factors, and how they are inextricably linked together within the workplace. This lab has extensive expertise in traditional manufacturing process design as well as adaptive, intelligent manufacturing systems. We work alongside our industrial partners to understand their current manufacturing processes and their emerging technological needs to develop exciting and novel solutions. 

Current PhD & EngD Students

Daniel DeBecker (PhD)
“Hybrid Manufacturing using an Industrial Robotic Arm within the Railway Maintenance Industry”

The use of additive manufacturing has grown exponentially over the past 5 years. However, even with the increased uptake of this relatively old manufacturing process, it still lacks the quality and accuracy that can be achieved through conventional manufacturing, which still dominates around 70% of the manufacturing market. Even with the increased complexity that additive manufacturing allows, it still lacks the required surface finish and tolerances for most production components. This has led to additive manufacturing predominantly being used for the prototyping stage within a products life cycle. Conventional subtractive manufacturing approaches are effectively the inverse of this, where the complexity of the component is limited but the accuracy of each component is extremely high. In order to utilise the high complexity of additive manufacturing and the high accuracy of subtractive manufacturing, research has been conducted into combining these two processes leading to a form of hybrid manufacturing. The aim of this project is to investigate hybrid manufacturing within the rail industry for maintenance purposes, such that track may be maintained with greater efficiency than with current processes.

Jim Dobrzanski (PhD)
"Automation of TIG Welding – Process Control and Human Skills"

Traditionally the introduction of automation into mass manufacturing has concentrated upon lower skilled areas. High volume production is typically complemented with the use of high cost fixturing, to overcome part disparity and provide a reliable means of accurate and repeatable manufacturing. Competition to established manufacturing comes from rising economies where labour costs are significantly lower, overcoming a technological disadvantage with large numbers of human operatives. In established manufacturing centres knowledge transfer from human operatives to autonomous systems enables product manufacture to remain competitive. The aims of this research are to identify Key Process Variables (KPVs) within autonomous welding, to capture human skill during the welding process and identify the key sensory feedback utilised in the TIG welding process  and to enable additional monitoring and control of the welding process with the addition of enhanced sensory feedback.

Keith Lorenz (EngD)
“Reducing Process Variability through Automation in Hybrid Manufacturing”

Within industry, there is a huge demand for highly skilled CNC machining operators and programmers to plan and execute Hybrid Additive Manufacturing processes. This results in a poorer uptake of the approach than might be possible if the need for such highly skilled individuals could be reduced. For Hybrid Additive Manufacturing to be widely adopted, certain key factors must be addressed to remove as much cognitive load from the machining operator and programmer as possible. This includes: development of automated tool set up, automated monitoring of the Additive Manufacturing process, and integration of CAD CAM software for Hybrid manufacturing and machining. The aim of the research is to develop an automated tool set up and monitoring system for an advanced Hybrid Additive Manufacturing process.

 

Past PhD Students

Tom Bamber
“Climbing Advanced Drilling RoBOT (CADBOT)”

Large-scale manufacturing is a key part of current UK manufacturing enterprise and the retention and growth of this industry is a key focus politically, economically and for sustainability purposes. One way towards doing this is intelligent automation; the use of highly capable technologies to cope with process variation and difficult conditions. Within manufacturing here are numerous tasks that are highly unsuitable for humans to do and, if possible, they should be automated. Such jobs involve much repetition, are dangerous or could cause long-term harm to the operator. These jobs, with the right automation, could be reassigned to a robot. The purpose of this research was to investigate the potential for the removal of a number of barriers preventing robots from working in large-scale manufacturing through the design of a next generation crawler robot for advanced robotic drilling.

Jianglong Guo
"Numerical and Experimental Study of Electroadhesion to Enable Manufacturing Automation”

Electroadhesion is an electrostatically controllable attractive effect between an electroadhesive pad and a substrate. Although electroadhesion is a promising and potentially revolutionising material handling technology due to its distinctive advantages such as enhanced adaptability, gentle handling, reduced complexity, and ultra-low energy consumption, the applicability of this technology is currently constrained as there is a lack of an in-depth understanding of electroadhesion, both theoretically and experimentally. In addition, there is a lack of an effective, efficient, and confident research methodology and platform aiding the electroadhesive pad design, manufacture, and testing. The aims of this research were to identify the factors influencing the electroadhesive forces, and to conduct theoretical optimisation and electrostatic simulation modelling of ectroadhesion and the experimental verification based on a confident pad design, manufacture, testing platform and procedure.

Prasad Manorathna
“Intelligent 3D Seam Tracking and Adaptable Weld Process Control for Robotic TIG Welding”

Tungsten Inert Gas (TIG) welding is extensively used in manufacturing, due to its unique ability to produce higher quality welds compared to other shielded arc welding types. However, most TIG welding is performed manually and it has not achieved the levels of automation that other welding techniques have. These types of applications need intelligent decision making capabilities to accommodate any unexpected variation and to carry out the welding of complex geometries. Such decision making procedures must be based on feedback about weld profile geometry. For this research, a real-time position based closed loop system was developed with a six axis industrial robot, a laser triangulation based sensor and data acquisition system for full computer control.  

‎Yuchen Zhao 
“Human Skill Capturing and Modelling using Wearable Devices”

This thesis aims to reduce robot programming efforts significantly by developing a methodology to capture, model and transfer the manual manufacturing skills from a human demonstrator to the robot. Wearable sensors are investigated as a promising device to record the state-action examples without restricting the human experts during the skilled execution of their tasks. The thesis provides a methodology to produce a state-action model from skilled demonstrations that can be translated into robot kinematics and joint states for the robot to execute.

 

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