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1. OAPS: An Optimization Algorithm for Part Separation in Assembly Design for Additive Manufacturing

   Deka, Angshuman; Behdad, Sara (August 2018, Proceedings of the ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2018 August 26-29, 2018, Quebec City, Quebec, Canada)

   Additive Manufacturing (AM) provides the advantage of producing complex shapes that are not possible through traditional cutting processes. Along with this line, assembly-based part design in AM creates some opportunities for productivity improvement. This paper proposes an improved optimization algorithm for part separation (OAPS) in assembly-based part design in additive manufacturing. For a given object, previous studies often provide the optimal number of parts resulting from cutting processes and their corresponding orientation to obtain the minimum processing time. During part separation, the cutting plane direction to generate subparts for assembly was often selected randomly in previous studies. The current work addresses the use of random cutting planes for part separation and instead uses the hill climbing optimization technique to generate the cutting planes to separate the parts. The OAPS provides the optimal number of assemblies and the build orientation of the parts for the minimum processing time. Two examples are provided to demonstrate the application of OAPS algorithm. 

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2. Part decomposition and 2D batch placement in single-machine additive manufacturing systems

   https://doi.org/https://doi.org/10.1016/j.jmsy.2018.07.006  

   Oh, Yosep; Zhou, Chi; Behdad, Sara (July 2018, Journal of manufacturing systems)

   To produce a large object within a limited workspace of an Additive Manufacturing (AM) machine, this study proposes a two-phase method: (1) part decomposition to separate a part into several pieces; and (2) 2D batch placement to place the decomposed parts onto multiple batches. In Phase 1, the large object is re-designed into small pieces by a Binary Space Partitioning (BSP) with a hyperplane, where parts are decomposed recursively until no parts are oversize the limited size of the workspace. In Phase 2, the decomposed parts are grouped as batches to go through serial build processes using a single AM machine. Within a batch, the decomposed parts are placed based on a 2D packing method in which parts are not placed over other parts to avoid potential surface damage caused by support structure between parts. A genetic algorithm (GA) for the 2D batch placement is applied to find near-optimal solutions for build orientations, placement positions, and batch number for each part. As an objective function, the total process time including build time and post-processing time is minimized. This research provides some insights into the relation between part decomposition and 2D batch placement. It shows that minimizing the number of decomposed parts could be more critical than minimizing the size of decomposed parts for reducing the overall process time in serial batch processes. 

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3. Model-driven directed-energy-deposition process workflow incorporating powder flowrate as key parameter

   https://doi.org/10.1016/j.mfglet.2020.08.005  

   Traxel, Kellen; Malihi, Darin; Starkey, Kyler; Bandyopadhyay, Amit (August 2020, Manufacturing letters)

   null (Ed.)

   Process optimization for directed-energy-deposition, an industrial laser-based additive manufacturing technique, is a time-intensive endeavor for manufacturers. Herein we investigate the use of a modified analytical process-model based on powder-bed-fusion techniques, to predict quality build parameters by incorporating the effects of three key parameters: laser-power, scanning-speed, and powder flowrate. Titanium alloy (Ti6Al4V) tracks of varying parameters were built, studied, and used to predict parameters for quality builds used at different parameters. The model agreed well with experimental build quality at powder flowrates less than 6.5g/min, whereas, higher flowrates created significant unmelted-particle regions, despite optimal parameter predictions. Processing of multi-layer bulk samples revealed that parameters in the optimal range account for relative densities >99%, indicating quality bulk processing parameters. Our results indicate that process modeling with the incorporation of powder feedrate as a key parameter is possible using a commercial laser-based additive manufacturing system. 

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4. Laser enhanced direct print additive manufacturing for mm-wave components and packaging

   https://doi.org/10.1109/ICEAA.2017.8065575  

   Rojas-Nastrucci, Eduardo A.; Ramirez, Ramiro; Hawatmeh, Derar; Lan, Di; Wang, Jing; Weller, Thomas (September 2017, 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA))

   Direct print additive manufacturing (DPAM) is an additive manufacturing technique that combines fused deposition modeling with micro-dispensing. As a multimaterial 3D printing method it has proven to be effective for fabricating printed electronics that operate in the microwave frequency range. This paper discusses the addition of picosecond laser processing to the DPAM process, and the enhancements to high-frequency performance and design capability that are made possible. The use of laser-enhanced DPAM for 3D fabrication of transmission lines, passive components and packaging is discussed. 

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5. Genetic Algorithm-Based Data-Driven Process Selection System for Additive Manufacturing in Industry 4.0

   https://doi.org/10.3390/ma17184544  

   Aljabali, Bader Alwomi; Shelton, Joseph; Desai, Salil (September 2024, Materials)

   Additive manufacturing (AM) has impacted the manufacturing of complex three-dimensional objects in multiple materials for a wide array of applications. However, additive manufacturing, as an upcoming field, lacks automated and specific design rules for different AM processes. Moreover, the selection of specific AM processes for different geometries requires expert knowledge, which is difficult to replicate. An automated and data-driven system is needed that can capture the AM expert knowledge base and apply it to 3D-printed parts to avoid manufacturability issues. This research aims to develop a data-driven system for AM process selection within the design for additive manufacturing (DFAM) framework for Industry 4.0. A Genetic and Evolutionary Feature Weighting technique was optimized using 3D CAD data as an input to identify the optimal AM technique based on several requirements and constraints. A two-stage model was developed wherein the stage 1 model displayed average accuracies of 70% and the stage 2 model showed higher average accuracies of up to 97.33% based on quantitative feature labeling and augmentation of the datasets. The steady-state genetic algorithm (SSGA) was determined to be the most effective algorithm after benchmarking against estimation of distribution algorithm (EDA) and particle swarm optimization (PSO) algorithms, respectively. The output of this system leads to the identification of optimal AM processes for manufacturing 3D objects. This paper presents an automated design for an additive manufacturing system that is accurate and can be extended to other 3D-printing processes. 

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