Understanding Behaviors In Different Domains: The Role Of Machine Learning Techniques And Network Science

Recent developments in the Internet of Things (IoT), social media, and the data sciences have resulted in larger volumes of data than ever before, offering more opportunity for observing and understanding behaviors. Advances in data analytic and machine learning techniques have also enabled assessments to be more multi-faceted, incorporating data from more sources. Machine learning algorithms such as Decision Trees and Random Forests, K-nearest neighbors, and Artificial Neural Networks have been used to uncover hidden patterns in data and derive predictions and recommendations from a wide range of data types and sources. However, these do not necessarily yield insights into behaviors in complex systems/domains. Methods from mathematics such as Set Theory, Graph Theory, and Network Science may be useful in shedding light on the interactions and relationships within and across domains. This paper provides a description of the applications, strengths, and limitations of some of these techniques and methods

Automated and Predictive Risk Assessment in Modern Manufacturing Based on Machine Learning

The revision of the ISO 9001 in 2015 and the end of the transition period in 2018 forces companies to integrate risk management into their company structure. Risk assessment as part of risk management poses challenges for many companies, especially SMEs. Existing methods are often complex, subjective and difficult to automate. To address this issue, this paper describes a risk assessment approach that can be fully automated after expert process evaluation. The automated risk assessment is based on a Machine Learning algorithm, which builds a model that predicts the output and allows the use of SPC control charts without measuring components characteristics. Based on the results of the control charts, the risk can be assessed by calculating the distances to critical values and analyzing th e control chart (e.g. run or trend identification). The use of process parameters, which are recorded by sensors, makes it possible to intervene in the process in high risk situations and reduce not only measurements but also the production of scrap. The method was applied to the use case of an injection molding process of a thin-walled thermoplastic. Based on a Design of Experiments the model was built by a Generalized Linear Regression machine learning algorithm. A predictive validation and an event validation test were used to validate the method. A two-sided t-test at a significance level of α = 5% provided equality between predicted and actual mean value. The Event Validation Test provided a 90100% correct classification