Engineering Design Research: Successful Integration of Education, Practice, and Study in the CEDAR Group

Engineering design is a generally nascent area of research within the engineering disciplines, spanning only a few decades of critical investigation. Clemson University has been at the forefront of the development of this field and continues this with a living experiment in how to integrate education, practice, and research through the CEDAR group. This essay introduces the reader to design research and the areas of study within CEDAR. Following this, an analysis of the research trends exposes three pillars of CEDAR philosophy: helping others, seeking variety, and learning from others. The goal of this essay is to introduce the wider scientific and engineering research and education community in South Carolina to this field and the possible opportunities for collaboration

Engineering Design Research: Successful Integration of Education, Practice, and Study in the CEDAR Group

Engineering design is a generally nascent area of research within the engineering disciplines, spanning only a few decades of critical investigation. Clemson University has been at the forefront of the development of this field and continues this with a living experiment in how to integrate education, practice, and research through the CEDAR group. This essay introduces the reader to design research and the areas of study within CEDAR. Following this, an analysis of the research trends exposes three pillars of CEDAR philosophy: helping others, seeking variety, and learning from others. The goal of this essay is to introduce the wider scientific and engineering research and education community in South Carolina to this field and the possible opportunities for collaboration

A Study of Designer Familiarity with Product and User During Requirement Elicitation

It is important to recognise the effects of a designer\u27s source of information and decision making during requirements elicitation. Requirements are widely recognised as an important step in the design process. Designers may have perspective based on their experience which results in a level of familiarity with the design. This paper reports on a study that explores the effects of designer familiarity with a project and its user on their ability to elicit requirement specifications. Two familiarity constructs, product and user, are measured as low or high and used to study requirement elicitation with varying familiarity. A high familiarity study using five graduate students and a low familiarity study using a team of five students during senior capstone design are compared for their requirements elicitation. The results of this study include an analysis of the requirements developed and participant survey results from the elicitation process. The results revealed familiarity does in fact have an effect on the ability of elicit requirements. Participants in the low familiarity study expressed difficulty and eliciting requirements while those in the high familiarity study were able to generate more requirements at a faster rate

Assembly Time Estimation: Assembly Mate Based Structural Complexity Metric Predictive Modeling

This paper presents an automated tool for estimating assembly times of products based on a three step process: connectivity graph generation from assembly mate information, structural complexity metric analysis of the graph, and application of the complexity metric vector to predictive artificial neural network models. The tool has been evaluated against different training set cases, suggesting that partially defined assembly models and training product variety are critical characteristics. Moreover, the tool is shown to be robust and insensitive to different modeling engineers. The tool has been implemented in a commercial CAD system and shown to yield results of within ±25% of predicted values. Additional extensions and experiments are recommended to improve the tool

Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation

This paper compares two different methods of graph generation for input into the complexity connectivity method to estimate the assembly time of a product. The complexity connectivity method builds predictive models for assembly time based on 29 complexity metrics applied to the product graphs. Previously, the part connection graph was manually created, but recently the assembly mate method and the interference detection method have introduced new automated tools for creating the part connectivity graphs. These graph generation methods are compared on their ability to predict the assembly time of multiple products. For this research, eleven consumers products are used to train an artificial neural network and three products are reserved for testing. The results indicate that both the assembly mate method and the interference detection method can create connectivity graphs that predict the assembly time of a product to within 45% of the target time. The interference detection method showed less variability than the assembly mate method in the time estimations. The assembly mate method is limited to only solidworks assembly files, while the interference detection method is more flexible and can operate on different file formats including IGES, STEP, and Parasolid. Overall, both of the graph generation methods provide a suitable automated tool to form the connectivity graph, but the interference detection method provides less variance in predicting the assembly time and is more flexible in terms of file types that can be used

Development of a Geometric Model Retrieval System: A design exemplar case study

This paper presents a case study examining design exemplar technology implemented as a search and retrieval tool for tyre mould inserts. Limitations of using the geometric-based exemplar approach, such as tediousness of authoring exemplars and time complexity, are identified and addressed through a new parametric-based exemplar approach. Here, the maxima and minima are calculated based upon the specifications of the query mould insert. The design exemplar is demonstrated to be useful primarily in prototyping query mechanisms. Ultimately, customer requirements necessitated implementing the parametric approach as a dedicated software package grounded on the exemplar-based prototyped query mechanism

Predicting Requirement Change Propagation Using Higher Order Design Structure Matrices: An Industry Case Study

This research examines higher order design structure matrices as requirements change modelling tools to predict requirement change propagation through two large-scale industry design projects. Due to the iterative nature of design, requirements will evolve and change. Changes in requirements can propagate to other requirements on different subsystems leading to possible increases in the project cost and lead-time. Predicting these requirement changes enables the designers to foresee unanticipated changes and maximises the probability for the project\u27s success. These studies reveal that second-order relationships are influential in predicting requirement change propagation. Unforeseen propagation occurring in first-order form was rare, rather it was occurring in second order. Modelling requirements change exposes these secondary relationships early in the engineering change (EC) definition process, thereby enhancing the decision-making process and augmenting cost estimation. A modelling tool, such as that proposed in this paper, can provide the designer insight into the requirements which may be affected before approving an EC

Reasons for Change Propagation: a case study in an automotive OEM

This paper focuses on identifying the reasons for change propagation during the production phase of the product life cycle. Unlike the traditional change propagation study where the focus is within the product, this study is focused to understand the propagation effects of change on other functional silos in the manufacturing firm. First, the reasons for the changes are identified using archival analysis through which it is found that 77.0 % of changes are due to internal reasons while 23.0 % are external. Second, these changes are distinguished into genesis, and propagated changes using a matrix-based modeling approach from which the reasons for propagation are identified. It is inferred that 32.4 % of the total changes are due to propagated changes such as inventory issues, manufacturing issues, and design error rectification. The majority of reasons for these propagated changes include document error rectification such as BOM error, drawing error, incorrect introduction date in engineering change note, and design error rectification such as design limitations. The findings indicate nearly one-third of time spent by the engineers can be reduced by developing appropriate controls during the change release process

Comparative Analysis of Requirements Change Prediction Models: Manual, Linguistic, and Neural Network

Requirement change propagation, if not managed, may lead to monetary losses or project failure. The a posteriori tracking of requirement dependencies is a well-established practice in project and change management. The identification of these dependencies often requires manual input by one or more individuals with intimate knowledge of the project. Moreover, the definition of these dependencies that help to predict requirement change is not currently found in the literature. This paper presents two industry case studies of predicting system requirement change propagation through three approaches: manually, linguistically, and bag-of-words. Dependencies are manually and automatically developed between requirements from textual data and computationally processed to develop surrogate models to predict change. Two types of relationship generation, manual keyword selection and part-of-speech tagging, are compared. Artificial neural networks are used to create surrogate models to predict change. These approaches are evaluated on three connectedness metrics: shortest path, path count, and maximum flow rate. The results are given in terms of search depth needed within a requirements document to identify the subsequent changes. The semi-automated approach yielded the most accurate results, requiring a search depth of 11 %, but sacrifices on automation. The fully automated approach is able to predict requirement change within a search depth of 15 % and offers the benefits of full minimal human input

Assembly Time Modeling Through Connective Complexity Metrics

This paper presents an approach for the development of surrogate models predicting the assembly time of a system based on complexity metrics of the physical system architecture when detailed geometric information is unavailable. A convention for modelling physical architecture is presented, followed by a sample of 10 analysed systems used for training and three systems used for validation. These systems are evaluated on complexity metrics developed from graph theoretic measures. An example model is developed based on a series of regressions of trends observed within the sample data. This is validated against the systems that are not used to develop the model. The model developed uses average path length, part count and path length density to approximate assembly time within the standard deviation of the subjective variation possible in Boothroyd and Dewhurst design for assembly (DFA) analysis. While the specific example model developed is generalisable only to systems similar to those in the sample set, the capability to develop mappings between physical architecture and assembly time in early-stage design is demonstrated