Abstract
Machining has been at the centre of manufacturing technologies since the start of the industrial revolution, and so it is fitting that this topic receives special attention in a journal that can trace its origins back to 1847.
The first publication of the Proceedings of the Institution of Mechanical Engineers describes a healthy debate concerning the machining of gear teeth, 1 alongside an obituary to George Stephenson himself – the ‘father of railways’ and the founding president of the Institution of Mechanical Engineers. 2 The next 60 years saw a steady growth in scientific and technical dissemination of knowledge concerning machining, which is epitomised by Taylor’s 3 famous monograph of 1907.
Over 100 years later, we have of course seen huge leaps in our understanding of all manufacturing processes. But the strive for increased productivity and quality is now also matched by a need for resource efficiency in light of societal challenges such as climate change and pollution.
The contributions included in this special issue seek to demonstrate how machining science research is playing a role in addressing this challenge: a rethinking of manufacturing is underway as a consequence of machining learning, ubiquitous data, and networked computing and machining science is a key part of this shifting manufacturing landscape.
The topics that are covered are intentionally diverse: they illustrate a vibrant and creative scientific approach across the spectrum of material removal processes, and show emergent approaches that can harness flexible manufacturing processes, as well as data-driven and intelligent automation.
The special issue begins with a focus on novel techniques for monitoring the performance of machine tools and their cutting operations. Here, there have been great developments in machine learning techniques that can be brought to bear on production processes. To pick just two examples from the manuscripts: McLeay et al. 4 develop fault detection techniques based upon unsupervised learning methods, and Moore et al. 5 also demonstrate how machine learning concepts can be applied to machine health monitoring.
The deployment of these novel monitoring techniques necessitates effective measurement capabilities, and on novel manufacturing problems this can itself be a challenge. Alhadeff et al. 6 explore wear measurements in micro milling, whilst Duboust et al. 7 characterise surface roughness in machining of composites.
The machining of new materials, and workpieces produced using novel additive manufacturing techniques, also presents challenges. Several manuscripts within this special issue address these problems, focussing for example on Inconel (Curtis et al. 8 ), Titanium (Khan et al. 9 ) and metal-matrix composites (Saberi et al. 10 ).
Finally, the development of state-of-the art modelling techniques can help to improve the performance of machining processes, focussing for example on dynamic effects (Urena et al. 11 ) and Robotics (Rooker et al. 12 ).
At The University of Sheffield, we have been fortunate to be able to explore these avenues of research within the remit of an EPSRC Centre for Doctoral Training in Machining Science (Grant Reference EP/L016257/1). This special issue was borne from discussions with the journal’s editorial board, in particular Professors Maropoulos and Long, as a consequence of the doctoral training centre. Consequently, much of the work included in this special issue has been inspired by the work within the doctoral training centre. The guest editors, who are co-directors of the centre, are grateful for the support of the journal’s editorial office who have ensured an independent peer review process for these manuscripts. We also express our thanks to Dr Francesca Breeden for her assistance in coordinating the special issue.
