Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements

2020 ◽  
Vol 143 (2) ◽  
Author(s):  
Khalil Al Handawi ◽  
Petter Andersson ◽  
Massimo Panarotto ◽  
Ola Isaksson ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Working Condition ◽  
Optimal Designs ◽  
Direct Energy Deposition ◽  
Direct Energy ◽  
Design Requirements ◽  
Transition Rules ◽  
Set Based Design ◽  
Manufacturing Technologies ◽  
Process Solutions

Abstract Design requirements are often uncertain in the early stages of product development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies may enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements.

Scalable Set-Based Design Optimization and Remanufacturing for Meeting Changing Requirements

2020 ◽  
Author(s):  
Khalil Al Handawi ◽  
Petter Andersson ◽  
Massimo Panarotto ◽  
Ola Isaksson ◽  
Michael Kokkolaras
Keyword(s):  
Design Optimization ◽  
Optimal Designs ◽  
Design Problems ◽  
Direct Energy Deposition ◽  
Engineering Design Problems ◽  
Direct Energy ◽  
Transition Rules ◽  
Set Based Design ◽  
Manufacturing Technologies ◽  
Process Solutions

Abstract Engineering design problems often have open-ended requirements, especially in the early stages of development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements.

scholarly journals Sensor-Based Additive Manufacturing Technologies

2021 ◽  
Vol 12 (3) ◽  
pp. 3513-3521
Keyword(s):  
Additive Manufacturing ◽  
Wearable Sensors ◽  
Sensor Technology ◽  
Layer By Layer ◽  
Environmental Sensors ◽  
Direct Energy Deposition ◽  
Powder Bed ◽  
Direct Energy ◽  
Building Components ◽  
Manufacturing Technologies

Additive manufacturing is the term that uses the CAD data to build components layer by layer; it is also termed layered manufacturing or 3D printing. The major advantage of additive manufacturing is the capability of building components without the use of molds or tools. Five major categories of AM processes include Powder Bed Fusion (PBF), Direct Energy Deposition (DED), Material Jetting (MJ), Binder Jetting (BJ), and Sheet Lamination (SL). The sensor may be defined as a device that responds to a physical stimulus and transmits a resulting impulse. Sensor technology has been widely adopted in advanced manufacturing, aerospace, biomedical and robotic applications. Commonly used sensors are temperature sensors, strain sensors, biosensors, environmental sensors, and wearable sensors, etc. Additive manufacturing technologies can fabricate sensors and microfluidic devices with less labor. This paper focuses on various sensors developed by additive manufacturing processes, and their practical application for the particular purpose is reviewed.

scholarly journals Influence of Metallic Powder Characteristics on Extruded Feedstock Performance for Indirect Additive Manufacturing

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7136
Author(s):  
Cyril Santos ◽  
Daniel Gatões ◽  
Fábio Cerejo ◽  
Maria Teresa Vieira
Keyword(s):  
Additive Manufacturing ◽  
Electron Beam Melting ◽  
Metallic Powder ◽  
Micro Ct ◽  
Direct Energy Deposition ◽  
3D Objects ◽  
Direct Energy ◽  
Powder Characteristics ◽  
Manufacturing Technologies

Material extrusion (MEX) of metallic powder-based filaments has shown great potential as an additive manufacturing (AM) technology. MEX provides an easy solution as an alternative to direct additive manufacturing technologies (e.g., Selective Laser Melting, Electron Beam Melting, Direct Energy Deposition) for problematic metallic powders such as copper, essential due to its reflectivity and thermal conductivity. MEX, an indirect AM technology, consists of five steps—optimisation of mixing of metal powder, binder, and additives (feedstock); filament production; shaping from strands; debinding; sintering. The great challenge in MEX is, undoubtedly, filament manufacturing for optimal green density, and consequently the best sintered properties. The filament, to be extrudable, must accomplish at optimal powder volume concentration (CPVC) with good rheological performance, flexibility, and stiffness. In this study, a feedstock composition (similar binder, additives, and CPVC; 61 vol. %) of copper powder with three different particle powder characteristics was selected in order to highlight their role in the final product. The quality of the filaments, strands, and 3D objects was analysed by micro-CT, highlighting the influence of the different powder characteristics on the homogeneity and defects of the greens; sintered quality was also analysed regarding microstructure and hardness. The filament based on particles powder with D50 close to 11 µm, and straight distribution of particles size showed the best homogeneity and the lowest defects.

scholarly journals Geometry and Distortion Prediction of Multiple Layers for Wire Arc Additive Manufacturing with Artificial Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4694
Author(s):  
Christian Wacker ◽  
Markus Köhler ◽  
Martin David ◽  
Franziska Aschersleben ◽  
Felix Gabriel ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Welding Process ◽  
High Energy ◽  
Welding Distortion ◽  
Direct Energy Deposition ◽  
Direct Energy ◽  
Industrial Use ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Wire Arc Additive Manufacturing

Wire arc additive manufacturing (WAAM) is a direct energy deposition (DED) process with high deposition rates, but deformation and distortion can occur due to the high energy input and resulting strains. Despite great efforts, the prediction of distortion and resulting geometry in additive manufacturing processes using WAAM remains challenging. In this work, an artificial neural network (ANN) is established to predict welding distortion and geometric accuracy for multilayer WAAM structures. For demonstration purposes, the ANN creation process is presented on a smaller scale for multilayer beads on plate welds on a thin substrate sheet. Multiple concepts for the creation of ANNs and the handling of outliers are developed, implemented, and compared. Good results have been achieved by applying an enhanced ANN using deformation and geometry from the previously deposited layer. With further adaptions to this method, a prediction of additive welded structures, geometries, and shapes in defined segments is conceivable, which would enable a multitude of applications for ANNs in the WAAM-Process, especially for applications closer to industrial use cases. It would be feasible to use them as preparatory measures for multi-segmented structures as well as an application during the welding process to continuously adapt parameters for a higher resulting component quality.

Multi-physics modeling of direct energy deposition process of thin-walled structures: defect analysis

2021 ◽  
Vol 67 (4) ◽  
pp. 1229-1242
Author(s):  
Shuhao Wang ◽  
Lida Zhu ◽  
Yichao Dun ◽  
Zhichao Yang ◽  
Jerry Ying Hsi Fuh ◽  
...  
Keyword(s):  
Energy Deposition ◽  
Deposition Process ◽  
Defect Analysis ◽  
Direct Energy Deposition ◽  
Thin Walled Structures ◽  
Direct Energy ◽  
Thin Walled ◽  
Physics Modeling
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