Jitesh H. Panchal

Key computational modeling issues in Integrated Computational Materials Engineering

Designing materials for targeted performance requirements as required in Integrated Computational Materials Engineering (ICME) demands a combined strategy of bottom-up and top-down modeling and simulation which treats various levels of hierarchical material structure as a mathematical representation, with infusion of systems engineering and informatics to deal with differing model degrees of freedom and uncertainty. Moreover, with time, the classical materials selection approach is becoming generalized to address concurrent design of microstructure or mesostructure to satisfy product-level performance requirements. Computational materials science and multiscale mechanics models play key roles in evaluating performance metrics necessary to support materials design. The interplay of systems-based design of materials with multiscale modeling methodologies is at the core of materials design. In high performance alloys and composite materials, maximum performance is often achieved within a relatively narrow window of process path and resulting microstructures. Much of the attention to ICME in the materials community has focused on the role of generating and representing data, including methods for characterization and digital representation of microstructure, as well as databases and model integration. On the other hand, the computational mechanics of materials and multidisciplinary design optimization communities are grappling with many fundamental issues related to stochasticity of processes and uncertainty of data, models, and multiscale modeling chains in decision-based design. This paper explores computational and information aspects of design of materials with hierarchical microstructures and identifies key underdeveloped elements essential to supporting ICME. One of the messages of this overview paper is that ICME is not simply an assemblage of existing tools, for such tools do not have natural interfaces to material structure nor are they framed in a way that quantifies sources of uncertainty and manages uncertainty in representing physical phenomena to support decision-based design.

Product Realization in the Age of Mass Collaboration

There has been a recent emergence of communities working together in large numbers to develop new products, services, and systems. Collaboration at such scales, referred to as mass collaboration , has resulted in various robust products including Linux and Wikipedia. Companies are also beginning to utilize the power of mass collaboration to foster innovation at various levels. Business models based on mass collaboration are also emerging. Such an environment of mass collaboration brings about significant opportunities and challenges for designing next generation products. The objectives in this paper are to discuss these recent developments in the context of engineering design and to identify new research challenges. The recent trends in mass collaboration are discussed and the impacts of these trends on product realization processes are presented. Traditional collaborative product realization is distinguished from mass collaborative product realization. Finally, the open research issues for successful implementation of mass collaborative product realization are discussed.

A Framework for Integrated Design of Mechatronic Systems

Mechatronic systems encompass a wide range of disciplines and hence are collaborative in nature. Currently, the collaborative development of mechatronic systems is inefficient and error-prone because contemporary design environments do not allow sufficient flow of design and manufacturing information across electrical and mechanical domains. Mechatronic systems need to be designed in an integrated fashion allowing designers from both electrical and mechanical engineering domains to receive automated feedback regarding design modifications throughout the design process. Integrated design of mechatronic systems can be facilitated through the integration of mechanical and electrical computer-aided design (CAD) systems. One approach to achieve such integration is through the propagation of constraints. Cross-disciplinary constraints between mechanical and electrical design domains can be classified, represented, modelled, and bi-directionally propagated in order to provide automated feedback to designers of both engineering domains. In this chapter, the authors focus on constraint classification and constraint modelling and provide an example by means of a robot arm. The constraint modelling approach serves as a preliminary concept for the implementation of constraint propagation between mechanical and electrical CAD systems.

Plasticity-Related Microstructure-Property Relations for Materials Design

Design has traditionally involved selecting a suitable material for a given application. A materials design revolution is underway in which the classical materials selection approach is replaced by design of material microstructure or mesostructure to achieve certain performance requirements such as density, strength, ductility, conductivity, and so on. Often these multiple performance requirements are in conflict in terms of their demands on microstructure. Computational plasticity models play a key role in evaluating structure-property relations necessary to support simulation-based design of heterogeneous, multifunctional metals and alloys. We consider issues related to systems design of several classes of heterogeneous material systems that is robust against various sources of uncertainty. Randomness of microstructure is one such source, as is model idealization error and uncertainty of model parameters. An example is given for design of a four-phase reactive powder metal-metal oxide mixture for initiation of exothermic reactions under shock wave loading. Material attributes (e.g. volume fraction of phases) are designed to be robust against uncertainty due to random variation of microstructure. We close with some challenges to modeling of plasticity in support of design of deformation and damage-resistant microstructures.

TOWARDS A STANDARDIZED ENGINEERING FRAMEWORK FOR DISTRIBUTED, COLLABORATIVE PRODUCT REALIZATION

In this paper, we propose a standardized computer-based engineering framework to support distributed product realization. The requirements for a standardized distributed product realization framework are developed based on the Open Engineering Systems paradigm. Existing computer frameworks are evaluated against the requirements and the missing features are identified. Our efforts towards development of such a framework – eXtensible Distributed Product Realization (X-DPR) environment are discussed. X-DPR is flexible and applicable to general industrial product realization processes. It is used to integrate distributed, collaborative product realization activities over the Internet. We trace the development of the framework based on design requirements. Features of X-DPR are implemented to satisfy each requirement. X-DPR is compared to existing engineering frameworks based on the required features. Limitations and future work are presented.

Agent-Based Modeling of Mass-Collaborative Product Development Processes

Mass-collaborative product development refers to a paradigm where large groups of people compete and collaborate globally to develop new products and services. In contrast to the traditional top-down decomposition-based design processes, the primary mechanism in mass-collaborative product development is bottom-up evolution. Hence, the issues underlying mass-collaborative processes are fundamentally different from those in traditional design processes. For example, instead of determining the best sequence in which activities should be carried out, the emphasis is on developing the right conditions under which product evolution can be fostered. Existing research on product development is primarily focused on top-down design processes. The evolutionary nature of mass-collaborative product development has received very little attention. Specifically, computational models for these processes have not been developed. In this paper, a step toward understanding the fundamental processes underlying mass-collaborative product development using a computational model is presented. The model presented in this paper is based on an agent-based modeling approach, which allows the modeling of the behavior of different entities within a product development scenario and the study of the effect of their interactions. The model captures the information about (i) products as modules and their interdependencies, and (ii) the participants involved and their strategies. The benefits of the agent-based model in understanding mass-collaborative product development are shown using a simple product model. The following aspects of the product development processes are studied: (a) the rate of evolution of the individual modules and the entire product, (b) product evolution patterns and the effect of the number of participants, (c) the effect of prior work on product evolution, (d) the evolution and distribution of participants, and (e) the effect of participant incentives. The agent-based modeling approach is shown as a promising approach for understanding the evolutionary nature of mass-collaborative product development processes.

A value-of-information based approach to simulation model refinement

Abstract The appropriateness of a simulation model for engineering design is dependent on the trade-off between model accuracy and the computational expense for its development and execution. Since no simulation model is perfect, any simulation model for a systems physical behaviour can be refined further, although likely at an increased computational cost. Hence, the question faced by a designer is ‘How much refinement of a simulation model is appropriate for a particular design problem?’ The simplified nature of simulation models results in two types of uncertainty—variability, which can be modelled using probability distribution functions and imprecision, best modelled using intervals. Value-of-information has been used in the engineering design literature to decide whether to make a decision using the available information or to gather more information before making a decision. However, the main drawback of applying existing value-of-information based metrics for model refinement problems is that existing metrics only account for variability; they do not account for imprecision in simulation models and the impact of its reduction on design decisions. To overcome the limitation of existing metrics in the context of model refinement, this article presents a value-of-information based approach for determining the appropriate extent of refinement of simulation models. The approach consists of (i) a metric called improvement potential for quantifying the value-of-information obtained via refinement of simulation models and (ii) a method in which this metric is utilized for supporting model refinement decisions. The improvement potential measures the value-of-information by considering both imprecision and variability in simplified models. It quantifies the maximum possible improvement in a designers decision that can be achieved by refining a simulation model. Specifically, we focus on multi-objective compromise decisions modelled using the compromise decision support problem construct, which is a hybrid formulation based on traditional optimization and goal programming. The method involves starting from a simple simulation model and gradually refining it until the value of further refinement on design decisions is small. The approach is presented using two examples—design of a pressure vessel and design of a multi-functional material. The pressure vessel problem is used to illustrate the benefits of using this approach shown by gradually refining its material parameters; the materials design problem is a comprehensive problem where a complex finite element model is gradually refined. The approach proposed in this article can be utilized by designers and analysts in developing effective simulation models for specific design problems while efficiently utilizing their model development resources.

DESIGNING DESIGN PROCESSES IN PRODUCT LIFECYCLE MANAGEMENT: RESEARCH ISSUES AND STRATEGIES

Product Lifecycle Management (PLM) promises to further a holistic consideration of product design, emphasizing integration, interoperability, and sustainability throughout a product’s lifecycle. Thus far, efforts have focused on addressing lifecycle concerns from a product-centric perspective by exploiting the reusability and scalability of existing products through product platform and product family design. Not much attention has been paid to leveraging the design process and its design in addressing lifecycle considerations, however. In striving for sustainability, it is the design process that should be considered to constitute an engineering enterprise’s primary resource commitment. In this paper, an overview of the challenges inherent in designing design processes is provided. These challenges are subsequently illustrated with regard to several design scenarios of varying complexity, using an example involving the design of Linear Cellular Alloys. A distinction is made between product related requirements/goals and design process related requirements/goals. Requirements, research issues, and strategies for addressing the diverse needs of modeling design processes from a decision-centric perspective are established. Finally, key elements for enabling the integrated design of products and their underlying design processes in a systematic fashion are provided, motivating the extension of PLM to include the lifecycle considerations of design processes, thereby moving towards Design Process Lifecycle Management (DPLM).

Reusable Design Processes via Modular, Executable, Decision-Centric Templates

Abstract : While there have been many advances with respect to reusability and scalability of product architectures over the past several decades, little progress has been made in applying the same concepts to underlying design processes. It is on this aspect of design process design that we focus in this paper. Design processes play a key role in product design and their configuration has a significant effect on both the efficiency and the effectiveness with which resources are committed. Design processes also directly influence the final design of the product under consideration. As such, more attention must be paid to the manner in which these processes are modeled so that they may be standardized, executed, analyzed, and stored, allowing for their leveraging across product lines and reducing product development times. Computer interpretability is a key consideration in making required adjustments as product considerations evolve and design requirements change from one product to the next. In this paper, we offer a fundamental step in this direction by presenting a method for modeling design processes as reusable process templates that can be captured, archived, analyzed and manipulated on a computer.

Managing Design-Process Complexity: A Value-of-Information Based Approach for Scale and Decision Decoupling

Design processes for multiscale, multifunctional systems are inherently complex due to the interactions between scales, functional requirements, and the resulting design decisions. While complex design processes that consider all interactions lead to better designs; simpler design processes where some interactions are ignored are faster and resource efficient. In order to determine the right level of simplification of design processes, designers are faced with the following questions: a) how should complex design-processes be simplified without affecting the resulting product performance? and b) how can designers quantify and evaluate the appropriateness of different design process alternatives? In this paper, the first question is addressed by introducing a method for determining the appropriate level of simplification of design processes — specifically through decoupling of scales and decisions in a multiscale problem. The method is based on three constructs: interaction patterns to model design processes, intervals to model uncertainty resulting from decoupling of scales and decisions, and value of information based metrics to measure the impact of simplification on the final design outcome. The second question is addressed by introducing a value-of-information based metric called improvement potential for quantifying the appropriateness of design process alternatives from the standpoint of product design requirements. The metric embodies quantitatively the potential for improvement in the achievement of product requirements by adding more information for design decision making. The method is illustrated via a datacenter cooling system design example.Copyright