Part count reduction through part combination is a recognized goal of Design For Assembly (DFA). Some of the many benefits of part count reduction are: a reduced number of assembly operations, reduced procurement costs, cycle time reduction, supply chain reduction, and higher potential profits. In previous work, force flow analysis, a new technique to map forces as they flow across interfaces in a product, was shown to be successful at systematically providing creative insights for part combination. These insights arise by highlighting components having no relative motion between them. This paper presents a novel concept that extends the theoretical basis of force flow analysis to address component combinations having varying degrees of relative motion. A systematic method for classifying these sets of components is given, and compliant mechanisms are presented as an example of combining parts with relative motion. Examples are provided for the redesign of a “Quick Grip™ Clamp” and a staple remover, both of which highlight a specific class of relative motion components.

The central theme of this paper is the representation of tolerances in engineering design and manufacturing. A sub-problem of this central theme is the structure of form and size errors created by typical machining processes. This subproblem is addressed using a fractal-based procedure. In particular, workpiece profiles are generated for the shaping manufacturing process, using a simulation model and incorporating the basic shearing mechanism of machining with typical error sources. The resultant profiles are analyzed using a fractal-based, box counting algorithm. The results, which reveal the presence of variational structure, are discussed, emphasizing the relationship between total variability and function. Finally, modifications and future directions to the research are presented.

Tolerancing is a crucial problem for mechanical designers, as it has quality and cost implications on product design. Research in tolerancing has addressed specific areas of the problem. Building upon previous research, a unified approach for geometric tolerancing with fractal-based parameters has been recently proposed. This paper explores an alternative error profile analysis and synthesis method, based on wavelets, that maintains and extends the use of fractals for surface error abstraction. An overview of the theory of wavelets is provided, and the link between fractals and wavelets is established. Experimental data are used to illustrate the application of wavelet theory to surface profile reconstruction and synthesis. The synthesis methods are then implemented in the design of ball-bearing elements, demonstrating the utility of fractal-based tolerancing. Plans for further study and implementation conclude the paper.

The primary objective of this research is to develop surface models of machining processes to simulate machined surface profiles and analyze their structure. In this paper, fractal analysis is used to discover and characterize the variational pattern of turned surfaces. This analysis provides a means of explicitly stating the precision of a machining process. The results are used to further test the validity of the concept of fractal dimensions at tolerance scales and to establish a relation between surface models, experimental surfaces, and fractals. Based on these results, a more complete model of turning is available to designers for choosing process and design parameters and for comparing the precision between competing machining processes.

Geometry is a language for representing and communicating mechanical design information. To enhance the integration of design and manufacturing tasks, a representation of product geometry at appropriate levels of abstraction for geometric reasoning is necessary. In addition, a mechanism to perform reasoning with the representation is needed. This paper describes a computational formalism for representing and manipulating geometry at different abstraction levels. Geometry is abstracted in terms of form features. Spatial relationships between features, which are important components for geometric reasoning, are represented using a modification of a previously developed technique — the intermediate geometry language (IGL). To enable geometric reasoning, information abstracted using features and the IGL is transformed into a data model based on object-oriented modeling technology. An object algebra is defined to query the data model for information.

A new feature recognition method is presented that generates volumetric feature representations from conventional boundary representations of mechanical parts. Recognition is accomplished by decomposing the known total feature volume of a part into a set of smaller volumes through analytic face extension. The decomposed volumes are combined to generate an initial set of features. Alternative sets of features are generated by maintaining and evaluating information on intersections of the initial feature set. The capabilities of the method are demonstrated through both a hypothetical and a real world design example. The method’s ability to locate features despite interactions with other features, and its ability to generate alternative sets of features, distinguishes it from existing recognition techniques.

Numerical concept selection methods are used throughout industry to determine which among several design alternatives should be further developed. The results, however, are rarely believed at face value. Uncertainties (or errors) in subjective choices, modeling assumptions, and measurement are fundamental causes of this disbelief. This paper describes a methodology developed to predict overall error ranges, in addition to estimating a confidence measure in the numerical evaluation results. Each numerical assignment is given an associated error tolerance, and then treated as a probability error to create a simple means to propagate the uncertainties. A degree of confidence is also derived, similar to a statistical t-test, to indicate an induced confidence level in the final decision. Two preliminary concept selections are shown, to illustrate the methodology. Results from these concept selections indicate that (1) uncertainties can be suitably captured and quantified; (2) critical design questions are addressed during the process of numerical concept selection with error propagation; and (3) designers can make more informed and confident decisions through error estimation.

The education community has focused attention recently on a number of initiatives to evolve, and perhaps revolutionize, approaches for teaching science, mathematics, and engineering. In this paper, we present a new engineering and design technology program, initiated in 1992 and referred to as DTEACh, that focuses on the elementary grades. Two components comprise this new program: (1) open-ended design and exploration lessons that use hands-on models to teach integrated mathematics and science principles, and (2) a two-part teacher preparation program to provide teachers with the necessary engineering, mathematics, and science fundamentals for DTEACh. In this paper, we focus on a description of the teacher preparation program, including discussions of a novel teaching model, the subject matter for engineering and design fundamentals, and evaluation of the program. Results of the evaluation that teachers are more confident and equipped to facilitate the instruction of mathematics, science, and engineering principles.

The problem addressed in this paper is the development of a physico-mathematical basis for mechanical tolerances. The lack of such a basis has fostered a decoupling of design (function) and manufacturing. The groundwork for a tolerancing methodology is laid by a model of profile errors, whose components are justified by physical reasoning and estimated using mathematical tools. The methodology is then presented as an evolutionary procedure. This procedure harnesses the various tools to analyze profiles in terms of a minimum set of profile parameters and to re-generate them from the parameters. This equips the designer with a rational means for estimating performance prior to manufacturing, hence integrating design and manufacturing. The utility of the functional tolerancing methodology is demonstrated with performance simulations of a lathe-head-stock design, focusing on gear transmission with synthesized errors.

New products drive business. To remain competitive, industry is continually searching for new methods to evolve their products. To address this need, we introduce a new reverse engineering and redesign methodology. Motivation is provided by the S-curve product improvement history. We start by formulating the customer needs, followed by reverse engineering, creating a functional model through teardowns. The functional model leads to specifications that match the customer needs. Depending upon required redesign scope, new features are possibly conceived, or not. Next models of the specifications are developed and optimized. The new product form is then built and further optimized using designed experiments. An electric wok redesign provides an illustration. The methodology has had a positive impact in results and systematic approach, both on design education and application.

In this paper, we investigate four methods that yield mathematical measures to analyze the precision of surfaces of manufactured parts. These four methods, namely the autocorrelation function, the Fourier spectrum, the Karhunen-Loève expansion, and a fractal-wavelet representation, are applied to surfaces produced from grinding processes. The first two methods are standard methods used in the surface analysis literature for qualitative signal characterization. The Karhunen-Loève expansion method, used in various signal processing applications, has never been applied to the field of surface characterization and representation. The fractal-wavelet representation has been previously proposed by the authors; its suitability to generate characteristic measures is investigated in this paper. The existence of characteristic measures of surface precision should aid designers in choosing process and design parameters and in comparing the precision between competing machining processes. The use of such measures is essential in taking a forward step towards integrating the fields of design and manufacturing.

To ensure a successful design, it is important to understand the effect that design parameters have on design performance. Tolerances are an important subset of design parameters. In this paper, the relevance of fractal surface characterization for profile tolerances is investigated. The notion of fractal geometry is discussed. The validity of a fractal characterization on a cam surface is determined. Dynamic simulations of a cam-follower system are used to qualify the effect of a fractal profile error on system performance.

Design for Assembly (DFA) is the process by which a product is designed to be easily assembled. Such design simplifications are accomplished through reducing the number of operations required to assemble the product, improving the handling of each component, and/or modifying the required operations (insertion, joining, etc.). There exist several techniques for assessing the assemblability of a design through an analysis of these three aspects. However, there also exists a clearly defined need for evolving such techniques to indicate how a product should be redesigned with respect to customer needs and associated functionality. This paper presents three such evolutions, aimed at reducing the number of components in an assembly during redesign. The first technique is a component elimination procedure, the second technique is a component combination analysis, and the third technique establishes a logical approach for revealing more abstract component elimination or combination opportunities. These three DFA techniques are integrated within a reverse engineering and redesign methodology. They are then applied to a industrial design application, i.e., redesign of an auxiliary automobile visor. Results demonstrate definitive part count reduction, while maintaining and improving design functionality.

In Selective Laser Sintering (SLS), one of several powder-based Rapid Prototyping (RP) methods, thermal transfer plays an important role in machine design. Thermal control of the processing area or “space” is crucial for accurate, repeatable part formation. In a typical SLS powder bed, significant thermal transfer occurs through mixed convection and conduction. In this paper, a polymeric powder bed subjected to in-bed convection is investigated with the goal of quantitatively determining transient thermal behavior. A numerical model describing heat transfer within a powder is presented. Convection and conduction are both numerically shown to play important roles in steady state temperature profiles. The design and construction of an experimental apparatus to measure the temperature profiles within a powder subjected to forced convection are also described. Full factorial experiments are used to calibrate and validate the numerical model, as well as to examine transient behavior. Using information gained from numerical and experimental work, suggestions are made for machine design to better control part precision and reproducibility. For example, it is shown that actively controlling the temperature of air convected through the powder bed gives fine, rapid control over the feed side cylinder thermal state.

At a basic level, product design composes three primary tasks: specification development, conceptual and configuration design, and product refinement. Customer needs analysis, product benchmarking, and business case analysis are fundamental to specification development, whereas conceptual design and product refinement entail functional analysis, the generation of solution principles and product geometry, concept selection, mathematical modeling, prototyping, and Taguchi analysis for variability. In this paper, we focus on an advanced method for functional analysis. This method demonstrates clear ties to customer needs, and is based on an empirical study of 58 household consumer products. As part of this study, a common vocabulary for product functions and flows is developed and applied to the consumer products. House of Quality results are then used to correlate customer importance to the product functions. Data from these correlations provide a basis for determining critical functions and flows across all products and within important product domains, such as material processors and beverage brewers. From this study, we discuss how the results can be applied to product testing and benchmarking, design by analogy, the identification of functional groups and dependencies, and design education.

The status of fault patterns on part surfaces can provide valuable information about the condition of the manufacturing system. In this work, we aim to develop a reliable fault detection and diagnosis tool in order to assure the automated production of high-quality parts. Such a tool provides a means of integrating the manufacturing and design phases. Accurate detection of the part surface condition in manufacturing ensures the fault-free design of the manufacturing parameters and machine components. This paper introduces a mathematical transform that has the potential to detect faults in manufacturing machines. Specifically, the paper focuses on the decomposition of complex signals to allow the detection of faults. The Karhunen-Loève transform is investigated by means of numerically-generated signals. Numerical signals are studied to decompose a variety of signals, including deterministic, stochastic, stationary, and nonstationary signals. Finally, the potential utility of the proposed technique is discussed in the context of a newly-maturing manufacturing process.

Monitoring the condition of parts and machine components is a crucial task in ensuring fault-free manufacturing. In this work, we propose an alternative condition monitoring technique, with great potential in extracting and isolating individual fault patterns from manufacturing signals. We propose that the Karhunen-Loève transform provides the ability to decompose measured signals into decorrelated fault patterns, in the form of fundamental eigenvectors. These fundamental eigenvectors can then be monitored by means of coefficient vectors, which indicate any changes in the fault patterns. The technique can provide accurate fault information, whether the manufacturing signals are deterministic, stochastic, stationary, or nonstationary. This paper presents the fundamentals of the proposed technique and its extension to condition monitoring. The outputs of the Karhunen-Loève transform are studied to interpret their physical significance. Then, a subset of general manufacturing signals is used to understand the mathematical foundations of the technique. Extensions to general functions are investigated by means of numerical simulations. The technique proposed in this paper has great potential in providing a robust condition monitoring tool.

Contemporary industries are devoting increasing attention to the product development process, due to tight market shares and the abridged product life cycle. Reliable scaled product testing with rapid prototypes has the potential to improve these processes by replacing traditional costly and time-consuming product tests. In this context, rapid prototypes provide visual, ergonomic, and functional information with minimal time delay. Among the information classes, reliable functional information is least realized because of several features of rapid prototypes: (1) limited material choices and part size; (2) distinct material structure; (3) restrictive loading conditions; and (4) state-dependent material properties. To develop reliable functional tests, an improved similarity method is needed to overcome these limitations. The traditional similarity method, based on a Buckingham П approach, is commonly applied to perform scaled tests. In contrast to this method, wherein the state transformation between two similar systems is derived from dimensional vectors, we present a new similarity method that empirically derives the transformation from a geometrically simple specimen pair. The primary advantage of the new method over the traditional method is the capability to relate highly distorted systems. In this paper, the concept and theoretical framework of the novel similarity method are introduced, and two numerical examples demonstrate the new method.