Design Exploration of Reliably Manufacturable Materials and Structures With Applications to Negative Stiffness Metamaterials and Microstereolithography1

2018 ◽  
Vol 140 (11) ◽  
Author(s):  
Clinton Morris ◽  
Logan Bekker ◽  
Michael R. Haberman ◽  
Carolyn C. Seepersad
Keyword(s):  
Material Properties ◽  
Matrix Material ◽  
Negative Stiffness ◽  
Mechanical Stiffness ◽  
Design Exploration ◽  
Exploration Process ◽  
Performance Targets ◽  
Bayesian Network Classifiers ◽  
Performance Requirements ◽  
Base Matrix

One of the challenges in designing metamaterials for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer's perspective, these differences can lead to degradation of part and metamaterial performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturing variation into the design exploration process and identify designs that reliably meet performance requirements when this variation is taken into account. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of NS inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. This variability is measured and modeled via design, fabrication, and characterization of metrology parts. The quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.

Design Exploration of Reliably Manufacturable Materials and Structures With Applications to a Microstereolithography System

2018 ◽  
Author(s):  
Clinton Morris ◽  
Carolyn C. Seepersad
Keyword(s):  
Material Properties ◽  
Probability Distributions ◽  
Matrix Material ◽  
Joint Probability ◽  
Negative Stiffness ◽  
Mechanical Stiffness ◽  
Design Exploration ◽  
Exploration Process ◽  
Performance Targets ◽  
Bayesian Network Classifiers

One of the challenges in designing for additive manufacturing (AM) is accounting for the differences between as-designed and as-built geometries and material properties. From a designer’s perspective, these differences can lead to degradation of part performance, which is especially difficult to accommodate in small-lot or one-of-a-kind production. In this context, each part is unique, and therefore, extensive iteration is costly. Designers need a means of exploring the design space while simultaneously considering the reliability of additively manufacturing particular candidate designs. In this work, a design exploration approach, based on Bayesian network classifiers (BNC), is extended to incorporate manufacturability explicitly into the design exploration process. The example application is the design of negative stiffness (NS) metamaterials, in which small volume fractions of negative stiffness (NS) inclusions are embedded within a host material. The resulting metamaterial or composite exhibits macroscopic mechanical stiffness and loss properties that exceed those of the base matrix material. The inclusions are fabricated with microstereolithography with features on the scale of tens of microns, but variability is observed in material properties and dimensions from specimen to specimen. In this work, the manufacturing variability of critical features of a NS inclusion fabricated via microstereolithography are characterized experimentally and modelled mathematically. Specifically, the variation in the geometry of the NS inclusions and the Young’s modulus of the photopolymer are measured and modeled by both nonparametric and parametric joint probability distributions. Finally, the quantified manufacturing variability is incorporated into the BNC approach as a manufacturability classifier to identify candidate designs that achieve performance targets reliably, even when manufacturing variability is taken into account.

Hierarchical Design of Composite Materials With Negative Stiffness Inclusions Using a Bayesian Network Classifier

2013 ◽  
Author(s):  
Jordan Matthews ◽  
Timothy Klatt ◽  
Carolyn C. Seepersad ◽  
Michael Haberman ◽  
David Shahan
Keyword(s):  
Composite Materials ◽  
Bayesian Network ◽  
Hierarchical Modeling ◽  
Effective Properties ◽  
Material Model ◽  
Negative Stiffness ◽  
Mechanical Stiffness ◽  
High Loss ◽  
High Stiffness ◽  
Bayesian Network Classifiers

Recent research in the field of composite materials has shown that it is theoretically possible to produce composite materials with macroscopic mechanical stiffness and loss properties that surpass those of conventional composites. This research explores the possibility of designing and fabricating these composite materials by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design, however, is a nontrivial task. This paper presents a hierarchical multiscale material model for these materials, coupled with a set-based, multilevel design approach based on Bayesian network classifiers. Bayesian network classifiers are used to map promising regions of the design space at each hierarchical modeling level, and then the maps are intersected to identify sets of multilevel or multiscale solutions that are likely to provide desirable system performance. Length scales range from the behavior of the structured microscale negative stiffness inclusions to the effective properties of mesoscale composite materials to the performance of an illustrative macroscale component — a vibrating beam coated with the high stiffness, high loss composite material.

Hierarchical Design of Negative Stiffness Metamaterials Using a Bayesian Network Classifier1

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Jordan Matthews ◽  
Timothy Klatt ◽  
Clinton Morris ◽  
Carolyn C. Seepersad ◽  
Michael Haberman ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Hierarchical Modeling ◽  
Effective Properties ◽  
Negative Stiffness ◽  
Mechanical Stiffness ◽  
Design Problems ◽  
Length Scales ◽  
Bayesian Network Classifiers ◽  
Design Variables ◽  
Modeling Level

A set-based approach is presented for exploring multilevel design problems. The approach is applied to design negative stiffness metamaterials with mechanical stiffness and loss properties that surpass those of conventional composites. Negative stiffness metamaterials derive their properties from their internal structure, specifically by embedding small volume fractions of negative stiffness inclusions in a continuous host material. Achieving high stiffness and loss from these materials by design involves managing complex interdependencies among design variables across a range of length scales. Hierarchical material models are created for length scales ranging from the structure of the microscale negative stiffness inclusions to the effective properties of mesoscale metamaterials to the performance of an illustrative macroscale component. Bayesian network classifiers (BNCs) are used to map promising regions of the design space at each hierarchical modeling level, and the maps are intersected to identify sets of multilevel solutions that are likely to provide desirable system performance. The approach is particularly appropriate for highly efficient, top-down, performance-driven, multilevel design, as opposed to bottom-up, trial-and-error multilevel modeling.

Microstructural Characteristics and Mechanical Behaviour of AA7075/TiB2 Composite

2020 ◽  
Vol 979 ◽  
pp. 40-46 ◽  
Author(s):  
P. Loganathan ◽  
A. Gnanavelbabu ◽  
K. Rajkumar ◽  
S. Ayyanar
Keyword(s):  
Mechanical Properties ◽  
Matrix Material ◽  
Titanium Boride ◽  
Stir Casting ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
Microstructural Characteristics ◽  
Xrd Pattern ◽  
Base Matrix ◽  
Aa 7075

Titanium Boride (TiB2) particles reinforced with aluminum alloy (AA 7075) composites were developed using the two-step stir casting method. TiB2 with aluminium alloy was varied in 5, 10, 15 weight percentages (wt.%) . The mechanical properties of the composites were assessed through density, hardness, tensile and impact. Factography observations were also evaluated with Scanning Electron Microscopy (SEM) and phase identification of the composite was carried out through X-ray diffraction technique (XRD). The XRD pattern of alloy and composites revealed peaks of Al and TiB2 particles and the intensity of TiB2 particles increased with increase in wt. %. Compared to the base matrix, the density and hardness of composites increased with the wt. % of TiB2. Addition of TiB2 particles exhibited grain refinement, thereby improving the mechanical properties. Composite materials exhibited high load bearing capacity due to the strong bonding of TiB2 and matrix material resulting in increased impact energy. The tensile strength of the composite increased with increasing wt. % of reinforcement. The failure in the composites observed were dimpled structure and ridges, voids, and cracks.

Multiple Target Exploration Approach for Design Exploration Using a Swarm Intelligence and Clustering

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Hyeongmin Han ◽  
Sehyun Chang ◽  
Harrison Kim
Keyword(s):  
Design Problem ◽  
Optimization Problems ◽  
Complete Solution ◽  
Solution Space ◽  
Analytical Models ◽  
Target Point ◽  
Design Exploration ◽  
Exploration Process ◽  
Engineering Design Problems

In engineering design problems, performance functions evaluate the quality of designs. Among the designs, some of them are classified as good designs if responses from performance functions satisfy a target point or range. An infinite set of good designs in the design space is defined as a solution space of the design problem. In practice, since the performance functions are analytical models or black-box simulations which are computationally expensive, it is difficult to obtain a complete solution space. In this paper, a method that finds a finite set of good designs, which is included in a solution space, is proposed. The method formulates the problem as optimization problems and utilizes gray wolf optimizer (GWO) in the way of design exploration. Target points of the exploration process are defined by clustering intermediate solutions for every iteration. The method is tested with a simple two-dimensional problem and an automotive vehicle design problem to validate and check the quality of solution points.

Design and Simulation of a Compact Electromagnetic Actuator for the Synchronized Segmentally Interchanging Pulley Transmission System (SSIPTS)

2011 ◽  
Author(s):  
Peter Baoping Wen ◽  
Vahid Mashatan ◽  
Jean W. Zu
Keyword(s):  
Material Properties ◽  
Transmission System ◽  
Comsol Multiphysics ◽  
Electromagnetic Actuator ◽  
Optimized Design ◽  
The Novel ◽  
Minimum Mass ◽  
Performance Requirements ◽  
Fea Modeling ◽  
Simulation Results

This paper presents a practical approach of FEA modeling and optimization of the design of the compact electromagnetic actuator. This special actuator is designed to perform the gear shifts in the synchronized segmentally interchanging pulley transmission system (SSIPTS.) The geometry and material properties of the actuator, which are confined by the assembling space and running condition of SSIPTS, are parametrically optimized by using FEA package, Comsol Multiphysics. The current density of coil, the geometric parameters of magnet, and the permeability of structural materials are major control variables in the optimization of the novel actuator. The target of the optimization process is to find the maximum electromagnetic force and the minimum mass of the entire device within the available space in the transmission package. The simulation results for the optimized design are presented and further compared with the performance requirements of the actuator.

Influence of ZrO2 nano particles on the behavior of mechanical and tribological properties of the AA7075 composite

2020 ◽  
pp. 239779142098152
Author(s):  
MS Prashanth Reddy ◽  
HP Raju ◽  
Nagaraj R Banapurmath ◽  
Vinod Kumar V Meti
Keyword(s):  
Matrix Material ◽  
Physical And Mechanical Properties ◽  
High Strength ◽  
Matrix Alloy ◽  
Stir Casting ◽  
Nano Particles ◽  
Test Results ◽  
Casting Technique ◽  
Mechanical And Tribological Properties ◽  
Base Matrix

A well-known AA7075 alloy used for most of the structural, aerospace, and automobile applications due to its excellent properties such as high strength, corrosion-resistant, and low density. To encourage industrialists, the physical and mechanical properties of the composite has to improve by reinforcing hard ceramic particles. In this investigation varying wt.% of hard ZrO2 (zirconium dioxide) particles (0.75, 1, 1.25, 1.5, 1.75, and 2 wt.%) are reinforced in AA7075 matrix alloy to form a composite. Motorized stir casting technique induced to distribute reinforcement particles homogeneously. The SEM micrographs reveal that uniform distribution of ZrO2 particles can be achieved after inducing motorized stir casting technique into the molten composite. The experimental test results revealed that the addition of ZrO2 particles enhanced the hardness and tensile strength of the AA7075/ZrO2 composite as compared to base matrix material. Among all composites, AA7075/1.5ZrO2 show higher hardness and strength.

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