Post-processing and testing-oriented design for additive manufacturing – A general framework for the development of hybrid AM parts

Schneberger, Jan-Henrik; Kaspar, Jerome; Vielhaber, Michael

doi:10.1016/j.procir.2020.01.059

Cited by 5 publications

(4 citation statements)

References 15 publications

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“…Mishra et al [16] further emphasizes the importance of post-processing techniques, categorizing them into methods for support material removal, surface texture improvements, thermal and non-thermal post-processing, and aesthetic improvements. Schneberger et al [17] underscores the need for a comprehensive framework for the development of hybrid AM parts, which includes post-processing, testing, and life cycle monitoring. These studies collectively highlight the significance of post-processing in additive manufacturing for improving part quality and performance.…”

Section: The Problems Related To Polishing Taskmentioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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This study explores the integration of Artificial Intelligence (AI) and collaborative robotics in industrial surface finishing, focusing on feature detection on object reflective surfaces using neural networks in computationally constrained environments. Central to this investigation is the utilization of cameras considered as sensors that can capture detailed images of reflective surfaces. These images are then analyzed by a custom neural network, demonstrating the feasibility of feature detection with non-powerful computers. The public dataset for recognizing light reflection defects, available in the SPADD repository, supports this approach. Employing open-source tools like Darknet YOLOv3 and Yolo\_mark, this method proves particularly advantageous for small and medium-sized enterprises (SMEs) due to its low cost and maintenance requirements. This research highlights the challenges of AI and collaborative robotics in industrial applications, especially in adapting to varying conditions and the need for continuous training with new data coming from several sensors. Looking ahead, plans are in place to integrate this system with cobot-assisted polishing tasks, using cameras to enhance manufacturing efficiency and quality. This work contributes to the broader understanding of practical applications of AI in industrial automation, surface finishing processes, and the effective use of sensor technology.

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Section: The Problems Related To Polishing Taskmentioning

confidence: 99%

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Bajrami,

Palpaceli

2024

Preprint

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“…TMPS gyroids do not require any postprocessing due to the use of computer-generated algorithms [22]. Designing components individually will increase surface quality for AM through reductions of re-chucking, orientation-based optimization, and geometry-based optimization allowing for e cient post-processing methods [20]. However, potential toolsets used to evaluate the ease of post-processing operations have been implemented for the use of powder bed fusion [21].…”

Section: Post-process Threshold Limitationsmentioning

confidence: 99%

“…A critical component for AM post-process techniques is the need for post-process threshold limitations [20]. Currently, there is a lack of research and accurate tools available to quantify limitations for AM postprocesses [21].…”

Section: Post-process Threshold Limitationsmentioning

confidence: 99%

A Novel Methodological Framework for Design for Additive Manufacturing for Sheet Metal Tooling

James

Buddine²

2022

Preprint

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Additive manufacturing (AM) has the ability to benefit sheet metal manufacturing industries by implementing novel design solutions for tools and dies. However, there is a lack of support for the use of additive manufacturing in sheet metal manufacturing. Unfortunately, designers do not have proficient guidance for incorporating these innovative strategies. Existing Design for Additive Manufacturing (DFAM) approaches are precisely reviewed in this paper. They are then analyzed further by being related to conventional sheet metal manufacturing processes. However, existing DFAM guidelines do not provide a complete development process for design and manufacturing. Therefore, a novel methodological framework for DFAM of tools in sheet metal fabrication processes is proposed. Existing design strategies for sheet metal tooling through conventional manufacturing and additive manufacturing are utilized in the new DFAM modular framework. A case study example follows the DFAM framework to offer designers additional guidance through the design and manufacturing stages for sheet metal tooling with the use of additive manufacturing as a secondary process.

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“…The major barriers to the optimal utilisation of metal additive manufacturing technologies have been identified as the nonexistence of standard procedures on powder recyclability (required for direct cost reduction), meeting surface quality requirements and finishing features like internal lattices/pathways [9]. In another perspective, the current level of maturity of MAM technologies is regarded not to be sufficient to guarantee consistency in producing high-quality parts [10] while other views acknowledged that there is a compelling need for requirements relating to qualification, quality assurance and life cycle monitoring of MAM-produced parts [11]. Notwithstanding, aerospace alloys like nickel-based superalloys and Ti alloys are being processed via the use of metal additive manufacturing (MAM) processes at the laboratory and industrial levels to gain a scientific understanding for the improvement and fabrication of useful components/parts.…”

Section: Introductionmentioning

confidence: 99%

Post-processing treatments–microstructure–performance interrelationship of metal additive manufactured aerospace alloys: A review

Ojo

Taban

2023

Materials Science and Technology

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The application of Metal Additive Manufacturing (MAM) in the aerospace industry has been gaining attention. The poor surface quality of parts greatly limits the prospects of MAM-produced parts in the aerospace industry as fatigue performance hinges on a good surface finish and homogeneous microstructure. Post-processing treatments are important to improve the surface quality and the overall performance of MAM-produced parts during static and dynamic loadings. This paper provides a review of the current state of post-processing treatment options for MAM-produced parts. The advances in the post-processing treatments of MAM parts to improve fatigue strength, tensile strength, surface integrity and other properties of parts are reviewed. Future research prospects on metal additive manufacturing of aerospace parts are briefly expounded. Abbreviations: AM: Additive Manufacturing; CMT-WAAM: Cold Metal Transfer-Based Wire Arc Additive Manufacturing; CSD: Cold Spray Deposition; DA: Direct Aging; DED: Directed Energy Deposition; E: Elongation; EBAM: Wire-feed Electron Beam Additive Manufacturing; EBAM Electron Beam Additive Manufacturing; EBSD: Electron Back Scattered Diffraction; FRAM: Friction Rolling Additive Manufacturing; FSAM: Friction Stir Additive Manufacturing; FSP: Friction Stir Processing; HFDB: Hybrid Friction Diffusion Bonding; HIP: Hot Isostatic Pressing; HSA Homogenisation + Solution Treatment + Aging; IFSP: Interlayer Friction Stir Processing; IPF: Inverse Pole Figure; KAM: Kernel Average Misorientation; LAM: Laser Additive Manufacturing Process; LENS: Laser Engineered Net Shaping; LDM: Laser Deposition Manufacturing; L-PBF: Laser Powder Bed Fusion; MAM: Metal Additive Manufacturing; MTFS: Multi-track Multi-layer Friction Surfacing; O-WLAM: Wire Oscillating Laser Additive Manufacturing (O-WLAM); S: Solution Treatment; SA: Solution Treatment + aging; TEM: Transmission Electron Microscope; UAM: Ultrasonic Additive Manufacturing; UTS: Ultimate Tensile Strength; WAAM: Wire Arc Additive Manufacturing; YS: Yield Strength

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Post-processing and testing-oriented design for additive manufacturing – A general framework for the development of hybrid AM parts

Cited by 5 publications

References 15 publications

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

Developing a YOLOv3-Based Neural Network to Enable Digital Cameras to Detect Features on the Reflective Surface of Objects in Robotic Applications: A Low-Computational Approach with a New Public Dataset

A Novel Methodological Framework for Design for Additive Manufacturing for Sheet Metal Tooling

Post-processing treatments–microstructure–performance interrelationship of metal additive manufactured aerospace alloys: A review

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