A framework to support automation in manufacturing through the study of process variability
thesisposted on 14.09.2016 by Angel Sanchez-Salas
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
In manufacturing, automation has replaced many dangerous, mundane, arduous and routine manual operations, for example, transportation of heavy parts, stamping of large parts, repetitive welding and bolt fastening. However, skilled operators still carry out critical manual processes in various industries such as aerospace, automotive and heavy-machinery. As automation technology progresses through more flexible and intelligent systems, the potential for these processes to be automated increases. However, the decision to undertake automation is a complex one, involving consideration of many factors such as return of investment, health and safety, life cycle impact, competitive advantage, and resources and technology availability. A key challenge to manufacturing automation is the ability to adapt to process variability. In manufacturing processes, human operators apply their skills to adapt to variability, in order to meet the product and process specifications or requirements. This thesis is focussed on understanding the variability involved in these manual processes, and how it may influence the automation solution. Two manual industrial processes in polishing and de-burring of high-value components were observed to evaluate the extent of the variability and how the operators applied their skills to overcome it. Based on the findings from the literature and process studies, a framework was developed to categorise variability in manual manufacturing processes and to suggest a level of automation for the tasks in the processes, based on scores and weights given to the parameters by the user. The novelty of this research lies in the creation of a framework to categorise and evaluate process variability, suggesting an appropriate level of automation. The framework uses five attributes of processes; inputs, outputs, strategy, time and requirements and twelve parameters (quantity, range or interval of variability, interdependency, diversification, number of alternatives, number of actions, patterned actions, concurrency, time restriction, sensorial domain, cognitive requisite and physical requisites) to evaluate variability inherent in the process. The level of automation suggested is obtained through a system of scores and weights for each parameter. The weights were calculated using Analytical Hierarchical Process (AHP) with the help of three experts in manufacturing processes. Finally, this framework was validated through its application to two processes consisting of a lab-based peg-in-a-hole manual process and an industrial process on welding. In addition, the framework was further applied to three processes (two industrial processes and one process simulated in the laboratory) by two subjects for each process to verify the consistency of the results obtained. The results suggest that the framework is robust when applied by different subjects, presenting high similarity in outputs. Moreover, the framework was found to be effective when characterising variability present in the processes where it was applied. The framework was developed and tested in manufacturing of high value components, with high potential to be applied to processes in other industries, for instance, automotive, heavy machinery, pharmaceutical or electronic components, although this would need further investigation. Thus, future work would include the application of the framework in processes in other industries, hence enhancing its robustness and widening its scope of applicability. Additionally, a database would be created to assess the correlation between process variability and the level of automation.
EPSRC Centre for Innovative Manufacturing in Intelligent Automation under grant reference number EP/IO33467/1
- Mechanical, Electrical and Manufacturing Engineering