Abstract
The many tunable parameters involved in laser processing, such as wavelength, pulse duration, pulse energy, and scan speed, not to mention various other complicating factors on the material side, makes it practically impossible to reliably find an optimized parameter set to realize a specific processing target. Currently, an acceptable parameter set is mainly found by tapping the experience and intuition of skilled people within the present production system. However, such methods do not scale to the mass-customization needs of the coming super-smart society, and it has become critical to develop ways to transfer such human experience and intuition to a more scalable setting: namely, the cyber-space. A major challenge in developing a cyber-space solution has been augmenting the limited experimental and theoretical insights of the laser processing phenomenon to the specific problems at hand. Here, we focus on automated data acquisition systems coupled with artificial intelligence (AI) methods to overcome this technological gap. We propose ways to realize cyber-physical systems specializing in specific facets of laser production by showing experimental results from four kinds of automated data acquisition systems. We lastly discuss such methods in context as an important first step to creating an AI based cyber-physical simulator.
Highlights
T HE WORLDWIDE market for laser manufacturing is growing at approximately twice the rate of the conventional machining market
Personalized orders are sent to a main server in cyber space, which chooses which machines, and what processes/parameters should be applied for each step in the production chain
We demonstrate automated, high-quality data acquisition systems for laser processing, where we show four kinds of different monitoring methods focusing on different aspects of laser processing
Summary
T HE WORLDWIDE market for laser manufacturing is growing at approximately twice the rate of the conventional machining market. There are too many combinations of processing parameters, such as wavelength, pulse duration, repetition rate, average power, fluence, intensity, scanning speed, and scanning pattern, to allow for an exhaustive study of all their effects This is further complicated by the fact that an appropriate parameter set is dependent on the specifics of the material. A promising approach to overcome this hurdle and allow for the effective scaling of laser manufacturing to mass-customized production is to transfer the experience and intuition of such craftsmen to cyber space. A sufficient number of data points, typically in the range of a few thousand to a few tens of thousands, are required to allow for AI approaches to derive non-data-specific conclusions We note that such dataset characteristics have traditionally been difficult to realize with laser processing. We discuss how such systems will be critical for creating a cyber-physical system for laser manufacturing
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