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Abstract
In this work, a new approach for fault diagnosis in the field of additive manufacturing (3d printing) using artificial intelligence will be given. This approach is based on the marriage of the Bayesian Networks theory and data acquisition techniques. Bayesian Networks are well known for their ability to infer probabilities and to give decisional support under uncertainty. In order to do so, these probability engines must be constructed and maintained by a big amount of data and information using learning algorithms. This work provides a methodology that uses sensors based data acquisition and processing to construct such networks. Some of these sensors are already available in most of the 3d printers available in the market, while other sensors were additionally embedded in a studied 3d printer in order to enrich the number of observational variables to gain a high level of fault diagnosis accuracy and support.
Highlights
The increasing complexity and pervasive uncertainty of large-scale industrial systems have dramatically increased their vulnerability to failures
Right next to the resistance bridge we find the temperature acquisition block for DS18B20 digital sensors [26]
An unsupervised discretization method based on the works of Cooper [13] et al and Friedman et al [27] was used in order to make possible the learning of the Bayesian Networks (BN) structure with continuous data collected from analog sensors
Summary
The increasing complexity and pervasive uncertainty of large-scale industrial systems have dramatically increased their vulnerability to failures. For some critical systems such as nuclear power plants, space vehicles, and chemical plants, considerable efforts have been made to design better equipment and control systems, fault and failure management remains largely dependent on human operators. These humans are "supposed to" respond accurately to the location of the origins of the failures and take prompt and appropriate corrective action in an emergency. Decision-makers face a variety of uncertainty issues that can arise from measurement bias, process noise, transmission loss, unmeasured exogenous influence, and so on To solve these problems, automatic fault diagnosis is helpful in helping technicians to detect, isolate and troubleshoot problems. The main idea of the paper is: how to use the already existent sensors in a 3d printer (such as thermistors, voltage sensors, A/D converters, stepper driver signals, etc.) and implement some new others (such as Hall Effect current sensors, voltage, temperature, etc.) in order to generate some datasets that will be useful to elaborate Bayesian networks using learning algorithms
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