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

Blondet, Gaëtan; Duigou, Julien Le; Boudaoud, Nassim; Eynard, Benoît

doi:10.1051/meca/2015041

Cited by 11 publications

(5 citation statements)

References 60 publications

(103 reference statements)

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“…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.…”

Section: Systemic Analysismentioning

confidence: 99%

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

Larocca¹,

Borsato²,

Kubo³

et al. 2021

VJIKMS

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Purpose Although organizations have more data than ever at their disposal, actually deriving meaningful insights and actions from them is easier said than done. In this concern, the main objective of this study is to identify trends and research opportunities regarding data management within new product development (NPD) and collaborative engineering. Design/methodology/approach Bibliometric and systemic analyses have been carried out using the methodological procedure ProKnow-C, which provides a structured framework for the literature review. A bibliographic portfolio (BP) was consolidated with 33 papers that represent the state of art in the subject. Findings Most recent researches within the BP indicate new trends and paradigm shifts in this area of research, tackling subjects such as the internet of things, cloud computing, big data analytics and digital twin. Research gaps include the lack of data automation and the absence of a common architecture for systems integration. However, from a general perspective of the BP, the management of experimental data is suggested as a research opportunity for future works. Although many studies have tackled data and collaboration based on computer-aided technologies environments, no study examined the management of the measured data collected during the verification and validation stages of a product. Originality/value This work provides a fresh and relevant source of authors, journals and studies for researchers and practitioners interested in the domain of data management applied to NPD and collaborative engineering.

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“…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).…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Pacheco

Librelato²

2022

IJQRM

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PurposeThis study responds to calls from industry and the literature to cope with the enormous challenges faced by companies operating in competitive business sectors. The main objective of this paper is to investigate how managers can optimise product quality and process efficiency of complex systems.Design/methodology/approachIn this paper, a design of experiments (DoE) method was used to improve the development of complex products and manufacturing processes in the industry of automotive audio components. To identify the optimal combination that minimises quality problems occurring with subwoofer speakers in the marketing, this study proposed a full Factorial experiment 24 with three replications in a single block summarised in an analysis of the interaction among the factors.FindingsThe research findings revealed the factors and levels regarding both the product development and manufacturing processes that significantly impact the quality and reliability performance of the subwoofer speaker analysed. The findings from the article allowed the company to prioritise internal improvements to enhance product quality and process efficiency. Other automotive firms will benefit from the research findings obtained.Practical implicationsFrom a managerial perspective, this research presented the DoE methodology as a real opportunity to deal with the inherent complexity of the manufacturing process in the automotive audio components sector. This research assist managers with insights into how they can improve the quality performance in production systems and in the market.Originality/valueThis study is an original contribution to the advance of theory and empirical implementation of DoE in competitive industrial sectors.

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“…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].…”

Section: Introductionunclassified

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

Shtangey¹,

Tereshchenko²,

Tereshchenko³

2018

Bulletin of the National Technical University «KhPI» Series: New Solutions in Modern Technologies

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АННОТАЦИЯ В статье предлагается метод ко-кластеризации стохастических данных многомерных критических параметров процесса (CPPs) с целью оценки влияния обнаруженных факторов на многомерные атрибуты критического качества (CQAs) продукта на стадии первоначального проектирования процесса производства. Метод представляет новый подход к обеспечению качества продукта, который учитывает проблему ко-кластеризации массивов данных CPPs для определения каузальной связи с CQAs. Используется технология неметрического многомерного шкалирования (NMDS) для определения исходных параметров ко-кластеризации. Ключевые слова: качество через дизайн; критические атрибуты качества; ко-кластеризация; многомерный статистический анализ

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“…This dilemma leads to a continuous effort to develop meta-models(models of model, or surrogate models) so that knowledge accumulated in such high fidelity models can be used efficiently in design, optimization and control. There have already been many useful reviews and books in the development and application of meta model and design of computer experiments, a brief list is provided here 1,2,3,4,5,6,7,8 . However, to the best of our knowledge, there is no such review attempt specifically from the perspective of a chemical process system engineer.…”

Section: Introductionmentioning

confidence: 99%

Meta-modelling in chemical process system engineering

Kajero

Chen

Wong

et al. 2017

Journal of the Taiwan Institute of Chemical Engineers

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

Cited by 11 publications

References 60 publications

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

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

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

Meta-modelling in chemical process system engineering

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