No abstract

Manufacturing equipment embraces an increasing measure of tacit intelligence, in both capacity and value. However, this intelligence is yet to be exploited effectively. This is due to both the costs and limitations of developed approaches and a deficient understanding of data value and data origin. This work investigates the principal limitations of typical machine tool data and encourages consideration of such inherent limitations in order to improve potential monitoring strategies. This work presents a novel approach to the acquisition and processing of machine tool cutting data. The approach considers the condition of the monitored system and the deterioration of cutting tool performance. The management of the cutting process by the machine tool controller forms the basis of the approach, and hence, makes use of the tacit intelligence that is deployed in such a task. By using available machine tool controller signals, the impact on day-to-day machining operations is minimised while avoiding the need to retrofit equipment or sensors. The potential of the approach in the contexts of the emerging internet of things and intelligent process management and monitoring is considered. The efficacy of the approach is evaluated by correlating the actively derived measure of process variation with an offline measurement of product form. The potential is then underlined through a series of experiments for which the derived variation is assessed as a direct measure of the cutting tool health. The proposed system is identified as both a viable alternative and synergistic addition to current approaches that mainly consider the form and features of the manufactured component.

The deterioration of cutting tools plays a significant role in the progression of subtractive manufacturing and substantially affects the quality of machined parts. Recognising this most organisations have implemented conventional methods for tool management. These reduce the economic loss associated with time-dependent and stochastic tool wear, and limit the damage arising from tools at end-of life. However, significant costs still remain to be addressed and more development towards tool and process prognostics is desirable. In response, this work investigates process deterioration through the acquisition and processing of selected machine signals. This utilises the internal processor of a CNC Vertical Machining Centre and considers the possible applications of such an approach for the prediction of tool and process health. This paper considers the prediction of tool and process condition with a discussion of the assumptions, benefits, and limitations of such approaches. Furthermore, the efficacy of the approach is tested using the correlation between an offline measurement of part accuracy and an active measure of process variation.