Papers by Saigopal Nelaturi
ArXiv, 2021
In this paper, we propose PATO—a producibility-aware topology optimization (TO) framework to help... more 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. Specifically, parts fabricated through Laser Powder Bed Fusion (LPBF) are prone to defects such as warpage or cracking due to high residual stress values generated from the steep thermal gradients produced during the build process. Maturing the design for such parts and planning their fabrication can span months to years, often involving multiple handoffs between design and manufacturing engineers. PATO is based on the a priori discovery of crack-free designs, so that the optimized part can be built defect-free at the outset. To ensure that the design is crack free during optimization, producibility is explicitly encoded within the standard formulation of TO, using a crack index. Multiple crack indices are explored and using ex...
Towards printing as an electronics manufacturing method: Micro-scale chiplet position control
2017 American Control Conference (ACC), 2017
We address the problem of position control of micro-chips (chiplets) immersed in dielectric fluid... more We address the problem of position control of micro-chips (chiplets) immersed in dielectric fluid. An electric field, shaped by controlling the voltages of spiral shaped electrodes, is used to reliably and accurately transport and position chiplets using dielectrophoretic forces. A lumped, capacitive based (nonlinear) motion model is used to generate an open loop control policy. The open loop policy is generated using a one step model predictive control approach. By exploiting the spatial symmetry and periodicity of the open loop control solution, a real-time control scheme is designed by applying simple algebraic operations to a base function defined on a finite domain. The chiplet position is tracked using image processing algorithms. We demonstrate the validity of our approach by describing an experimental result, where real-time control is used to move a chiplet for 1000µm in a controlled manner.
CAD Special Issue Editorial: Process Planning for Additive and Hybrid Manufacturing
Comput. Aided Des., 2021
ArXiv, 2017
The work presented here applies deep learning to the task of automated cardiac auscultation, i.e.... more 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 classification algorithm that combines the use of time-frequency heat map representations with a deep convolutional neural network (CNN). Given the cost-sensitive nature of misclassification, our CNN architecture is trained using a modified loss function that directly optimizes the trade-off between sensitivity and specificity. We evaluated our algorithm at the 2016 PhysioNet Computing in Cardiology challenge where the objective was to accurately classify normal and abnormal heart sounds from single, short, potentially noisy recordings. Our entry to the challenge achieved a final specificity of 0.95, sensitivity of 0.73 and overall score of 0.84. We achieved the greatest specificity score out of all challenge entries and, using just a single CNN, our algorithm differed in overall score by only 0.02 compared ...
SPM 2021 Editorial
Computer-Aided Design, 2022
Computer-Aided Design, 2022
Metal additive manufacturing (AM) processes often fabricate a near-net shape that includes the as... more 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 machined away by subtractive manufacturing (SM), for instance multi-axis machining. Thus, although AM is capable of generating highly complex parts, the limitations of SM due to possible collision between the milling tool and the workpiece can render an optimized part non-manufacturable. We present a systematic approach to topology optimization (TO) of parts for AM followed by SM to ensure removability of support structures, while optimizing the part's performance. A central idea is to express the producibility of the part from the near-net shape in terms of accessibility of every support structure point using a given set of cutting tool assemblies and fixturing orientations. Our approach does not impose any artificial constraints on geometric complexity of the part, support structures, machining tools, and fixturing devices. We extend the notion of inaccessibility measure field (IMF) to support structures to identify the inaccessible points and capture their contributions to non-manufacturability by a continuous spatial field. IMF is then augmented to the sensitivity field to guide the TO towards a manufacturable design. The approach enables efficient and effective design space exploration by finding nontrivial complex designs whose near-net shape can be 3D printed and post-processed for support removal by machining with a custom set of tools and fixtures. We demonstrate the efficacy of our approach on nontrivial examples in 2D and 3D.
2016 Computing in Cardiology Conference (CinC), 2016
We describe the development of an algorithm for the automatic classification of heart sound phono... more We describe the development of an algorithm for the automatic classification of heart sound phonocardiogram waveforms as normal, abnormal or uncertain. Our approach consists of three major components: 1) Heart sound segmentation, 2) Transformation of one-dimensional waveforms into two-dimensional timefrequency heat map representations using Mel-frequency cepstral coefficients and 3) Classification of MFCC heat maps using deep convolutional neural networks. We applied the above approach to produce submissions for the 2016 PhysioNet Computing in Cardiology Challenge. We present results from the challenge, as well as describe in detail the resulting neural network architecture produced and design decisions made.
Computer-Aided Design, 2020
We present the sliding basis computational framework to automatically synthesize heterogeneous (g... more We present the sliding basis computational framework to automatically synthesize heterogeneous (graded or discrete) material fields for parts designed using constrained optimization. Our framework uses the fact that any spatially varying material field over a given domain may be parameterized as a weighted sum of the Laplacian eigenfunctions. We bound the parameterization of all material fields using a small set of weights to truncate the Laplacian eigenfunction expansion, which enables efficient design space exploration with the weights as a small set of design variables. We further improve computational efficiency by using the property that the Laplacian eigenfunctions form a spectrum and may be ordered from lower to higher frequencies. Starting the optimization with a small set of weighted lower frequency basis functions we iteratively include higher frequency bases by sliding a window over the space of ordered basis functions as the optimization progresses. This approach allows greater localized control of the material distribution as the sliding window moves through higher frequencies. The approach also reduces the number of optimization variables per iteration, thus the design optimization process speeds up independent of the domain resolution without sacrificing analysis quality. While our method is useful for problems where analytical gradients are available, it is most beneficial when the gradients may not be computed easily (i.e., optimization problems coupled with external black-box analysis) thereby enabling optimization of otherwise intractable design problems. The sliding basis framework is independent of any particular physics analysis, objective and constraints, providing a versatile and powerful design optimization tool for various applications. We demonstrate our approach on graded solid rocket fuel design and multi-material topology optimization applications and evaluate its performance.
Computer-Aided Design, 2020
We present an approach to map Additive Manufacturing (AM) process parameters and a given tool pat... more We present an approach to map Additive Manufacturing (AM) process parameters and a given tool path to a representation of the as-manufactured shape that captures machine-specific manufacturing uncertainty. Multi-physics models that capture the deposition process at the smallest manufacturing scale are solved to accurately simulate local material accumulation. A surrogate model for the multiphysics simulation is used to practically simulate the material accumulation by locally varying the spatial distribution of material along the tool path. This generates a training set representing a variational class of as-manufactured shapes. Machine specific manufacturing uncertainty is then represented as a 3D kernel obtained by deconvolving the simulated as-printed shape with the tool path. This kernel provides a good estimate of the probability of local material accumulation independent of the chosen part and tool-path. Convolution of the kernel with a tool-path combined with an appropriate super-level-set of the resulting field provides a computationally efficient way to estimate the as-manufactured shape of AM parts. The efficiency results from the highly parallelized implementation of convolution on the GPU. We demonstrate high-resolution shape estimation and visualization of as-printed parts constructed using this approach. We validate the method using data generated by simulating a build process for droplet-based AM, by performing model order reduction of a system of partial differential equations for the 3D Navier-Stokes multiphase flows coupled with heat-transfer and phase change.
Computer-Aided Design, 2020
In this paper, we present a topology optimization (TO) framework to enable automated design of me... more 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 machining. Although TO improves the part's performance, the as-designed model is often geometrically too complex to be machined and the as-manufactured model can significantly vary due to machining constraints that are not accounted for during TO. In other words, many of the optimized design features cannot be accessed by a machine tool without colliding with the part (or fixtures). The subsequent post-processing to make the part machinable with the given setup requires trial-and-error without guarantees on preserving the optimized performance. Our proposed approach is based on the well-established accessibility analysis formulation using convolutions in configuration space that is extensively used in spatial planning and robotics. We define an inaccessibility measure field (IMF) over the design domain to identify non-manufacturable features and quantify their contribution to non-manufacturability. The IMF is used to penalize the sensitivity field of performance objectives and constraints to prevent formation of inaccessible regions. Unlike existing discrete formulations, our IMF provides a continuous spatial field that is desirable for TO convergence. Our approach applies to arbitrary geometric complexity of the part, tools, and fixtures, and is highly parallelizable on multi-core architecture. We demonstrate the effectiveness of our framework on benchmark and realistic examples in 2D and 3D. We also show that it is possible to directly construct manufacturing plans for the optimized designs based on the accessibility information.
Journal of Mechanical Design, 2019
We introduce a method to analyze and modify a shape to make it manufacturable for a given additiv... more 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 materials introduce geometric constraints on what is manufacturable or not. Given an input 3D model and minimum printable feature size dictated by the manufacturing process characteristics and parameters, our algorithm generates a corrected geometry that is printable with the intended AM process. A key issue in model correction for manufacturability is the identification of critical features that are affected by the printing process. To address this challenge, we propose a topology aware approach to construct the allowable space for a print head to traverse during the 3D printing process. Combined with our build orientation optimization algorithm, the amount of modifications performed on the shape is kept at minimum while providing an accurate approximation of the as-manufactured part. We demonstrate our meth...
Production & Manufacturing Research, 2019
Turning is the most commonly available and least expensive machining operation, in terms of both ... more 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 maximize the utilization of turning, not only to attain precision requirements for turnable surfaces, but also to minimize the machining cost, while non-turnable features can be left for other processes such as milling. Most existing methods rely on separation of surface features and lack guarantees when analyzing complex parts with interacting features. In a previous study, we demonstrated successful implementation of a feature-free milling process planner based on configuration space methods used for spatial reasoning and AI search for planning. This paper extends the feature-free method to include turning process planning. It opens up the opportunity for seamless integration of turning actions into a millturn process planner that can handle arbitrarily complex shapes with or without a priori knowledge of feature semantics.
Computer-Aided Design, 2019
An additive manufacturing (AM) process often produces a near-net shape that closely conforms to t... more An additive manufacturing (AM) process often produces a near-net shape that closely conforms to the intended design to be manufactured. It sometimes contains additional support structure (also called scaffolding), which has to be removed in post-processing. We describe an approach to automatically generate process plans for support removal using a multi-axis machining instrument. The goal is to fracture the contact regions between each support component and the part, and to do it in the most cost-effective order while avoiding collisions with evolving near-net shape, including the remaining support components. A recursive algorithm identifies a maximal collection of support components whose connection regions to the part are accessible as well as the orientations at which they can be removed at a given round. For every such region, the accessible orientations appear as a 'fiber' in the collision-free space of the evolving near-net shape and the tool assembly. To order the removal of accessible supports, the algorithm constructs a search graph whose edges are weighted by the Riemannian distance between the fibers. The least expensive process plan is obtained by solving a traveling salesman problem (TSP) over the search graph. The sequence of configurations obtained by solving TSP is used as the input to a motion planner that finds collision free paths to visit all accessible features. The resulting part without the support structure can then be finished using traditional machining to produce the intended design. The effectiveness of the method is demonstrated through benchmark examples in 3D.
Computer-Aided Design, 2019
Additive manufacturing (AM) enables enormous freedom for design of complex structures. However, t... more Additive manufacturing (AM) enables enormous freedom for design of complex structures. However, the processdependent limitations that result in discrepancies between as-designed and as-manufactured shapes are not fully understood. The tradeoffs between infinitely many different ways to approximate a design by a manufacturable replica are even harder to characterize. To support design for AM (DfAM), one has to quantify local discrepancies introduced by AM processes, identify the detrimental deviations (if any) to the original design intent, and prescribe modifications to the design and/or process parameters to countervail their effects. Our focus in this work will be on topological analysis. There is ample evidence in many applications that preserving local topology (e.g., connectivity of beams in a lattice) is important even when slight geometric deviations can be tolerated. We first present a generic method to characterize local topological discrepancies due to material under-and over-deposition in AM, and show how it captures various types of defects in the as-manufactured structures. We use this information to systematically modify the as-manufactured outcomes within the limitations of available 3D printer resolution(s), which often comes at the expense of introducing more geometric deviations (e.g., thickening a beam to avoid disconnection). We validate the effectiveness of the method on 3D examples with nontrivial topologies such as lattice structures and foams.
Volume 1A: 36th Computers and Information in Engineering Conference, 2016
Functionally graded materials (FGM) have recently attracted a lot of research attention in the wa... more Functionally graded materials (FGM) have recently attracted a lot of research attention in the wake of the recent prominence of additive manufacturing (AM) technology. The continuously varying spatial composition profile of two or more materials affords FGM object to simultaneously possess ideal properties of multiple different materials. Additionally, emerging technologies in AM domain enables one to make complex shapes with customized multifunctional material properties in an additive fashion, where laying down successive layers of material creates an object. In this paper, we focus on providing an overview of research at the intersection of AM techniques and FGM objects. We specifically discuss the FGM modeling representation schemes and outline a classification system to classify existing FGM representation methods. We also highlight the key aspects such as the part orientation, slicing, and path planning processes that are essential for fabricating a quality FGM object through ...
2016 11th System of Systems Engineering Conference (SoSE), 2016
Complex systems with high dimensional design spaces are often specified by subsystem compositions... more Complex systems with high dimensional design spaces are often specified by subsystem compositions that constrain feasible designs towards tight regimes in the design space. Composition between (sub)systems is limited by the interfaces exposed by the interacting architectures. Treating architectures as first class objects with their own formal properties is required to enable designing complex systems by novel compositions of heterogeneous subsystems and their design spaces. We describe a combinatorial model for system architectures using the formalism of cellular sheaves to identify all feasible compositions across heterogeneous subsystems through exposed interfaces. We discuss how the proposed model may be used to automatically generate novel systems of systems compositions and test key properties of composability and compositionality, required for solving planning and reconfiguration problems in systems of systems design.
2015 IEEE International Conference on Automation Science and Engineering (CASE), 2015
We present a novel spatial planning system that automatically generates machining plans with stab... more We present a novel spatial planning system that automatically generates machining plans with stable fixtures to fabricate complex geometries. Given a cutting tool, the system initially finds all tool poses that do not cut into the desired part geometry, and constructs the maximal volume removable from a raw stock according to the tool's degrees of freedom. The planning space is then defined as the product space of all available tools and their associated maximal removal volumes. A sequence of machining operations to iteratively remove material from a raw stock is generated by searching the planning space and finding successive machining steps that minimize manufacturing time or cost. For each step, a vise or modular fixture is automatically assembled such that the part is stable and the fixture assembly does not interfere with tool motion. Process planning is an inverse problem and multiple feasible solutions exist to machine the part if it is deemed manufacturable by the system. The system generates and visualizes several qualitatively distinct fabrication plans for an engineer to choose from. The chosen plan is sent to a tool path planner to generate machining instructions. We will show several examples of rapid and automatic end-to-end machining process planning on complex geometries to demonstrate the scalability and practicality of our system.
Computer-Aided Design, 2015
Representations of solid models were initially formulated partially in response to the need to su... more Representations of solid models were initially formulated partially in response to the need to support automation for numerically controlled machining processes. The assumed equivalence between shape, topology, and material properties of manufactured components and their computer representations led to the practice of modeling and simulating the behavior of physical parts before manufacture. In particular, representations of shape and material properties are treated in distinct nominal models for most unit manufacturing processes. Additively manufactured parts usually exhibit deviations from their nominal geometry in the form of stair-stepping artifacts and topological irregularities in the vicinity of small features. Furthermore, structural properties of additively manufactured parts have experimentally been shown to be dependent on the build orientation defining the cross sections where material is accumulated. Therefore geometric models of additively manufactured parts cannot be decoupled from the manufacturing process plan. In this paper we show that as-manufactured shapes may be represented in terms of the convolution operation to capture the additive deposition of material, measure the conformance to nominal geometry in terms of overlap volume, and model uncertainties involved in material flow and process control. We then demonstrate a novel interoperable approach to physical analysis on as-manufactured part geometry represented as a collection of machine-specific cross sections augmented with boundary conditions defined on the nominal geometry. The analysis only relies on fundamental queries of point membership classification and distance to boundary and therefore does not involve the overhead of model preparation required in approaches such as finite element analysis. Results are shown for non-trivial geometries to validate the proposed approach.
Tolerance Analysis for Validating Manufacturing Process Plans
Volume 1A: 34th Computers and Information in Engineering Conference, 2014
In manufacturing process planning, it is critical to ensure that the part generated from a proces... more In manufacturing process planning, it is critical to ensure that the part generated from a process plan complies with tolerances specified by designers to meet engineering constraints. Manufacturing errors are stochastic in nature and are introduced at almost every stage of executing a plan, for example due to inaccuracy of tooling, misalignment of location, distortion of clamping etc. Furthermore, these errors accumulate or ‘stack-up’ as the manufacturing process progresses to inevitably produce a part that varies from the designed model. The resultant variation should be within prescribed design tolerances. In this work, we present a novel approach for validating process plans using 3D tolerance stack-up analysis by representing variations of nominal features in terms of extents of their degrees of freedom within design and manufacturing tolerance zones. We will show how the manufacturing error stack-up can be effectively represented by composition and intersection of these transformations. We demonstrate several examples with different tolerance specifications to show the applicability of our approach for process planning.Copyright © 2014 by ASME
Feasible spaces in weld gun selection
2008 IEEE International Conference on Automation Science and Engineering, 2008
Abstract An important requirement of any robot-assisted welding process is to ensure that a set o... more Abstract An important requirement of any robot-assisted welding process is to ensure that a set of weld locations on a work assembly can be reached by the robot-weld gun assembly without colliding with the work assembly and surrounding tooling. A weld gun that maintains a valid contact at the a weld location without interference is said to be feasible at that location. An important class of problems in welding process planning and optimization reduce to the problems of verification and synthesis of feasible weld guns for a given set of ...
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Papers by Saigopal Nelaturi