A5024135770- Additive Manufacturing and 3D Printing Technologies
- Additive Manufacturing Materials and Processes
- Machine Learning in Materials Science
- Manufacturing Process and Optimization
- Remote Sensing and LiDAR Applications
- Injection Molding Process and Properties
- Metallurgy and Material Forming
- Metal Forming Simulation Techniques
- Digital Transformation in Industry
- Hydrogen embrittlement and corrosion behaviors in metals
- Robotics and Sensor-Based Localization
- Advanced machining processes and optimization
- 3D Surveying and Cultural Heritage
- Sustainable Supply Chain Management
- Mineral Processing and Grinding
- High Entropy Alloys Studies
- Advanced Numerical Analysis Techniques
- Flexible and Reconfigurable Manufacturing Systems
- Titanium Alloys Microstructure and Properties
- Remote Sensing and Land Use
- Drilling and Well Engineering
- Reinforcement Learning in Robotics
- Optical measurement and interference techniques
- Fault Detection and Control Systems
- Elasticity and Material Modeling
Northwestern University
2018-2025
Amazon (United States)
2024-2025
K.N.Toosi University of Technology
2012-2021
Geomatics (Norway)
2020
Plasticity theory aims at describing the yield loci and work hardening of a material under general deformation states. Most its complexity arises from nontrivial dependence on complete strain history microstructure. This motivated 3 ingenious simplifications that underpinned century developments in this field: 1) criteria location; 2) associative or nonassociative flow rules defining direction plastic flow; 3) effective stress-strain laws consistent with equivalence principle. However, 2 key...
Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from computer-aided design model by successively adding material layer layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing well functional parts with complex geometries using processes laser metal deposition would be difficult create traditional machining. As process creating an intricate part expensive Titanium prohibitive respect cost,...
Abstract Discovering manufacturing processes has been largely experienced-based. We propose a shift to systematic approach driven by dependencies between energy inputs and performance outputs. Uncovering these across diverse process classes requires universal language that characterizes performances. Traditional languages, with their individualized syntax terminology, hinder the characterization varying length scales inputs. To enable evaluation of dependencies, we broad facilitates classes,...
Cyber-physical systems (CPS) enable unprecedented communication between product designers and manufacturers. Effective use of these technologies both enables requires a new paradigm methods models to identify the most profitable environmentally friendly production plans for manufacturing network. The operating system cyber-physical (OSCM) paired network operations administration monitoring (NOAM) software are introduced. These guide our development mixed integer bilevel programming (BP)...
Double-Sided Incremental Forming (DSIF) is a subcategory of Sheet (ISF) that utilizes two tools to provide squeezing support achieve improved formability and dimensional accuracy. Due the complexity geometric nonlinearities associated with DSIF, finite element (FE) method suitable analysis tool further analyze process mechanics predict forming forces. However, little research specific DSIF has been conducted in relation identifying critical factors affect accuracy simulation terms In this...
The flexibility of modern manufacturing processes such as additive creates an opportunity to build parts with customized material properties and geometries. However, converting this into functionality requires computational tools that can handle highdimensional design spaces. In work, we present a differentiable simulation method for AM is capable designing time-series laser power optimizes thermal behaviors materials, including the overall history heat treatment time each point. We analyze...
Toolpath optimization of metal-based additive manufacturing processes is currently hampered by the high-dimensionality its design space. In this work, a reinforcement learning platform proposed that dynamically learns toolpath strategies to build an arbitrary part. To end, three prominent model-free formulations are investigated toolpaths and demonstrated for two cases dense sparse reward structures. The results indicate learning-based approach achieves high scores, especially when structure present.
We present a novel computational paradigm for process design in manufacturing processes that incorporates simulation responses to optimize parameters high-dimensional temporal and spatial spaces. developed differentiable finite element analysis framework using automatic differentiation which allows accurate optimization of challenging such as time-series laser power. demonstrate the capability our proposed method through three illustrative case studies additive for: (i) material parameter...
Abstract. Today, three dimensional modeling of objects is considered in many applications such as documentation ancient heritage, quality control, reverse engineering and animation In this regard, there are a variety methods for producing three-dimensional models. paper, 3D system developed based on photogrammetry method using image processing laser line extraction from images. the beam profile radiated body object with video acquisition, frames, coordinates can be achieved. first design...
Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from computer-aided design model by successively adding material layer layer. AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing well functional parts with complex geometries using processes laser metal deposition would be difficult create traditional machining. As process creating an intricate part expensive Titanium prohibitive respect cost,...