Building free-form thin shell parts using supportless extrusion-based additive manufacturing

Bhatt, Prahar M.; Malhan, Rishi K.; Rajendran, P.; Gupta, Satyandra K.

doi:10.1016/j.addma.2019.101003

Cited by 29 publications

(16 citation statements)

References 35 publications

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“…Printing of free-form, thin shell parts without using support structures was achieved recently using a dynamically reorienting build-platform. [156] An ANN-based contextdependent compensation scheme was used to apply corrections to complex tool paths. Another strategy is changing print parameters adaptively, which is more applicable especially when largescale parts limit the capability to dynamically reorient the build-platform.…”

Section: Tool Path Planning To Achieve Full Densitymentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

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The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.

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Section: Tool Path Planning To Achieve Full Densitymentioning

confidence: 99%

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Tian

Bustillos

et al. 2021

Advanced Intelligent Systems

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“…In Spain, the Institute for Advanced Architecture of Catalonia with the aim of developing a family of small-scale construction robots (the mini-builders concept) created a family of three small mobile robots capable of printing concrete. The system consists of a base robot to print the first 10 layers, a griper robot to print by attaching and moving along the previously printed layers Regarding the filament-based extrusion, researchers from the University of Southern California (Bhatt et al, 2020) presented a novel approach to build fine and thin shell parts. They developed a layer slicing and a tool-path planning algorithm to print supportless structures.…”

Section: Robotic Systems Advantages and Disadvantagesmentioning

confidence: 99%

“…A robotic arm equipped with a DED powder system, named AddiTube [Figure 15(b) and 15(c)], was developed, consisting of 6-DOF ABB IRB 4600-45 manipulator, a 2-DOF building platform (ABB IRBP A-250) and a laser source with 3 kW maximum power output from IPG (YLS-3000). The objective is to show the possibility to improve the surface quality of AISI 316L thin-wall structures through laser re-melting, using the same laser equipment, after the deposition process (Bruzzo et al, 2021). Robotic arm laser-wire DED process is also being studied by researchers at Southern Methodist University, USA.…”

Section: Robotic Systems Advantages and Disadvantagesmentioning

confidence: 99%

The role of robotics in additive manufacturing: review of the AM processes and introduction of an intelligent system

Pires

Azar

Nogueira

et al. 2021

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Purpose Additive manufacturing (AM) is a rapidly evolving manufacturing process, which refers to a set of technologies that add materials layer-by-layer to create functional components. AM technologies have received an enormous attention from both academia and industry, and they are being successfully used in various applications, such as rapid prototyping, tooling, direct manufacturing and repair, among others. AM does not necessarily imply building parts, as it also refers to innovation in materials, system and part designs, novel combination of properties and interplay between systems and materials. The most exciting features of AM are related to the development of radically new systems and materials that can be used in advanced products with the aim of reducing costs, manufacturing difficulties, weight, waste and energy consumption. It is essential to develop an advanced production system that assists the user through the process, from the computer-aided design model to functional components. The challenges faced in the research and development and operational phase of producing those parts include requiring the capacity to simulate and observe the building process and, more importantly, being able to introduce the production changes in a real-time fashion. This paper aims to review the role of robotics in various AM technologies to underline its importance, followed by an introduction of a novel and intelligent system for directed energy deposition (DED) technology. Design/methodology/approach AM presents intrinsic advantages when compared to the conventional processes. Nevertheless, its industrial integration remains as a challenge due to equipment and process complexities. DED technologies are among the most sophisticated concepts that have the potential of transforming the current material processing practices. Findings The objective of this paper is identifying the fundamental features of an intelligent DED platform, capable of handling the science and operational aspects of the advanced AM applications. Consequently, we introduce and discuss a novel robotic AM system, designed for processing metals and alloys such as aluminium alloys, high-strength steels, stainless steels, titanium alloys, magnesium alloys, nickel-based superalloys and other metallic alloys for various applications. A few demonstrators are presented and briefly discussed, to present the usefulness of the introduced system and underlying concept. The main design objective of the presented intelligent robotic AM system is to implement a design-and-produce strategy. This means that the system should allow the user to focus on the knowledge-based tasks, e.g. the tasks of designing the part, material selection, simulating the deposition process and anticipating the metallurgical properties of the final part, as the rest would be handled automatically. Research limitations/implications This paper reviews a few AM technologies, where robotics is a central part of the process, such as vat photopolymerization, material jetting, binder jetting, material extrusion, powder bed fusion, DED and sheet lamination. This paper aims to influence the development of robot-based AM systems for industrial applications such as part production, automotive, medical, aerospace and defence sectors. Originality/value The presented intelligent system is an original development that is designed and built by the co-authors J. Norberto Pires, Amin S. Azar and Trayana Tankova.

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“…Dai et al (2018) and colleagues presented an algorithm for decomposing a volume into a sequence of accessible surfaces with nearly uniform thickness to guarantee accessibility for deposition. Researchers at the University of Southern California (USA) proposed a collision detection module by approximating an extrusion tool and a build platform using a sphere fitting algorithm (Bhatt et al, 2020). For multiaxis motion, when given a constant travel speed (TS), the relative velocities can be different.…”

Section: Multiaxis Motionmentioning

confidence: 99%

Multiaxis wire and arc additive manufacturing for overhangs based on conical substrates

Dai

Zhang²,

et al. 2021

RPJ

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Purpose This paper aims to present a series of approaches for three-related issues in multiaxis in wire and arc additive manufacturing (WAAM) as follows: how to achieve a stable and robust deposition process and maintain uniform growth of the part; how to maintain consistent formation of a melt pool on the surface of the workpiece; and how to fabricate an overhanging structure without supports. Design/methodology/approach The principal component analysis-based path planning approach is proposed to compute the best scanning directions of slicing contours for the generation of filling paths, including zigzag paths and parallel skeleton paths. These printing paths have been experimented with in WAAM. To maintain consistent formation of a melt pool at overhanging regions, the authors introduce definitions for the overhanging point, overhanging distance and overhanging vector, with which the authors can compute and optimize the multiaxis motion. A novel fabricating strategy of depositing the overhanging segments as a support for the deposition of filling paths is presented. Findings The second principal component of a planar contour is a reasonable scanning direction to generate zigzag filling paths and parallel skeleton filling paths. The overhanging regions of a printing layer can be supported by pre-deposition of overhanging segments. Large overhangs can be successfully fabricated by the multiaxis WAAM process without supporting structures. Originality/value An intelligent approach of generating zigzag printing paths and parallel skeleton printing paths. Optimizations of depositing zigzag paths and parallel skeleton paths. Applications of overhanging point overhanging distance and overhanging vector for multiaxis motion planning. A novel fabricating strategy of depositing the overhanging segments as a support for the deposition of filling paths.

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Building free-form thin shell parts using supportless extrusion-based additive manufacturing

Cited by 29 publications

References 35 publications

Data‐Driven Approaches Toward Smarter Additive Manufacturing

Data‐Driven Approaches Toward Smarter Additive Manufacturing

The role of robotics in additive manufacturing: review of the AM processes and introduction of an intelligent system

Multiaxis wire and arc additive manufacturing for overhangs based on conical substrates

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