Simulation Tools for Automated and Robust Manufacturing

The aim of this project is to use statistical methods to develop "green button" manufacturing processes: processes that can be run without a human operator, and can respond to unpredictable variations in the properties of the materials that are being machined. We will be focussing on "high value, low volume" manufacturing: manufacturing relatively small numbers of very expensive components, where it is costly to have to scrap a component because of a fault in the machining process. We will work on a case study: machining the landing gear of an aircraft, which we will use to develop methods that can be applied more generally. The first step will be to build a computer model of the machining process. Given the computer model, we can experiment with different parameters of the machining process such as the speed at which the metal is cut, and the path that the cutting tool takes through the metal. In theory, we could then search for the best choice of parameters, such that the component is machined in the shortest time and is least likely to be defective. However, the properties of the metal to be cut will vary from item to item, so what is best for one item may not be best for another. We can't measure all the relevant properties, so we need to first assess how much variability we are likely to see, and then find parameter settings that best able to handle this variability without producing faulty items.

Once we have determined the best parameter settings, we will then run a small number of machine cutting tests at different choices of machine cutting parameters. During these tests, we will take high quality but expensive measurements, telling us for example, the temperatures and forces exerted on the cutting tools. This information will tell us whether the process is operating satisfactorily, or whether there is a risk of tool damage and possibly a faulty machined component. We will also take lower quality, cheaper, sensor measurements, of the sort that would be available during the manufacturing process in the factory. We will study the relationship between all the variables that we have measured, so that we can construct a simulation model of the entire manufacturing process. (We can also make corrections to the computer model predictions, by inspecting how well the computer model predicts the cutting test outcomes). We can then use the simulation model to explore different strategies for modifying the process mid-production, in response to the cheaper sensor data, to avoid faults (eg "reduce the cutting speed by 10%" if a sensor reports vibration 5% above average"). It will be cheaper and faster to design the automated process using the simulation model, rather than conducting more expensive cutting tests.

The end product will be a manufacturing process that can run efficiently without a human operator, making adjustments as the sensor data are observed, and will be configured in such a way so that it can deal with variability in the properties of the items to be machined. Our aim is to produce statistical methodology for configuring such a process, that can be applied in many different settings.

This project has been designed around the potential for economic impact, and has an obvious main intended beneficiary: the UK manufacturing sector. We believe that to remain globally competitive, it will be important for manufactures in industries such as aerospace, rail and the energy sector to develop state of the art, fully automated manufacturing facilities. One challenge in developing such a facility is in handling variability in the material properties of items to be machined, exacerbated by difficulties in measuring the properties directly. An impact of this research will be to give manufacturers tools to overcome this challenge. We will develop methodology for quantifying uncertainty about the variable properties, deriving optimal machining settings that account for this variability, and deriving control rules for adjusting the machining settings mid-process, in response to sensor data. Our methodology will be largely simulation-based, relying on as little physical experimentation is possible. This will help manufactures develop their automated processes more quickly, and at less expense.

Other impacts will result from the training of the researchers on this project. They will gain valuable experience of industrial collaboration (for example, with our project partner Messier-Bugatti-Dowty), multidisciplinary research, and giving training to representatives from industry, from helping to prepare and deliver our training courses.