J. Mech. Des 139(12), 121101; doi: 10.1115/1.4037627
Inayat Ullah, Dunbing Tang, Qi Wang and Leilei Yin
J. Mech. Des 139(12), 121101; doi: 10.1115/1.4037627
Delivering a variety of products with minimal lead time is a critical issue given today’s competitive manufacturing industry. Many design and production firms address the challenges of variety by adopting a product family manufacturing strategy. Product families are a broad range of artifacts, known as product variants, which share a number of common components. Thus, engineering changes in a product family affect the product under consideration and other product variants in the family. This increases the difficulty of predicting the change propagation within a family of products. This paper introduces a seven-step change propagation approach that predicts and evaluates the impact of change propagation across product variants. Interdependencies and logical relationships between directly connected components are captured using a Component-based Design Structure Matrix. This highlights the different change propagation paths that are available in the product’s structure. Risk analysis in terms of lead time is performed at the component level. The results demonstrate that avoiding project delays requires selecting suitable change propagation paths in a family of products.
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Dipanjan Ghosh, Andrew Olewnik, Kemper Lewis, Junghan Kim and Arun Lakshmanan
J. Mech. Des 139(9), 091401 (Jul 12, 2017); doi: 10.1115/1.4036780
Understanding consumer perceptions of products and the potential impact of those perceptions on purchase decisions is critical information that should influence product development decisions. Though firms often seek consumer feedback on products, such feedback often occurs long after product use and lacks specific details about the interaction, usage context, etc. This work introduces a novel framework – Cyber-Empathic Design – that integrates sensor data and real-time user feedback to develop a more accurate model of user perceptions. The framework is applied to a case study focused on user perceptions of shoes. The results of this work demonstrate the potential for product developers to leverage the IoT (internet-of-things) movement, real-time user feedback, and advances in machine learning to connect user perceptions to specific engineered product features.
Figure: Data collection method (left) and resulting perceptual model (right).
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Development and Evaluation of a Mechanical Stance-Controlled Orthotic Knee Joint With Stance Flexion
Jan Andrysek; Matthew J. Leineweber; Hankyu Lee
J. Mech. Des. 2017; 139(3):035001-035001-7
People with severe impairment of the lower body caused by conditions such as polio or stroke often rely on assistive devices for mobility. Knee orthosis plays an important role in restoring mobility by stabilizing the weakened lower limb and providing support for standing and walking. Concurrently, the orthosis should allow for natural and efficient movement of the limb as required for walking. The focus of this work is to develop a new method for controlling orthotic knee joints. The new control method uses a mechanical system to monitor loading and timing events and patterns, and apply knee-locking function when the limb is loaded. A prototype was built and tested on a polio patient and demonstrated the feasibility of this approach for providing reliable orthotic function. Further work aims to test the knee joint on a larger group of individuals within the community.
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Seda Yilmaz, Colleen Seifert, Shanna R. Daly and Richard Gonzalez
J. Mech. Des 138(7), 071102; doi: 10.1115/1.4032219
Current design theory lacks a systematic method to identify what designers know that helps them to create innovative products. In the early stages of idea generation, designers may find novel ideas come readily to mind, or may become fixated on their own or existing products. This may limit the ability to consider more, and more varied candidate concepts that may potentially lead to innovation. To aid in idea generation, we sought to identify “design heuristics,” or “rules of thumb,” evident in award-winning designs. In this paper, we demonstrate a content analysis method for discovering heuristics in the designs of innovative products. Our method depends on comparison to a baseline of existing products so that the innovative change can be readily identified. Through an analysis of key features and functional elements in the designs of over 400 award-winning products, forty heuristic principles were extracted. These Design Heuristics are outlined according to their perceived role in changing an existing product concept into a novel design, and examples of other products using the heuristics are provided. To demonstrate the ease of use of these Design Heuristics, we examined outcomes from a classroom study, and found that concepts created using Design Heuristics were rated as more creative and varied. The analysis of changes from existing to innovative products can provide evidence of useful heuristic principles to apply in creating new designs.
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Soheil Arastehfar, Ying Liu and Wen Feng Lu
J. Mech. Des 138(3), 031103 (Feb 01, 2016); doi: 10.1115/1.4032396
Digital prototype (DP), as a form of communication media, allows designers to communicate design concepts to users by rendering the physical characteristics, e.g., size, colour, and texture. One important aspect is how well users can estimate the values of the physical characteristics of design concepts through interactions with DPs. Better estimates can lead to better perceptions of the designed attributes closely associated with the physical characteristics, and hence, useful user feedback about design concepts. The correctness of the estimates depends on two crucial factors: the ability of DPs to render physical characteristics and the way DPs are used to communicate physical characteristics in a particular environment and via different input/output devices. To date, little attention has been paid to the latter. Hence, it is important to identify an effective way of using DPs via the effectiveness assessment of various possibilities. This paper introduces a methodology for evaluating the effectiveness of communicating physical characteristics to users using DPs. During user interactions with DPs, the methodology collects user estimates of various physical characteristics and assesses the estimates on three dimensions, i.e., degree of correctness, time to make an estimate and handling of different values. The assessments are then evaluated by statistical analysis to reveal the effectiveness of the way of engaging DPs in helping users correctly and quickly estimate the values. The evaluated effectiveness reflects how successful the way of using a DP is, and also helps to suggest a better approach.
Additive manufacturing (AM) techniques provide designers with greater freedom in creating customized products with complex shapes. When major design changes are made to a part, undesirable high cost increments may be incurred due to AM process setting adjustments, challenging designers to explore AM-enabled design freedom while controlling costs at the same time. In this research, we introduce the concept of a variable product platform and its associated AM process setting platform, based on which the design and process setting adjustments can be restricted within a bounded feasible space in order to limit cost increments. Fuzzy Time-Driven Activity-Based Costing (FTDABC) approach is introduced to predict AM production costs based on process settings. The process setting adjustment’s feasible space boundary is identified by solving a multiobjective optimization problem. Design parameter limitations are computed in a Mamdani-type expert system and then used as constraints in the design optimization to maximize customer perceived utility. Case studies on designing an R/C racing car family illustrate the proposed methodology and demonstrate that the optimized additive manufactured variable platforms can improve product performances at lower costs than conventional consistent platform based design.
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Comparing Strategies for Topologic and Parametric rule Application in Automated Computational Design Synthesis
Corinna Königseder and Kristina Shea
J. Mech. Des. 2015;138(1):011102-011102-12. doi:10.1115/1.4031714
Computational Design Synthesis (CDS) methods can be used to enable the computer to generate valid and even creative solutions for engineering tasks. In grammatical approaches to CDS, formal grammars are used to represent a desired design language. This language consists of vocabulary that usually describes components and subsystems of a design and a set of grammar rules that describe possible design transformations. The formalized engineering knowledge can then be used by the computer to synthesize designs. For most engineering tasks, two different kinds of rules are required: rules that change the topology of a design, i.e. how the components are connected, and rules that change parameters of a design. One of the main challenges in CDS using topologic and parametric grammar rules is to decide a priori which type of rule to apply in which stage of the synthesis process as well as whether to start from a valid design and perturb it or to start from a void design. The research presented in this paper compares different strategies for topologic and parametric rule applications during automated design synthesis driven by a search algorithm. The presented strategies are compared considering quantity and quality of the generated designs. The effect of the strategies, the selected search algorithm, and the initial design, from which the synthesis is started, are analyzed for two case studies: the synthesis of gearboxes and of bicycle frames. Results show that the effect of the strategy is dependent on the design task and recommendations are given on which strategies to use for which design task.
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Assessing Quality of User-Submitted Need Statements From Large-Scale Needfinding: Effects of Expertise and Group Size
Cory R. Schaffhausen and Timothy M. Kowalewski
J. Mech. Des 137(12), 121102 (2015); doi: 10.1115/1.4031655
Collecting data on user needs can result in overwhelming amounts of data, especially if user groups are large and diverse. Additional analysis is necessary to prioritize a small subset of needs for further consideration. This study presents a simplified quality metric and online interface appropriate to initially screen and prioritize lists exceeding 500 statements for a single topic or product area. Over 20,000 ratings for 1697 need statements across three common product areas were collected in 6 days. A series of analyses tested whether particular characteristics of users and groups affect the number of high-quality needs that can be generated. The evaluated characteristics were user group size, needs submitted per person, and expertise and experience levels of users. The results provided important quantitative evidence of fundamental relationships between the quantity and quality of need statements. Increased quantities of high-quality need statements resulted both due to increasing user group size and due to increasing counts per person using novel content-rich methods to help users articulate needs. However, a user’s topic-specific expertise (self-rated) and experience level (self-rated hours per week) were not significantly associated with increasing need quality.
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Joran W. Booth, Tahira N. Reid, Claudia Eckert and Karthik Ramani
J. Mech. Des 137(8), 081101 (Aug 01, 2015) Paper No: MD-14-1324; doi: 10.1115/1.4030232
Defining functions when dissecting a product is an important method to learn how it works. There are three primary methods for identifying functions in a product. In this study, we tested the hypothesis that one of the methods is superior to the others. We tested the three methods in a within-subjects design of experiments to see how each method compared to the others, as measured by the size, shape, and content of each function tree. We found that there was no dominant method for discovering functions in a dissected device. This implies that any method is appropriate for understanding how a device works. Additionally, we found generalized aspects of creating function diagrams that are difficult for novice designers.
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Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks
Suppawong Tuarob and Conrad S. Tucker
J. Mech. Des 137(7), 071402 (Jul 01, 2015) Paper No: MD-14-1611; doi: 10.1115/1.4030049
Lead users play a vital role in next generation product development, as they help designers discover relevant product feature preferences months or even years before they are desired by the general customer base. Existing design methodologies proposed to extract lead user preferences are typically constrained by temporal, geographic, size and heterogeneity limitations. To mitigate these challenges, the authors of this work propose a set of mathematical models that mine social media networks for lead users and the product features that they express relating to specific products. The authors hypothesize that i) lead users are discoverable from large scale social media networks and ii) product feature preferences, mined from lead user social media data, represent product features that do not currently exist in product offerings but will be desired in future product launches. An automated approach to lead user product feature identification is proposed to identify latent features (product features unknown to the public) from social media data. These latent features then serve as the key to discovering innovative users from the ever increasing pool of social media users. The authors collect ~2.1 billion social media messages in the United States during a period of 31 months (from March 2011 to September 2013) in order to determine whether lead user preferences are discoverable and relevant to next generation smartphone designs.
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This section includes brief descriptions of articles soon to be or recently published by the Journal of Mechanical Design. These featured articles highlight recent research developments and emerging trends in mechanical design. For Abstracts and Full Articles please see ASME's Digital Collection.