83 citations · 287 across the 11 of their papers we have counts for
12 papers
PATO: Producibility-Aware Topology Optimization using Deep Learning for Metal Additive Manufacturing
Naresh S. Iyer, Amir M. Mirzendehdel, Sathyanarayanan Raghavan +5
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…
Topology Optimization for Manufacturing with Accessible Support Structures
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
Metal additive manufacturing (AM) processes often fabricate a near-net shape that includes the as-designed part as well as the sacrificial support structures that need to be machin…
Optimizing Build Orientation for Support Removal using Multi-Axis Machining
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
Parts fabricated by additive manufacturing (AM) are often fabricated first as a near-net shape, a combination of intended nominal geometry and sacrificial support structures, which…
Sliding Basis Optimization for Heterogeneous Material Design
Nurcan Gecer Ulu, Svyatoslav Korneev, Erva Ulu +1
We present the sliding basis computational framework to automatically synthesize heterogeneous (graded or discrete) material fields for parts designed using constrained optimizatio…
Topology Optimization with Accessibility Constraint for Multi-Axis Machining
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
In this paper, we present a topology optimization (TO) framework to enable automated design of mechanical components while ensuring the result can be manufactured using multi-axis…
Manufacturability Oriented Model Correction and Build Direction Optimization for Additive Manufacturing
Erva Ulu, Nurcan Gecer Ulu, Walter Hsiao +1
We introduce a method to analyze and modify a shape to make it manufacturable for a given additive manufacturing (AM) process. Different AM technologies, process parameters or mate…
Exploring Feasible Design Spaces for Heterogeneous Constraints
Amir M. Mirzendehdel, Morad Behandish, Saigopal Nelaturi
We demonstrate an approach of exploring design spaces to simultaneously satisfy kinematics- and physics-based requirements. We present a classification of constraints and solvers t…
Automated Process Planning for Turning: A Feature-Free Approach
Morad Behandish, Saigopal Nelaturi, Chaman Singh Verma +1
Turning is the most commonly available and least expensive machining operation, in terms of both machine-hour rates and tool insert prices. A practical CNC process planner has to m…
A Classification of Topological Discrepancies in Additive Manufacturing
Morad Behandish, Amir M. Mirzendehdel, Saigopal Nelaturi
Additive manufacturing (AM) enables enormous freedom for design of complex structures. However, the process-dependent limitations that result in discrepancies between as-designed a…
Automatic Support Removal for Additive Manufacturing Post Processing
Saigopal Nelaturi, Morad Behandish, Amir M. Mirzendehdel +1
An additive manufacturing (AM) process often produces a {\it near-net} shape that closely conforms to the intended design to be manufactured. It sometimes contains additional suppo…
Automated Process Planning for Hybrid Manufacturing
Morad Behandish, Saigopal Nelaturi, Johan de Kleer
Hybrid manufacturing (HM) technologies combine additive and subtractive manufacturing (AM/SM) capabilities, leveraging AM's strengths in fabricating complex geometries and SM's pre…
Recognizing Abnormal Heart Sounds Using Deep Learning
Jonathan Rubin, Rui Abreu, Anurag Ganguli +3
The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound cl…