bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-Linear and Non-Stationary Random Responses of Discretized Plate Structures by Stochastic Direct Integration With Correction Factor
Authors: Liu M. L. 
Published: December 2020

Abstract
The investigation reported in this paper is to further improve the effectiveness of some stochastic direct integration schemes. These stochastic direct integration schemes were proposed to compute response statistics, such as mean squares and variances of generalized displacements, of large discretized structures undergoing large non-linear deformation and under non-stationary random excitation. First of all, the stochastic Newmark method is extended to include the stochastic as well as the deterministic excitations. Next, a correction factor that is to be applied to the discrete white noise is introduced. The stability criterion is then examined. The advantage of introducing such a correction factor is that one is not limited to those time step sizes that have been found to yield accurate response statistics in previous investigations. Instead, one can choose a time step size in the way he may in an analysis using the deterministic Newmark method. The correction factor is determined based on this chosen time step size, thus providing the flexibility in balancing the needs of accuracy and effectiveness. Subsequently, the hybrid strain based three-noded flat triangular shell element, single- or multi-layered, is employed to model selected plate structures. These numerical examples demonstrate the accuracy and effectiveness of the proposed methodology.
An Intelligent Manufacturing System for Predictive Scheduling and Reactive Scheduling
Authors: Sun J. , Xue D. , Norrie D. H. 
Published: December 2020

Abstract
This research introduces an intelligent, distributed approach for predictive scheduling and reactive scheduling. Product descriptions and their manufacturing requirements are represented using a feature-based modeling scheme. Manufacturing resources, including facilities and persons, are modeled as agents. In predictive scheduling, the optimal production schedule is identified through heuristic search and agent-based negotiation. In reactive scheduling, the original schedule is modified for responding to the changes of orders and resources through a match-up approach and agent-based negotiation, when the original schedule cannot be accomplished due to these changes.
A Mathematical Model for Predicting Conditions That Prevent Gaseous Fuel-Air Mixing
Authors: Brasoveanu Dan , Gupta Ashwani K. 
Published: December 2020

Abstract
A unified model is used here to determine the distributions of pressure, temperature and velocity that reduce gaseous fuel-air mixing. The model uses the fuel mass fraction within infinitesimal fluid elements and the total derivative of this fraction with respect to time to quantify the degree and rate of mixing, respectively. An Eulerian representation is used. The model is valid for gaseous fuels that contain a single chemical species and for low-pressure combustors. The model permits the presence of only trace amounts of combustion products within the mixing region. Aside from these restrictions, the mixing model can be applied to a variety of combustor designs and operational conditions. Results show that certain distributions of pressure, temperature and velocity at the fuel-air boundary reduce or even prevent gaseous fuel-air mixing, in particular whenever the logarithmic rate of temperature and the velocity divergence are comparable. These conditions yield low intensity and efficiency combustion as well as high pollutants emission level.
Treatment of the Responsibility and Controllability in Team-Based Concurrent Product and Process Design
Authors: Chen Li , Li Simon 
Published: December 2020

Abstract
In team-based concurrent product and process design (CPPD), one type of team activities proceeds alternately in a one-by-one fashion. This sequential mode of design flow can be treated as a generalized leader/follower team paradigm, in which the leader acts as a principal team and the follower as a subordinate team. In the dual-team approach, the leader and the follower correspond to two aspects of CPPD — product design and process design — whereby the alternate nature of design tasks can be characterized through the interactions of the leader and the follower. To reveal the variety of such team activities, the notion of responsibility and controllability in various design protocols is introduced to describe the respective roles of the leader and the follower in team design. The method of Design for Satisfaction (DfS) is used as a framework to characterize team’s preference toward a feasible design in view of the team’s goal. Accordingly, the satisfaction-driven dual-team based CPPD models are developed to account for different team scenarios. A design example is used to support the illustration of the team design approach suggested.
A Hybrid Fuzzy Simplex Genetic Algorithm
Authors: Trabia Mohamed B. 
Published: December 2020

Abstract
Nelder and Mead Simplex (NMS) algorithm is an effective nonlinear programming technique. Trabia and Lu (1999) recently presented a novel algorithm, Fuzzy Simplex (FS), which improved the efficiency of Nelder and Mead Simplex by using fuzzy logic to determine the orientation and size of the simplex. While Fuzzy Simplex algorithm can be successfully used to search a wide variety of functions, it suffers, as other simplex algorithms, from its dependence on the initial guess and the original simplex size. This paper addresses this problem by combining the Fuzzy Simplex with Genetic Algorithm (GA) in a hybrid algorithm. Standard test problems are used to evaluate the efficiency of the algorithm. The algorithm is also applied successfully to several engineering design problems. The Hybrid GA Fuzzy Simplex algorithm generally results in a faster convergence.
An Introduction to Adaptive Robust Design for Mechanical Assemblies
Authors: Parkinson Alan R. , Chase Kenneth W. 
Published: December 2020

Abstract
This paper introduces a relatively new area of robust design we choose to call adaptive robust design. An adaptive robust design has features, not otherwise required by the function of the design, which allow the design to absorb or cancel out the effects of variation. A passive adaptive robust design (or just “passive design” for short) requires external adjustment or setting; passive designs are usually set at the factory but may also be adjustable in the field. An active adaptive robust design (or “active” design) can automatically adapt itself to variation which changes over time. We discuss adaptive robust design as applied to mechanical assemblies. A framework for research in this area is presented. We also discuss the relationship of adaptive design to other robust design methods.
A Multi-Level Genetic Assembly Planner
Authors: Chen Shiang-Fong , Liu Yong-Jin 
Published: December 2020

Abstract
Assembly sequence planning (ASP) is a combinatorial optimization problem with highly non-linear geometric constraints. Most proposed methodologies are based on graph theory and involve complex geometric and physical analysis. As a result, even for a simple structure, it is difficult or impossible to take all important criteria into consideration. In order to bring assembly sequence planning closer to real-world application, this paper proposes a genetic planner for efficiently finding global-optimal assembly sequences. To optimize our genetic-algorithm-based approach, we propose a hierarchical genetic structure and an evaluation mechanism for dynamically adapting control parameters in our hierarchical structure. Unlike conventional genetic algorithms, which use static genetic operator probability settings (GOPS), our hierarchical genetic planner searches for optimal assembly sequences in a low-level GA and manipulates low-level GOPS using a high level GA. Conventional “GA within GA” approaches perform, for every high-level generation, a full low-level GA run, whereas our multi-level GA uses a high-level GA to isochronously update low-level GA control parameters during each low-level GA run. Experimental results show that our multi-level genetic assembly sequence planner solves combinatorial ASP problems quickly, reliably, and accurately.
Decision-Based Collaborative Optimization Under Uncertainty
Authors: Gu Xiaoyu , Renaud John E. , Ashe Leah M. , Batill Stephen M. , Budhiraja Amarjit S. , Krajewski Lee J. 
Published: December 2020

Abstract
In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg (1996a, 1998) is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming the Hazelrigg framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.
Visualization and Comparison of Solution Paths With Artificial Metrics
Authors: Rudolph Stephan 
Published: December 2020

Abstract
The use of graph theoretical concepts, which can bridge the gap between the construction of verbal and simple analytical design model representations, is explored. Graph methods offer the possibility to process both verbal and analytical object information, since in either case the identification of the objects represented is achieved using an identifier concept, which relies on pattern matching techniques. The comparison of different solution paths for the same conceptual design problem using artificial metrics is shown in a case study. The results show that the solution paths for the analytical model based on the complete set of analytical equations can already be determined and analyzed using a verbal description model. Furthermore, both solution paths of the analytical and the verbal model are identical, if the relevance lists of the verbal design descriptions are complete.
On the Link Between Design Against Fatigue and Fracture Mechanics
Authors: Debongnie J. F. 
Published: December 2020

Abstract
The gradient method, often used in design against fatigue, is analyzed from the point of view of its link with fracture mechanics. By a logical step, a law is deduced for the variation of the endurance limit in terms of the relative stress gradient. The proposed expression is found to be in good agreement with older ones and may easily be extended to a large class of materials.
Robust Design Using Fuzzy Numbers: Consideration of Correlation of Design Variables in Fuzzy Operation
Authors: Arakawa Masao , Yamakawa Hiroshi , Ishikawa Hiroshi 
Published: December 2020

Abstract
These days robust design has become more and more important to raise reliability in the products under many kinds of imprecision, including human errors, modeling errors and so on. In the past, robust design only tries to obtain the robust design variables that have less sensitivity with quasi-optimum solutions. Recently, many researchers try to design not only of design variable’s value but also to its deviations. Which means that robust design tries to have wider range of design variables and still keeps robustness in the design. For the simplicity of calculation, most of them assume independence of design variables, thus, the ranges are determined by their upper and lower bound. However, in the real design almost every design variables are not independent with the influence of constraints. In this study, we will newly propose the method that can determine the relationships of design variables, something like correlation of design variables. We will show the effectiveness of the method by some numerical examples.
Numerical Kinematic Analysis of the MacPherson Strut Motor-Vehicle Suspension System
Authors: Attia Hazem A. , Mohamed Maher G. 
Published: December 2020

Abstract
In the present paper, an efficient numerical algorithm for the kinematic analysis of the multi-loop MacPherson Strut suspension system is formulated. The suspension system is usually used for front as well as axles of current car productions. The spatial two-DOF multi-loop MacPherson Strut suspension mechanism contains all types of basic kinematic joints; revolute, prismatic, and spherical joints. The kinematic analysis is carried out in terms of the rectangular Cartesian coordinates of some defined points in the links and at the joints. The presented formulation in terms of this system of coordinates is simple and involves · only elementary mathematics. A numerical example is presented.
Approximate Life-Cycle Assessment in Conceptual Product Design
Authors: Eisenhard Julie L. , Wallace David R. , Sousa Ines , De Schepper Mieke S. , Rombouts Jeroen P. 
Published: December 2020

Abstract
Prior work has demonstrated the integration of detailed life-cycle assessment into a traditional design modeling process. While a full life-cycle assessment provides insight into a product’s potential impact on the environment, it is often too time consuming for analysis during conceptual product design, where ideas are numerous and information is scarce. The work presented in this paper explores an approximate method for preliminary life-cycle assessments without detailed modeling requirements. Learning algorithms trained on the known characteristics of existing products allow the environmental impacts of new products to be approximated quickly during conceptual design. Artificial neural networks train on product attributes and environmental impact data from pre-existing life-cycle assessment studies. The product design team queries the trained artificial model with new high-level product attribute data to quickly obtain an approximate impact assessment for a new product concept. Tests based on simplified inventory data have shown it is possible to predict impacts on life-cycle energy consumption, and that there is a basis for the method to be used in also predicting solid material, greenhouse effect, ozone layer depletion, acidification, eutrophication, winter smog, and summer smog.
FixtureScout: Restructuring Process Planning and Fixturing Decisions
Authors: Hayes Caroline C. , Veretennikov Aleksey 
Published: December 2020

Abstract
This paper describes FixtureScout, a program that performs a high-level clamp and clamping surface analysis very early in the planning process. FixtureScout’s function is not to perform detailed fixture planning, but to find clamping surface information that will help in determining the setup sequence. This information is important in sequencing because it allows the planner to determine which setups destroy the clamping surfaces required by another. FixtureScout is a component of a larger process planning architecture, MAPP, which explores various ways of re-organizing the decisions and information flow in feature extraction and process planning, for example, by moving Fixture Scouts’ clamping surface analysis to a very early stage of planning. Our goal is to to improve planning effectiveness through these re-organizations.
Octree Based Recognition of Assembly Features
Authors: Sung Raymond C. W. , Corney Jonathan R. , Clark Doug E. R. 
Published: December 2020

Abstract
This paper reviews the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features, these are features which arise from these “spatial” and “contact” face adjacency relationships (known as s- and c-adjacency respectively).
The algorithm uses an octree representation of a B-rep model to support the geometric reasoning required to locate assembly features on disjoint bodies. Once all the adjacent faces which form features have been located, they are used to partition the original faces of the assembly into adjacent and non-adjacent portions. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and at different resolutions. By way of illustration, the algorithm is applied to a trial component.
Multi-Fixture Assembly System Diagnosis Based on Part and Subassembly Measurement Data
Authors: Carlson Johan S. , Lindkvist Lars , Söderberg Rikard 
Published: December 2020

Abstract
A method for multi-fixture assembly system diagnosis is presented. The subassembly dimensional error in the plane of rigidity caused by the fixtures is isolated using both part and subassembly measurement data. The error is explained by either a multiple locator fault in a specified fixture or by a single locator fault in an unspecified fixture. By means of this method the trouble shooting dimensional error efforts in an assembly plant can become more efficient and successful.
Activities in Collaborative Concept Selection Processes for Engineering Design
Authors: Girod Maurice , Elliott Amanda C. , Wright Ian C. , Burns Neil D. 
Published: December 2020

Abstract
The aims of this paper are (i) to introduce a set of activities found to occur during collaborative concept selection processes in engineering design; (ii) to discuss implications of their observed time contributions to such processes; and (iii) to describe the methodology used for this research. We identified a set of 12 main activities of which some were further divided into sub-activities. Their time contributions led us to 5 observations regarding some characteristics of the observed processes. In 3 workshops groups of differently experienced participants were recorded while carrying out concept selection tasks. The recordings were transcribed and a content analysis was conducted to identify the activities. Our findings may be further used as foundation for establishing a descriptive model of concept selection processes in engineering design.
A Performance-Based Representation of Constraint Based Reasoning and Decision Based Design
Authors: Zhu Liang , Kazmer David 
Published: December 2020

Abstract
A performance-based representation is developed utilizing multi-dimensional clipping and transformation algorithms. Given analytic performance functions, three types of supporting information is presented to the decision maker: 1) a function matrix that describes the performance attributes varying with the decision variables; 2) a decision space that illustrates the feasible decision set that meets performance requirements, and; 3) a performance space that provides the feasible performance region and the Pareto Optimal set. The performance-based representation is compatible with other influential design methodologies. A case study is presented for aircraft beam design utilizing constraint based reasoning and decision based design. The results demonstrate not only the benefit of the representation on interactive parametric design, but also an intriguing comparison between constraint based reasoning and decision based design approaches incorporated with the described representation. While a linearized parametric design problem is implemented, the approach shows a potential extension to non-linear systems.
A Proposed Framework for Research in Forming Successful Engineering Design Teams
Authors: Magleby Spencer P. , Roach Gregory M. , Sorensen Carl D. 
Published: December 2020

Abstract
Most engineering design is performed by teams. Team formation from a pool of competent designers and engineers is an important factor in helping determine performance potential. This paper is proposes a framework that we have developed to help guide research, and highlights challenges and obstacles that must be addressed in order to improve results in team formation research. A basic framework is presented and used as a basis for reviewing past work in team formation studies. This framework is then expanded to include explicit decisions about team formation and experimental strategy. Three important aspects of the framework that are critical to obtaining significant results about team formation are discussed: experimental strategy, success measures, and the separation of team formation effects from environmental effects. Recommendations for improvement in these areas are presented.
Guidelines for Experimental Evaluation of Idea Generation Methods
Authors: Shah Jami J. , Kulkarni Santosh V. , Vargas-Hernández Noé 
Published: December 2020

Abstract
Several formal Idea Generation (IG) methods can be found in the design literature ranging from simple methods like brainstorming to complex procedures such as synectics. Experimental evidence is needed to support claims regarding the effectiveness of these methods in promoting idea generation. This paper attempts to prescribe a method for evaluating intuitive idea generation methods. The set of guidelines for experimental evaluation include metrics, how to measure against those metrics (quality, quantity, novelty, variety), how to compute the results, and types of experiments to run. Statistically based Design of Experiments (DOE) principles are in developing the guidelines. Four classes of variables are considered. Two experiment methodologies are proposed. In the Direct Method experiments are conducted using the idea generation method in its entirety. In the Indirect Method, each idea generation method is decomposed into key components and its effectiveness is predicted by experimentally studying the effectiveness of its components and interactions between them.
Validating Design Methods and Research: The Validation Square
Authors: Pedersen Kjartan , Emblemsvåg Jan , Bailey Reid , Allen Janet K. , Mistree Farrokh 
Published: December 2020

Abstract
Validation of engineering research is typically anchored in the scientific inquiry tradition that is based primarily on logical induction and / or deduction. Since much engineering research is based on mathematical modeling, this kind of validation has worked — and still works — very well. There are, however, other areas of engineering research that rely on subjective statements as well as mathematical modeling, which makes this type of validation problematic. One such area is that of design methods within the field of engineering design. In this paper, we explore the question of how one validates design research in general, and design methods in particular.
Being anchored in the scientific inquiry tradition, research validation is strongly tied to a fundamental problem addressed in epistemology, namely, what is scientific knowledge and how is new knowledge confirmed? Thus, we first look to epistemology for answers to why an approach solely based on ‘formal, rigorous and quantifiable’ validation constitutes a problem, and for an augmented approach to research validation. We then propose the ‘Validation Square’ which we validate by testing its internal consistency based on logic in addition to testing its external relevance based on its usefulness with respect to a purpose.
We recognize that no one has the complete answer to the question we pose. To help us converge on an answer to these questions we “think aloud” and invite you to join us in doing the same. It is our hope that in so doing we, the members of this design research community, will all be the richer for it.