Machine learning for additive manufacturing of electronics

Quality of electronic products fabricated with additive manufacturing (AM) techniques such as 3D inkjet printing can be assured by adopting pro-active predictive models for process condition monitoring instead of using conventional post-manufacture assessment techniques. This paper details a model-based approach, and associated machine learning algorithms, which can be used to achieve and maintain optimal product quality during production runs and to realise model predictive process control (MPC). The investigated data-driven prognostics based on state-space modelling of the dynamic behaviour of 3D inkjet printing for electronics manufacturing is new and makes it an original contribution. 3D printing of conductive lines for electronic circuits is a main targeted application, and is used to demonstrate and validate the prognostics capability of machine learning models developed from measured process data. The results show that, for moderately non-linear dynamics of the 3D-Printing process, state-space models can inform on the expected process trends (states) and related product quality characteristics even over large prediction horizons. The models can also support the realisation of model predictive process control for optimal target performance

New Functionalities of a Virtual Computer Model Design and Construction

The purpose of this paper is to construct a virtual model of a computer system for student training, based on analysis of different platforms, creating virtual reality and its related 3D graphics. A classification of three-dimensional models was made. Software platforms for three-dimensional modelling and creation of virtual scenes have been analysed and selected. A virtual model has been developed

CVaR sensitivity with respect to tail thickness

We consider the sensitivity of conditional value-at-risk (CVaR) with respect to the tail index assuming regularly varying tails and exponential and faster-than-exponential tail decay for the return distribution. We compare it to the CVaR sensitivity with respect to the scale parameter for stable Paretian, the Student's t, and generalized Gaussian laws and discuss implications for the modeling of daily returns and marginal rebalancing decisions. Finally, we explore empirically the impact on the asymptotic variability of the CVaR estimator with daily returns which is a standard choice for the return frequency for risk estimation. --fat-tailed distributions,regularly varying tails,conditional value-at-risk,marginal rebalancing,asymptotic variability

Enabling social identity interaction : Bulgarian migrant entrepreneurs building embeddedness to a transnational network

Bulgarian migrant entrepreneurs (MEs) approaching diaspora networks (i.e. ethnic spaces in host countries) provides a unique context for exploring the processes by which peripheral actors achieve embeddedness. The study considers how in-group social norms and expectations influence out-group candidates’ network standing. The integration of the social identity perspective with embeddedness research allows identifying the sequence of intergroup actions and the circulation of identity signals between groups. Traditionally, the social identity perspective focuses on the act of constructing identity through positively stereotyping in-groups and negatively stereotyping out-groups. Nevertheless, an empirical study of 12 cases of Bulgarian MEs indicates that the circulation of identity signals that facilitate inter-group comparison can result in complementarity and brokerage. The study suggests the existence of a novel strategy (i.e., social circulation), to add to already known social identity strategies (i.e. social mobility, social creativity and social change). Contrary to previous ones, the new construct does not occur at the expense of either in-groups’ or out-groups’ identity. Thus, it adopts an integrative logic, currently missing from the social identity perspective

Data analytics approach for optimal qualification testing of electronic components

In electronics manufacturing, required quality of electronic components and parts is ensured through qualification testing using standards and user-defined requirements. The challenge for the industry is that product qualification testing is time-consuming and comes at a substantial cost. The work reported with this paper focus on the development and demonstration of a novel approach that can support “smart qualification testing” by using data analytics and data-driven prognostics modelling. Data analytics approach is developed and applied to historical qualification test datasets for an electronic module (Device under Test, DUT). The qualification spec involves a series of sequentially performed electrical and functional parameter tests on the DUTs. Data analytics is used to identify the tests that are sensitive to pending failure as well as to cross-evaluate the similarity in measurements between all tests, thus generating also knowledge on potentially redundant tests. The capability of data-driven prognostics modelling, using machine learning techniques and available historical qualification datasets, is also investigated. The results obtained from the study showed that predictive models developed from the identified so-called “sensitive to pending failure” tests feature superior performance compared with conventional, as measured, use of the test data. This work is both novel and original because at present, to the best knowledge of the authors, no similar predictive analytics methodology for qualification test time reduction (respectively cost reduction) is used in the electronics industry