Simulation data management for adaptive design of experiments: A litterature review

Abstract

-Recent evolutions of computer-aided product development and massive integration of numerical simulations to the design process require new methodologies to manage the continuously increasing flow of data and decrease the computational costs of numerical design of experiments. This paper presents a literature review of Simulation Data Management strategy and adaptive design of experiments methodology to detect possible links between these two fields and identify potential improvements for simulation process shortening. Adaptive design of experiments is based on several methods implying a profusion of different technics. Re-using best practices may help designers to choose relevant methods to reduce computational cost and simulation process duration.

Data management within new product development and collaborative engineering: a bibliometric and systemic analysis

“…CAD data have also been integrated into the product structure and assembly planning by Demoly et al (2013) and to design tools such as quality function deployment and failure mode and effects analysis by Torres et al (2010Torres et al ( , 2013. Simulation data management (SDM) is reviewed by Blondet et al (2015Blondet et al ( , 2016) and connected to the numerical design of experiment within the PDP. They contextualized their research problem by arguing that the amount of data and data heterogeneity are continually increasing with computers and network capabilities.…”

Optimising process and product performance in complex systems: a study in the automotive industry

“…The competitiveness and efficiency of industrial firms must be constantly improved to face market competition. Consequently, both processes and products must be continually optimised in terms of quality, cost and time (Colpani et al, 2020;Lukemire et al, 2020;Blondet et al, 2015). Customers demand highly reliable products and processes (Prashar, 2016a).…”

“…When properly implemented, DoE programs can offer excellent results compared to other techniques for systematically planning, conducting and analysing experiments as well as statistically improving both products and processes (Lundkvist et al, 2020;Peng and Lin, 2019). However, although data models support complex simulations of multiple domains integrated into software, DoE has not yet been sufficiently tested in the industrial routines (Prabhu et al, 2020;Blondet et al, 2015;Antony et al, 2004). Therefore, based on the knowledge gaps and research possibilities discussed above, the key research question of this study is: RQ.…”

Co-clustering the data of multivariate quality attributes for the estimation of mutual influence factors

“…Одно из важнейших условий развития любого современного производстваобеспечение постоянно высокого сертифицированного качества конечного продукта. Быстро меняющаяся конъюнктура рынка и усиление конкуренции объективно способствуют имплементации высокотехнологичных методов обеспечения качества на этапе разработки продукции [1][2][3] с дальнейшим менеджментом всего производственного процесса в соответствии с определёнными критическими атрибутами качества CQA (Critical Quality Attributes) [4].…”