Engineering optimization methods for new product development model consumer demand as a function of product attributes and price in order to identify designs that maximize expected profit. However, prior approaches have ignored the ability of competitors to react to a new product entrant; thus these methods can overestimate expected profit and select suboptimal designs that perform poorly in a competitive market. We propose an efficient approach to new product design accounting for competitor pricing reactions by imposing Nash and Stackelberg conditions as constraints, and we test the method on three product design case studies from the marketing and engineering design literature. We find that a Stackelberg leader strategy generates higher profit than a Nash strategy. Both strategies are superior to ignoring competitor reactions: In our case studies, ignoring price competition results in overestimation of profits by 12%–79%, and accounting for price competition increases realized profits by up to 3.4%. The efficiency, convergence stability, and ease of implementation of the proposed approach enables practical implementation for new product design problems in competitive markets.

In this paper, the four-point interpolatory subdivision scheme for curve generation is adapted to the interpolation of a set of positions of a cylindrical tool represented by dual quaternions. The resulting discrete model of the tool path lends itself naturally to an algorithm for computing the characteristic curve belonging to the boundary surface of the swept volume of a cylinder at each of the discrete positions. This approach to compute the discrete model of the swept surface of a motion is numerically robust and computationally efficient since it is based only on linear combinations. The results have applications in NC simulation and verification, robot path planning, and computer graphics.

Designers perform many tasks when developing new products and systems, and making decisions may be among the most important of these tasks. The trade space exploration process advocated in this work provides a visual and intuitive approach for formulating and solving single- and multi-objective optimization problems to support design decision-making. In this paper, we introduce an advanced sampling method to improve the performance of the visual steering commands that have been developed to explore and navigate the trade space. This method combines speciation and crowding operations used within the Differential Evolution (DE) algorithm to generate new samples near the region of interest. The accuracy and diversity of the resulting samples are compared against simple Monte Carlo sampling as well as the current implementation of the visual steering commands using a suite of test problems and an engineering application. The proposed method substantially increases the efficiency and effectiveness of the sampling process while maintaining diversity within the trade space.

Modern design methodologies have used Function Component Matrices in a variety of different ways in order to support various facets of an engineering design process. The mapping of functions to components can be used to model and capture the dependencies and relationships that exist. This process is accomplished by breaking down complicated functions into smaller, easier to understand functions. This decomposition allows engineers to get a better understanding for how a change in each component within a product will affect the overall operation of the product. Being able to recognize the impact of the propagation of a sub-function change will give designers a better understanding of the flexibility (or lack thereof) of choices they have when designing a product for customization. In turn they can be used to inform the consumer regarding the consequences their customization choices can have on the final product. This paper discusses how a Functional Component Matrix (FCM) can be used to assist in this process of product customization and understanding change propagation.

Market differentiation strategies must identify competitive advantages when offering a line of products varying in features, price, quality, and/or aesthetics. Although this concept is well-known, many companies still have difficulties positioning their own products within their own product lines and against competitors. Few approaches combine two or more facets to answer the product differentiation problem. In this study, two novel indices are proposed to audit shape and functional differentiation within a family of products. The shape index appraises the shape similarity between the products upon digitization, while the functional assessment is based on functions characteristics of the product. Customers’ perception data is obtained experimentally and compared to these indices to validate the result. Pairs of products are evaluated, and the average scores are considered as the indices for a product family. A case study illustrates the usage of these two indices and performance of these tools as well. This approach can be used during detailed studies as well as early stages of the design process to help validate product family positioning.

Process planning for parts made on CNC machines is usually performed in sequence and carried out without feeding back information about the fabrication process to the planning stage. Shifting planning capabilities to the machine level enables consideration of direct machine feedback. In this paper a method for feature decomposition and tool path planning for pocket milling on a 3 axis milling machine is presented. Based on standard computerized numerical control (CNC) technology, G-code is transferred to the fabrication machine whereat adaptation of processing parameters can take place within a feature. In order to enable a feedback loop from the processing to the planning stage, subfeature elements are defined. Interrelations between subsequent elements are identified and standardized with regard to the manufacturing needs to realize fully machined features. For each element a tool path is generated and translated into valid G-code. After fabrication of each subfeature element processing parameters can be improved. The presented method is implemented in a prototype software tool based on an open source CAD/CAM/CAE kernel and extends an existing open source CAM software framework.

Design Structure Matrices (DSM) and Domain Mapping Matrices (DMM) are commonly used to model and analyze the relationships within one domain (DSM) or between two domains (DMM). Being assembled into one larger square matrix, having DSMs on its diagonal and DMMs in all other fields, a so-called Multiple Domain Matrix (MDM) is formed. When relating two domains using a DMM, a problem arises when the nature of one individual relationship between the two domains is to be described. Usually, this is modeled by annotating each relationship with the additional information, much like comments in spreadsheet software. This, however, is yet impossible if the relationships should be in matrix notation to allow for algorithmic matrix analyses. Equally, this way, the annotations are not accessible as elements of another matrix, e.g. as DSM. This paper suggests a generic principle to solve the described problem in a way consistent with the matrix methodology. It proposes an approach using MDM and is thereby able to unambiguously provide the nature of each relationship between the elements of two domains. As a DSM is a mere case of a DMM having two identical domains, the approach proposed can equally be used to enrich the relationships within a DSM.

In a gradient-based design optimization, it is necessary to know sensitivities of the constraint with respect to the design variables. In a reliability-based design optimization (RBDO), the constraint is evaluated at the most probable point (MPP) and called the probabilistic constraint, thus it requires the sensitivities of the probabilistic constraints at MPP. This paper presents the rigorous analytic derivation of the sensitivities of the probabilistic constraint at MPP for both First Order Reliability Method (FORM)-based Performance Measure Approach (PMA) and Dimension Reduction Method (DRM)-based PMA. Numerical examples are used to demonstrate that the analytic sensitivities agree very well with the sensitivities obtained from the finite difference method (FDM). However, since the sensitivity calculation at the true DRM-based MPP requires the second-order derivatives and additional MPP search, the sensitivity derivation at the approximated DRM-based MPP, which does not require the second-order derivatives and additional MPP search to find the DRM-based MPP, is proposed in this paper. A convergence study illustrates that the sensitivity at the approximated DRM-based MPP converges to the sensitivity at the true DRM-based MPP as the design approaches the optimum design. Hence, the sensitivity at the approximated DRM-based MPP is proposed to be used for the DRM-based RBDO to enhance the efficiency of the optimization.

Researchers have paid relatively little attention to the fact that most of what is considered design is more like redesign than original design. Redesign activities are characterized by an attempt to leverage experience, knowledge, and the capital that a company has already invested into existing engineering systems. In this paper, a method for undertaking strategic redesign is proposed and explained. This method includes support for designers making decisions in redesign problems when there exist systems to be leveraged and multiple new systems to be created. In addition, strategy is introduced to the problem through the consideration that new systems may not be offered all at once, as is often assumed in product family design research. In this paper, the aim of the designer is assumed to be a creation, through redesign, of a series of new systems with desirable and distinct performance levels. In addition, a plan is required to involve as little redesign effort throughout the life of the family of systems as possible. The proposed approach is based upon the concepts of Constructal Theory and previous work to create methods for the design of mass customized families of systems. In addition, two metrics are developed to represent considerations unique to redesign as opposed to original design. These metrics for redesign effort and commonality value are utilized in the overall objective formulation for the proposed approach to redesign. Through a simple redesign scenario involving a family of universal motors, it is shown that the overall approach proposed can lead the designer towards promising redesign plans involving leveraging of existing systems, but that the constructal-inspired approach in and of itself has certain limitations when applied to redesign.

Most structural products have complex geometry to meet customer’s demand of high functionality. Since manufacturing those products in one piece is either impossible or uneconomical, most structural products are assemblies of components with simpler geometries. The conventional way to design structural assemblies is to design overall geometry first, and then decompose the geometry to determine the part boundary and joint locations. This two-step process, however, can lead to sub-optimal designs since the product geometry, even if optimized as one piece, would not be optimal after decomposition. This paper presents a method for synthesizing structural assemblies directly from the design specifications, without going through the two-step process. Given an extended design domain with boundary and loading conditions, the method simultaneously optimizes the topology and geometry of an entire structure and the location and configuration of joints, considering structural performance, manufacturability, and assembleability. As a relaxation of our previous work utilizing a beam-based ground structure [1], this paper presents a new formulation in a continuum design domain, which greatly enhances the ability to represent complex structural geometry observed in real-world products. A multi-objective genetic algorithm is used to obtain Pareto optimal solutions that exhibits trade-offs among stiffness, weight, manufacturability, and assembleability.

The use of vibration-based techniques in damage identification has recently received considerable attention in many engineering disciplines. While various damage indicators have been proposed in the literature, those relying only on changes in the natural frequencies are quite appealing since these quantities can conveniently be acquired. Nevertheless, the use of natural frequencies in damage identification is faced with many obstacles, including insensitivity and non-uniqueness issues. The aim of this paper is to develop a viable damage identification scheme based only on changes in the natural frequencies and to attempt to overcome the challenges typically encountered. The proposed methodology relies on building a Finite Element Model (FEM) of the structure under investigation. A modified Particle Swarm Optimization (PSO) algorithm is proposed to facilitate updating the FEM in accordance with experimentally-determined natural frequencies in order to predict the damage location and extent. The method is tested on beam structures and was shown to be an effective tool for damage identification.

Modern retailers now control in excess of 70% of many markets and thereby control the access manufacturers have to the end customer. Success of a new product design therefore depends upon acceptance of the product by the powerful retailer as well as the end customer. One prevalent approach to increasing both retailer and manufacturer revenues is to improve the attractiveness of a product offering by bundling related items together for one price. To be most effective, bundled products should be developed with an integrated design approach that seeks to achieve utility for the end customer as well as cost efficiencies through measures such as using common parts. We propose a bundled product design approach that endogenizes the profit maximizing prices set by the channel controlling (monopolist) retailer. The approach accounts for demand dependencies between the product categories and thus the impact of the bundle and cross-category competition on proposed engineering designs is known. Additionally, the approach simultaneously considers uncertainty in engineering design, competing manufacturer product attributes and customer preferences to ensure acceptable product profitability and market share under interval uncertainty. A bundled product design case study is presented for two complimentary power tools. Manufacturer profit and market share are optimized both deterministically and under uncertainty. We find that considering demand dependencies can create optimal bundle and individual product designs that increase profits for both retailer and manufacturer.

In this paper, a new design of a parallel manipulator is proposed for industrial applications, specifically for material surface finishing processes. Though most current parallel mechanisms have been based on the Stewart-Gough platform which has 6 degrees of freedom (DOF), the focus of this design is on a 3-DOF manipulator with one novel configuration. In order to benefit production, a parallel kinematic machine (PKM) capable of high speed industrial operations with high accuracy and rigidity is necessary. First, system modelling includes mobility study, inverse kinematic model, Jacobian matrix, singularity analysis and workspace calculation are conducted. Then, a CAD model is presented showing the optimum design features and detailed mechanics. Finally, finite element analysis is carried out for the device optimization.

The development process is a key aspect of ultimate product success. The front-end of the development activity is the foundation for building new products by first gathering customers’ needs, identifying the company’s goals, and assessing the competitive landscape. By doing so, this crucial activity directly impacts eventual development cost, which includes engineering resources, manufacturing, etc. In this paper, we study a specific design approach, namely, product family design, which allows companies to increase revenue by developing an entire family of products targeting different market segments while reducing lead-time and manufacturing costs. However, there is a significant amount of risk given the costs of developing complex shared architectures, and there are many examples from industry where product families have failed. Thus, the development stage is critical, and a well-structured development strategy can bring success while a poor one can cause significant problems during product launch, as recent case studies illustrate. In this platform-based study, we assess two drivers of this product family design: (1) a platform-driven strategy and (2) a product-driven strategy. Three facets are examined: the product, the company, and the competition. The goal is to recommend a planning framework to aid companies in selecting the right process considering their product, strategy, and environment.

Previous work developed a knowledge-based method named Design GuideLines Collaborative Framework (DGLs-CF), adopted during product redesign and process reconfiguration and aimed at dealing and matching with particular manufacturing and verification technologies, according to ISO GPS concepts. Aim of present work is improving the role of the ISO GPS within the DGLs-CF, not only to raise coherence in terms of concepts, but to gain some important internal and external improvements for the DGLs-CF. The main activity toward achieving this goal is the formalization of DGLs-CF product features by means of the ISO GPS features. The procedure is proposed and the consequences of its application on the elements of the DGLs-CF are evaluated. A case of study as an example of application is also presented. A significant improvement in the DGLs-CF is realized, both in terms of knowledge structure and organization, and in terms of the possibility of interfacing it with other methodologies, tools, and environments.

One of the methods design engineers use to achieve alignment between precision features of two mating parts is through locating or alignment features. The most common locating features are a pair of small holes and pins that would define the in-plane position and orientation of two parts with respect to each other. These locating features establish a datum frame for all other critical features of a part. Design engineers would be interested to know the worst-case and probabilistic value of misalignments resulting from their choices of tolerances that assure fit of the datum features as well as their choices of tolerances that locate the precision features with respect to such a datum. This paper uses two-dimensional tolerance analysis methods to formulate this alignment problem. Worst-case estimates as well as Monte Carlo simulation have been used to analyze the misalignments resulting from play in the datum fit. It is shown that a number of dimensionless parameters can characterize the misalignment and these are used to create graphs adequate for design work.

A multiscale design approach is proposed in this paper considering the impacts of product manufacturing process and material on product performance. A framework is established to integrate designs of manufacturing process, material and product based on the information flow across these three domains. Random field is employed to realistically model the uncertainty existing in material microstructure which spatially varies in a product inherited from the manufacturing process. An efficient procedure for uncertainty propagation from the material random field to the end product performance is established. To reduce the dimensionality of random field representation, a reduced order Karhunen-Loeve expansion is used with a discretization scheme applied to finite element meshes. The univariate dimension reduction method and the Gaussian quadrature formula are used to efficiently quantify the uncertainties in product performance in terms of its statistical moments, which are critical information for design under uncertainty. A control arm example is used to demonstrate the proposed approach. The impact of the initial microscale porosity random field produced during a casting process on the product damage is studied and a reliability-based design of the control arm is performed.

Designers are continuously challenged to manage complexity in embodiment design processes (EDPs), in the context of integrated product and materials design. In order to manage complexity in design processes, a systematic strategy to embodiment design process generation and selection is presented in this paper. The strategy is based on a value-of-information-based Process Performance Indicator (PPI). The approach is particularly well-suited for integrated product and materials design, and all other scenarios where knowledge of a truthful, i.e., perfect, design process and bounds of error are not available in the entire design space. The proposed strategy is applied to designing embodiment design processes for photonic crystal waveguides in the context of a next-generation optoelectronic communication system. In this paper, it is shown that the proposed strategy based on the Process Performance Indicator is useful for evaluating the performance of embodiment design processes particularly when accuracy of the prediction or the associated error bounds are not known.

In literature, design structure matrix (DSM), which is a square matrix, has been widely used to address single-domain dependency relationships (e.g., product architecture, process workflow, and organization structure). To extend the DSM efforts, a rectangular matrix becomes a logical format to capture and analyze cross-domain dependency relationships, namely, domain mapping matrix (DMM) [1]. In this context, this paper proposes a unified framework for decomposition of DSM and DMM. The unified framework consists of four methodological phases to offer the functions of DSM clustering, DSM sequencing, and DMM decomposition. To support the development of this framework, various decomposition-related techniques from applied mathematics and engineering design are reviewed. Three matrix examples have been used to illustrate the framework’s applicability.

Providing nano engineers and scientists efficient and easy-to-use tools to create geometry conformations that have minimum energies is highly desirable in material design. Recently we developed a periodic surface model to assist the construction of nano structures parametrically for computeraided nano-design. In this paper, we present a feature-based approach for crystal construction. The proposed approach starts to create models of basic features by the aide of periodic surfaces followed by operations between basic features. The goal is to introduce a rapid construction method for complex crystal structures.