0

IN THIS ISSUE

Newest Issue


Research Papers

J. Comput. Inf. Sci. Eng. 2019;19(3):031001-031001-10. doi:10.1115/1.4042913.

A challenge systems engineers and designers face when applying system failure risk assessment methods such as probabilistic risk assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system testbed is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system's heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031002-031002-9. doi:10.1115/1.4042961.

A modular product architecture is a strategic means to deliver external variety and internal commonality. In this paper, we propose a new clustering-based method for product modularization that integrates product complexity and company business strategies. The proposed method is logically verified by a studied industrial case, where the architecture of a heavy truck driveline is analyzed in terms of how it has evolved over a couple of decades, due to changed business strategies and the evolution of new technology. The presented case indicates that the new methodology is capable of identifying and proposing reasonable module candidates that address product complexity as well as company-specific strategies. Furthermore, the case study clearly shows that the business strategic reasons for a specific architecture can be found by analyzing how sensitive the clusters are to changes in the module drivers (MD).

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031003-031003-11. doi:10.1115/1.4042537.

The real-life use of a product is often hard to foresee during its development. Fortunately, today's connective products offer the opportunity to collect information about user actions, which enables companies to investigate the actual use for the benefit of next-generation products. A promising application opportunity is to input the information to engineering simulations and increase their realism to (i) reveal how use-related phenomena influence product performance and (ii) to evaluate design variations on how they succeed in coping with real users and their behaviors. In this article, we explore time-stamped usage data from connected fridge-freezers by investigating energy losses caused by door openings and by evaluating control-related design variations aimed at mitigating these effects. By using a fast-executing simulation setup, we could simulate much faster than real time and investigate usage over a longer time. We showed that a simple, single-cycle load pattern based on aggregated input data can be simulated even faster but only produce rough estimates of the outcomes. Our model was devised to explore application potential rather than producing the most accurate predictions. Subject to this reservation, our outcomes indicate that door openings do not affect energy consumption as much as some literature suggests. Through what-if studies we could evaluate three design variations and nevertheless point out that particular solution elements resulted in more energy-efficient ways of dealing with door openings. Based on our findings, we discuss possible impacts on product design practice for companies seeking to collect and exploit usage data from connected products in combination with simulations.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031004-031004-10. doi:10.1115/1.4042695.

Designers need a way to overcome information-related risks, including information leakage and misuse by their own collaborators during collaborative product realization. Existing cryptographic techniques aimed at overcoming these information-related risks are computationally expensive and impractical even for moderate problem sizes, and legal approaches such as nondisclosure agreements are not effective. The computational practicality problem is particularly pronounced for computational techniques, such as the finite element analysis (FEA). In this paper, we propose a technique that enables designers to perform simulations, such as FEA computations, without the need for revealing their information to anyone, including their design collaborators. We present a new approach, the secure finite element analysis approach, which enables designers to perform FEA without having to reveal structural/material information to their counterparts even though the computed answer depends on all the collaborators' confidential information. We build secure finite element analysis (sFEA) using computationally efficient protocols implementing a secure codesign (SCD) framework. One of our findings is that the direct implementation of using SCD framework (termed as naïve sFEA) suffers from lack of scalability. To overcome these limitations, we propose hybrid sFEA that implements performance improvement strategies. We document and discuss the experiments we conducted to determine the computational overhead imposed by both naïve and hybrid sFEA. The results indicate that the computational burden imposed by hybrid sFEA makes it challenging for large-scale FEA—our scheme significantly increases the problem sizes that can be handled when compared to implementations using previous algorithms and protocols, but large enough problem sizes will swamp our scheme as well (in some sense this is unavoidable because of the cubic nature of the FEA time complexity).

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031005-031005-10. doi:10.1115/1.4042697.

Detection of potential failures and human error and their propagation over time at an early design stage will help prevent system failures and adverse accidents. Hence, there is a need for a failure analysis technique that will assess potential functional/component failures, human errors, and how they propagate to affect the system overall. Prior work has introduced functional failure identification and propagation (FFIP), which considers both human error and mechanical failures and their propagation at a system level at early design stages. However, it fails to consider the specific human actions (expected or unexpected) that contributed toward the human error. In this paper, we propose a method to expand FFIP to include human action/error propagation during failure analysis so a designer can address the human errors using human factors engineering principals at early design stages. The capabilities of the proposed method is presented via a hold-up tank example, and the results are coupled with digital human modeling to demonstrate how designers can use these tools to make better design decisions before any design commitments are made.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031006-031006-12. doi:10.1115/1.4042553.

Information is transferred through a process consisting of an information source, a transmitter, a channel, a receiver, and its destination. Unfortunately, during the engineering design process, there is a risk of a design idea or solution being incorrectly transferred and interpreted due to the nonlinearity of the process, and many ways to communicate and disseminate ideas or solutions. The objective of this work is to explore the amount of relevant design information transmitted by different idea dissemination methods and how the receiver's familiarity with the idea impacts the effectiveness of the methods. First, this work explores the advantages and disadvantages of different dissemination methods in engineering design. Next, an experiment is conducted with engineering and nonengineering participants in order to quantify the information transmitted by different idea dissemination methods. This work also quantifies the effect that receivers' familiarity with a design artifact has on the amount of information transmitted by different dissemination methods. Finally, the results obtained from the experiments are compared with a previous theoretical model for validation. The results indicate that while certain methods are perceived as more informative and are able to convey more information than others (e.g., linguistic textual description versus virtual three-dimensional (3D) models), the effectiveness of the methods depends on a receiver's familiarity with the ideas being transmitted. Knowledge gained from this work can aid designers in selecting a suitable dissemination method needed to effectively communicate ideas and achieve a design solution.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031007-031007-11. doi:10.1115/1.4042918.

This paper presents a numerical model able to control the temperature distribution along a 4340 steel cylinder heat-treated with laser. The numerical model developed using the numerical finite element method (FEM) was based on a study of surface temperature variation and the adjustment of this temperature by a control of the heat treatment laser power. The proposed analytical approach was built gradually by (i) the development of a numerical model of laser heat treatment of the cylindrical workpiece, (ii) an analysis of the results of simulations and experimental tests, (iii) development of a laser power adjustment approach, and (iv) proposal of a laser power control predictor using neural networks. This approach was made possible by highlighting the influence of the fixed (nonvariable) parameters of the laser heat treatment on the case depth and has shown that it is possible by controlling the laser parameters to homogenize the distribution of the maximum temperature reached on the surface for a uniform case depth. The feasibility and effectiveness of the proposed approach lead to a reliable and accurate model able to guarantee a uniform surface temperature and a regular case depth for a cylindrical workpiece of a length of 50 mm and with a diameter of between 16 and 22 mm.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031008-031008-12. doi:10.1115/1.4042300.

Product platforms allow companies to compete in the global marketplace by facilitating product variety and by adding, removing, or substituting components and features across a product family, while reducing costs and lead times. In many cases, developing a common platform involves determining which components are in a product family, their connections, and their spatial layouts. The development of product configurations and layouts is a complex problem and involves both discrete and continuous mathematical processes. This paper presents algorithms and an implementation to address the problem of configuring products and component layouts. The algorithms will describe the processes used to generate the product configurations based on constraints on combinations and the layout of components within the products. The implementation presents software developed to present the algorithms for the configuration and layout processes.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031009-031009-8. doi:10.1115/1.4042105.

Additive manufacturing (AM) enables the fabrication of objects using successive additions of mass and energy. In this paper, we explore the use of analytic solutions to model the thermal aspects of AM, in an effort to achieve high computational performance and enable “in the loop” use for feedback control of AM processes. It is shown that the utility of existing analytical solutions is limited due to their underlying assumption of a homogeneous semi-infinite domain. These solutions must, therefore, be enriched from their exact form in order to capture the relevant thermal physics associated with AM processes. Such enrichments include the handling of strong nonlinear variations in material properties, finite nonconvex solution domains, behavior of heat sources very near boundaries, and mass accretion coupled to the thermal problem. The enriched analytic solution method (EASM) is shown to produce results equivalent to those of numerical methods, which require six orders of magnitude greater computational effort. It is also shown that the EASM's computational performance is sufficient to enable AM process feedback control.

Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031010-031010-10. doi:10.1115/1.4042639.

This paper analyzes participation behaviors in design crowdsourcing by modeling interactions between participants and design contests as a bipartite network. Such a network consists of two types of nodes, participant nodes and design contest nodes, and the links indicating participation decisions. The exponential random graph models (ERGMs) are utilized to test the interdependence between participants' decisions. ERGMs enable the utilization of different network configurations (e.g., stars and triangles) to characterize different forms of dependencies and to identify the factors that influence the link formation. A case study of an online design crowdsourcing platform is carried out. Our results indicate that designer, contest, incentive, and factors of dependent relations have significant effects on participation in online contests. The results reveal some unique features about the effects of incentives, e.g., the fraction of total prize allocated to the first prize negatively influences participation. Further, we observe that the contest popularity modeled by the alternating k-star network statistic has a significant influence on participation, whereas associations between participants modeled by the alternating two-path network statistic do not. These insights are useful to system designers for initiating effective crowdsourcing mechanisms to support product design and development. The approach is validated by applying the estimated ERGMs to predict participants' decisions and comparing with their actual decisions.

Topics: Design , Modeling
Commentary by Dr. Valentin Fuster
J. Comput. Inf. Sci. Eng. 2019;19(3):031011-031011-13. doi:10.1115/1.4043013.

Additive manufacturing (AM) technologies have been widely used to fabricate three-dimensional (3D) objects quickly and cost-effectively. However, building parts consisting of complex geometries with curvatures can be a challenging process for the traditional AM system whose capability is restricted to planar layered printing. Using six degrees-of-freedom (DOF) industrial robots for AM overcomes this limitation by allowing the material deposition to take place on nonplanar surfaces. In this paper, we present trajectory planning algorithms for 3D printing using nonplanar material deposition. Trajectory parameters are selected to avoid collision with printing surface and satisfy robot constraints. We have implemented our approach by using a 6DOF robot arm. The complex 3D structures with various curvatures were successfully fabricated with a good surface finish.

Commentary by Dr. Valentin Fuster

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In