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Data-informed inverse design by product usage information: a review, framework and outlook

DOI:10.1007/s10845-019-01463-2
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Liang Hou
Liang Hou
Liang Hou
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Roger J. Jiao
Roger J. Jiao
Roger J. Jiao
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Abstract and Figures

A significant body of knowledge exists on inverse problems and extensive research has been conducted on data-driven design in the past decade. This paper provides a comprehensive review of the state-of-the-art methods and practice reported in the literature dealing with many different aspects of data-informed inverse design. By reviewing the origins and common practice of inverse problems in engineering design, the paper presents a closed-loop decision framework of product usage data-informed inverse design. Specifically reviewed areas of focus include data-informed inverse requirement analysis by user generated content, data-informed inverse conceptual design for product innovation, data-informed inverse embodiment design for product families and product platforming, data-informed inverse analysis and optimization in detailed design, along with prevailing techniques for product usage data collection and analytics. The paper also discusses the challenges of data-informed inverse design and the prospects for future research.
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Journal of Intelligent Manufacturing (2020) 31:529–552
https://doi.org/10.1007/s10845-019-01463-2
Data-informed inverse design by product usage information: a review,
framework and outlook
Liang Hou1,2 ·Roger J. Jiao2
Received: 6 September 2018 / Accepted: 16 January 2019 / Published online: 23 January 2019
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
A significant body of knowledge exists on inverse problems and extensive research has been conducted on data-driven design
in the past decade. This paper provides a comprehensive review of the state-of-the-art methods and practice reported in
the literature dealing with many different aspects of data-informed inverse design. By reviewing the origins and common
practice of inverse problems in engineering design, the paper presents a closed-loop decision framework of product usage
data-informed inverse design. Specifically reviewed areas of focus include data-informed inverse requirement analysis by
user generated content, data-informed inverse conceptual design for product innovation, data-informed inverse embodiment
design for product families and product platforming, data-informed inverse analysis and optimization in detailed design,
along with prevailing techniques for product usage data collection and analytics. The paper also discusses the challenges of
data-informed inverse design and the prospects for future research.
Keywords Inverse design ·Product usage information ·Data-informed design ·Data analytics ·Cyber-physical systems
Introduction
With the emerging technologies of product-embedded sen-
sors and information devices, product operating data can be
captured remotely and continuously (Kiritsis et al. 2003;
Huang et al. 2017). The growing digitization of products
and smart sensing technologies covered under the umbrella
of Internet of Things and cyber-physical systems support
the possibility to collect an increasing amount of product
usage information to be accessible for product design teams
(Lützenberger et al. 2016; Kong et al. 2019; Thürer et al.
2019). E-commerce platforms and social media facilitate
companies’ access to the massive user generated data, which
empowers a data-driven approach to continuous design
improvement and next-generation product prediction (Porter
and Heppelmann 2015; Issa et al. 2017). Opresnik et al.
(2013) systematized this data-driven decision process as an
information feedback loop of collecting, storing and ana-
BRoger J. Jiao
rjiao@gatech.edu
1Department of Mechanical and Electrical Engineering,
Xiamen University, Xiamen 361005, China
2School of Mechanical Engineering, Georgia Institute of
Technology, Atlanta, GA 30332-0405, USA
lyzing data from customers and end-users of the products,
with the goal to discover new needs or identify changes in
usage patterns, and in turn to provide information about new
product offerings back to the customers. By exploiting huge,
versatile and highly contextualized product through-life data,
design engineers can harness their organization’s competitive
edge by uncovering patterns, novel insights, and knowl-
edge through data-driven design (Zhao et al. 2007;Lietal.
2019b). While data-driven design makes better informed
decisions possible for developing better products, enormous
and multiplex user- and product-generated data brings about
unprecedented challenges, alongside unmatched opportuni-
ties, for advancing the theory, methods, tools, and practice of
engineering design for products, systems, and services (Kim
et al. 2017; Hyun et al. 2017).
Product design typically entails a forward decision pro-
cess that is technically driven and focused on intrinsic
performances of the products (Yannou et al. 2013). Con-
ventionally a variety of design specifications and system
conditions are either acquired through hypothetical market
studies or assumed a priori based on anecdotal experience,
which inevitably involves subjective judgments and approx-
imation, leading to deviation from true customer satisfaction
(Zhang et al. 2017). Elicitation of customer requirements is
commonly achieved through interviews, focus groups, user
123
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