Search Results for author: Morad Behandish

Found 10 papers, 0 papers with code

Model Consistency for Mechanical Design: Bridging Lumped and Distributed Parameter Models with A Priori Guarantees

no code implementations11 May 2023 Randi Wang, Vadim Shapiro, Morad Behandish

Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs).

Computational Efficiency

Hybrid Manufacturing Process Planning for Arbitrary Part and Tool Shapes

no code implementations24 May 2022 George P. Harabin, Morad Behandish

We present a general framework for identifying AM/SM actions that make up an HM process plan based on accessibility and support requirements, using morphological operations that allow for arbitrary part and tool geometries to be considered.


FRC-TOuNN: Topology Optimization of Continuous Fiber Reinforced Composites using Neural Network

no code implementations7 May 2022 Aaditya Chandrasekhar, Amir Mirzendehdel, Morad Behandish, Krishnan Suresh

In contrast, we propose a mesh-independent representation based on a neural network (NN) both to capture the matrix topology and fiber distribution.

Computational Efficiency

Accelerating Part-Scale Simulation in Liquid Metal Jet Additive Manufacturing via Operator Learning

no code implementations2 Feb 2022 Søren Taverniers, Svyatoslav Korneev, Kyle M. Pietrzyk, Morad Behandish

Predicting part quality for additive manufacturing (AM) processes requires high-fidelity numerical simulation of partial differential equations (PDEs) governing process multiphysics on a scale of minimum manufacturable features.

Operator learning

AI Research Associate for Early-Stage Scientific Discovery

no code implementations2 Feb 2022 Morad Behandish, John Maxwell III, Johan de Kleer

Artificial intelligence (AI) has been increasingly applied in scientific activities for decades; however, it is still far from an insightful and trustworthy collaborator in the scientific process.

Surrogate Modeling for Physical Systems with Preserved Properties and Adjustable Tradeoffs

no code implementations2 Feb 2022 Randi Wang, Morad Behandish

The latter generates interpretable surrogate models by fitting artificial constitutive relations to a presupposed topological structure using experimental or simulation data.

PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing

no code implementations8 Dec 2021 Naresh S. Iyer, Amir M. Mirzendehdel, Sathyanarayanan Raghavan, Yang Jiao, Erva Ulu, Morad Behandish, Saigopal Nelaturi, Dean M. Robinson

In this paper, we propose PATO-a producibility-aware topology optimization (TO) framework to help efficiently explore the design space of components fabricated using metal additive manufacturing (AM), while ensuring manufacturability with respect to cracking.

Automated Process Planning for Hybrid Manufacturing

no code implementations18 May 2018 Morad Behandish, Saigopal Nelaturi, Johan de Kleer

A multimodal HM process plan is represented by a finite Boolean expression of AM and SM manufacturing primitives, such that the expression evaluates to an 'as-manufactured' artifact.

Collision Avoidance Computational Efficiency

Shape Complementarity Analysis for Objects of Arbitrary Shape

no code implementations1 Dec 2017 Morad Behandish, Horea T. Ilies

The basic problem of shape complementarity analysis appears fundamental to applications as diverse as mechanical design, assembly automation, robot motion planning, micro- and nano-fabrication, protein-ligand binding, and rational drug design.

Motion Planning

Concurrent Pump Scheduling and Storage Level Optimization Using Meta-Models and Evolutionary Algorithms

no code implementations14 Nov 2017 Morad Behandish, Zheng Yi Wu

In spite of the growing computational power offered by the commodity hardware, fast pump scheduling of complex water distribution systems is still a challenge.

Evolutionary Algorithms Scheduling

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