Philips Consumer Lifestyle (PCL) is an advanced manufacturing site located in Drachten, the Netherlands. Our organization falls within the Personal Heath business cluster of Philips, and is primarily concerned with the manufacturing of personal electric shavers.
Electric shavers are comprised of two principle component ‘blocks’: a body and a shaving unit. Each shaving unit contains three metallic shaving ‘heads’, which in turn are composed of a shaving blade (the cutting element) and shaver cap (the guard). The focus of the MANTIS project at PCL falls on the production of these shaver caps.
An electro-chemical process is used in the manufacturing of shaver caps, where an electric current is passed over the raw input material, which is conductive, in order to cut this material into the desired shape. Production of the shaver caps at PCL is fully automated.
Precision tooling is required throughout the various stages of shaver cap manufacturing. At present, these tools are built on-site, and are required to be kept in stock so that replacements are available in the event of tooling malfunctions. Having functional tools available around the clock is essential to meet our goal of 100% ‘up-time’ for our assembly lines. However, this is an expensive approach to resolve the problem, both in terms of the additional equipment required and extensive down-time that results from manual tooling replacements. Therefore, the timely maintenance of these tools presents a challenge.
Currently the maintenance strategy on the production line for shaver caps is a mixture of reactive and preventive maintenance. In line with the Mantis goal, our goal is to transform this towards a predictive or even a prescriptive maintenance strategy. However, this comes with the need for data. In order to perform maintenance on the tooling at exactly the right moment needed, information is necessary about the tooling to make useful decisions.
The data directly related to the current state of tooling (e.g. degree of wear, damages, etc.) is hard to retrieve in some cases, due to process-specific reasons. In our use case the tooling is delicate and very precise (micron range, difficult geometries), which makes frequent measurements of the tooling difficult and expensive in a mass production environment. Currently, there is only indirect data available about the use of the tools in the production machines, but not about the actual state of the tool itself. These data can be used to estimate, for example, the remaining useful life of a tool, but in order to improve and verify the RUL prediction models, more direct data is necessary.
Tool wear sensor
To solve this matter, a collaboration between the University of Groningen and Philips Consumer Lifestyle has been started in context of the Mantis consortium, with the goal to develop a tool wear sensor based on an optical image system. A robust setup with a high-resolution sensor will make detailed images of the individual tools.
The raw images are preprocessed, where the parts of interest of the tool will be cut out of the image and rotated to form the input for a machine learning algorithm. Next step would be to normalize the pictures so they are more or less comparable.
Since we have no baseline, we asked our maintenance engineers (they are the domain experts) to label all these individual images. Together we choose three specific labels: wear, damage and contamination. The input of the maintenance engineers is used to train the algorithm, but also to assess how well these individual pictures are labelled similar when considering multiple engineers.
Currently, over 1500 pictures are labelled in about a month. Initial results seem to indicate that simple machine learning can outperform human labeling regarding tooling deviations.
If results are good, the trained algorithm will ultimately be used with an automatic calculation engine to run new images through the algorithm. This means that we also have to change the way of work, and provide the maintenance engineers with easy-to-use tools to make these new images, as part of their regular maintenance steps. The outcome of the analysis forms an input for determining the remaining useful life of the tool, in combination with both process and quality data.