Hybrid General Heuristic Gradient Projection for Frame Optimization of Micro and Macro Applications

In this paper, a generalization is suggested for the Heuristic Gradient Projection method. The previous Heuristic Gradient Projection method (HGP) has been developed for 3D-frame design and optimization. It mainly employed bending stress relations in order to simplify the process of iterations for stress constrained optimization. The General Heuristic Gradient Projection (GHGP) is used in a more general form to satisfy the stress constraints. Another direct search method is hybridized to satisfy other constraints on deflection. Two examples are solved using the new method. The proposed method is compared with the Hybrid Fuzzy Heuristic technique (FHGP) when solving a MEMS resonator. Results showed that the proposed hybrid technique with (GHGP) converges to the optimum solutions faster by an 8%. The MEMS weight is also decreased by 23.7%. For a macro level, the GHGP improved the solution time by 33.3%. The hybrid technique with (GHGP) improved the stresses in the members of the optimum ten-member cantilever.

Interval Uncertainty Reduction and Single-Disciplinary Sensitivity Analysis With Multi-Objective Optimization

Sensitivity analysis has received significant attention in engineering design. While sensitivity analysis methods can be global, taking into account all variations, or local, taking into account small variations, they generally identify which uncertain parameters are most important and to what extent their effect might be on design performance. The extant methods do not, in general, tackle the question of which ranges of parameter uncertainty are most important or how to best allocate investments to partial uncertainty reduction in parameters under a limited budget. More specifically, no previous approach has been reported that can handle single-disciplinary multi-output global sensitivity analysis for both a single design and multiple designs under interval uncertainty. Two new global uncertainty metrics, i.e., radius of output sensitivity region and multi-output entropy performance, are presented. With these metrics, a multi-objective optimization model is developed and solved to obtain fractional levels of parameter uncertainty reduction that provide the greatest payoff in system performance for the least amount of “investment”. Two case studies of varying difficulty are presented to demonstrate the applicability of the proposed approach.

Optimized Mask Image Projection for Solid Freeform Fabrication

Solid freeform fabrication (SFF) processes based on mask image projection have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on these processes. For the SFF processes, the mask image planning is an important process planning step. In this paper, we present an optimization based method for mask image planning. It is based on a light intensity blending technique called pixel blending. By intelligently controlling pixels’ gray scale values, the SFF processes can achieve a much higher XY resolution and accordingly better part quality. We mathematically define the pixel blending problem and discuss its properties. Based on the formulation, we present several optimization models for solving the problem including a mixed integer programming model, a linear programming model, and a two-stage optimization model. Both simulated and physical experiments for various CAD models are presented to demonstrate the effectiveness and efficiency of our method.

Early Design Modeling and Simulation of Behaviors: Case Study of Mobile Work Machine

The early evaluation of a proposed function structure for a product and also, the possibility to expose the potential failures related to this provides that the design process can be modeled in its entirety. However, so far there are no existed suitable models for the early phase of design process. This article presents an integrated approach aimed to explore the behaviors of concept designs in the early design phase. The approach is founded on a combination of Petri net, π-numbers, qualitative physics principles and Design Structure Matrix. The final aim is to implement this method on the SysML modeling language to integrate a simulation approach that is initially not standardized in the language. A second interest of the approach is to provide a coherent simulation framework that can be used as a reference to verify the coherency of other simulation models further in the design process.

A Modified Reliability Index Approach for Reliability-Based Design Optimization

RBDO problems have been intensively studied for many decades. Since Hasofer and Lind defined a measure of the second-moment reliability index, many RBDO methods utilizing the concept of reliability index have been introduced as the Reliability Index Approach (RIA). In the RIA, a reliability analysis problem is formulated to find the reliability index for each performance constraint and the solutions are used to evaluate the failure probability. However, the traditional RIA suffers from inefficiency and convergence problems. In this paper, we revisited the definition of the reliability index and revealed the convergence problem in the traditional RIA. Furthermore, a new definition of the reliability index is proposed to correct this problem and a modified Reliability Index Approach based on this definition is developed. Numerical examples using both the traditional RIA and the modified RIA are compared and discussed.

Learning Geometric Design Knowledge From Conceptual Sketches and Its Utilization in Shape Creation and Optimization

In current product design, significant effort is put into creating aesthetically pleasing product forms. Often times, the final shape evolves in time based on designers’ ideas externalized through early design activities primarily involving conceptual sketches. While designers negotiate and convey a multitude of different ideas through such informal activities, current computational tools are not well suited to work from such forms of information to leverage downstream design processes. As a result, many promising ideas either remain under-explored, or require restrictive added effort to be transformed into digital media. As one step toward alleviating this difficulty, we propose a new computational method for capturing and reusing knowledge regarding the shape of a developing design from designers’ hand-drawn conceptual sketches. At the heart of our approach is a geometric learning method that involves constructing a continuous space of meaningful shapes via a deformation analysis of the constituent exemplars. The computed design space serves as a medium for encoding designers’ shape preferences expressed through their sketches. With the proposed approach, designers can record desirable shape ideas in the form of raw sketches, while utilizing the accumulated information to create and explore novel shapes in the future. A key advantage of the proposed system is that it enables prescribed engineering and ergonomic criteria to be concurrently considered with form design, thus allowing such information to suitably guide conceptual design processes in a timely manner.

A Context-Aware Information Model for Elderly Homecare Services in a Smart Home

One of the critical situations facing the society across the globe is the problem of elderly homecare services (EHS) due to the aggravation of the society coupled with diseases and limited social resources. This problem has been typically dealt with by manual assistance from caregivers and/or family members. The emerging Ambience Intelligence (AmI) technology suggests itself to be of great potential for EHS applications, owing to its strength in constructing a pervasive computing environment that is sensitive and responsive to the presence of human users. The key challenge of AmI implementation lies in context awareness, namely how to align with the specific decision making scenarios of particular EHS applications. This paper proposes a context-aware information model in a smart home to tackle the EHS problem. Mainly, rough set theory is applied to construct user activity models for recognizing various activities of daily living (ADLs) based on the sensor platform constructed in a smart home environment. Subsequently, issues of case comprehension and homecare services are also discussed. A case study in the smart home environment is presented. Initial findings from the case study suggest the importance of the research problem, as well as the feasibility and potential of the proposed framework.

Design Optimization of a Special Class of Steel Structures via Genetic Algorithms and Stochastic Sampling

This paper addresses the design optimization of a special class of steel structures, which is clear-span building built up via off-shelf standard steel-sections. The problem is of particular importance in small to medium span buildings due to an attractive opportunity for reduction of the manufacturing cost compared to trusses and custom-built beams. The problem is also difficult from an optimization perspective as it exhibits both continuous and discrete variables, as well as discontinuities and flat regions in the topology of the objective function. Genetic algorithms (GA) and a special stochastic sampling technique are considered for the problem, as well as a mixed GA and stochastic sampling approach. The stochastic sampling is guided via heuristic rules based on knowledge specific to the problem, and is thus perceived well suited to the optimization task. While all the tested algorithms produced satisfactory results, the mixed approach seemed to yield the most consistent performance.

Numerical Experiments in Using Voronoi Cell Finite Elements for Topology Optimization

This paper provides two separate methodologies for implementing the Voronoi Cell Finite Element Method (VCFEM) in topological optimization. Both exploit two characteristics of VCFEM. The first approach utilizes the property that a hole or inclusion can be placed in the element: the design variables for the topology optimization are sizes of the hole. In the second approach, we note that VCFEM may mesh the design domain as n sided polygons. We restrict our attention to hexagonal meshes of the domain while applying Solid Isotropic Material Penalization (SIMP) material model. Researchers have shown that hexagonal meshes are not subject to the checker boarding problem commonly associated with standard linear quad and triangle elements. We present several examples to illustrate the efficacy of the methods in compliance minimization as well as discuss the advantages and disadvantages of each method.

Port-Based Ontology Semantic Similarities for Module Concept Creation

The modularity indicates a one-to-one mapping between functional concepts and physical components. It can allow us to generate more product varieties at lower costs. Functional concepts can be described by precise syntactic structures with functional terms. Different semantic measures can be used to evaluate the strength of the semantic link between two functional concepts from port ontology. In this paper, different methods of modularity based on ontology are first investigated. Secondly, the primitive concepts are presented based on port ontology by using natural language, and then their semantic synthesis is used to describe component ontology. The taxonomy of port-based ontology are built to map the component connections and interactions in order to build functional blocks. Next, propose an approach to computing semantic similarity by mapping terms to functional ontology and by examining their relationships based on port ontology language. Furthermore, several modules are partitioned on the basis of similarity measures. The process of module construction is described and its elements are related to the similarity values between concepts. Finally, a case is studied to show the efficiency of port ontology semantic similarity for modular concept generation.