In-process Titanium Alloy Microstructural Information: Towards Intelligent Adaptive Machining Strategies for Lean Manufacturing.

Wednesday, March 16, 2022: 4:30 PM
104 (Pasadena Convention Center)
Dr. Daniel Suarez Fernandez , The University of Sheffield, Sheffield, United Kingdom, Advanced Manufacturing Research Centre, Rotherham, United Kingdom
Mr. Thomas Childerhouse , The University of Sheffield, Sheffield, United Kingdom
Dr. Pete Crawforth , Advanced Manufacturing Research Centre, Rotherham, United Kingdom
Prof. Brad Wynne , University of Strathclyde, Glasgow, United Kingdom
Prof. Martin Jackson , The University of Sheffield, Sheffield, United Kingdom
This work presents a newly developed approach that uses the unique materials machining force response to characterise the microstructural features of a workpiece.

This in-process technique is non-invasive and non-disruptive and it is carried out using standard machining parameters and tool inserts. The materials response to machining is measured using internal and external sensors such as dynamometers, accelerometers and acoustic emission sensors. Through the analysis of these signals and materials characterisation, microstructural resolution has been achieved to higher levels than standard etching and optical analysis methods - and at zero time cost.

The machining force fluctuation response has been correlated to microstructural features and validated using conventional characterisation methods. The experiments performed demonstrate the possibility of mapping large areas (and in some cases, entire components) while performing grain size measurements, detecting specific microstructural features and adverse heat treatment effects that could have a detrimental impact on performance. This opens the exciting possibility of tailoring machining processes based on in-situ local material response and properties (Intelligent or Smart Manufacturing).

This technique is now being integrated into a larger system, with two main objectives: the first is to automatically identify microstructural features or process dynamics that can compromise the component’s performance. The second is to use the “workpiece fingerprint” from the force response, to modify the machining parameters in real time, with the aim of maximising (1) the component’s structural integrity and (2) the efficiency of the machine resources. All this data-rich analysis will also contribute to the identification of non-conformant material earlier in the manufacturing process, as well as aiding traceability and quality assurance.