Design for additive manufacturing of variable dimension wave springs analyzed using experimental and finite element methods

Haq, Muhammad Rizwan ul; Nazir, Aamer; Jeng, Jeng-Ywan

doi:10.1016/j.addma.2021.102032

Cited by 11 publications

(8 citation statements)

References 34 publications

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“…During the 10 th cycle of each CTC wave spring of loading and unloading, the microparticle i.e. PP powder have been deformed [15] which results in less energy loss compared to the 1 st cycle of loading and unloading condition.…”

Section: Energy Loss Percentagementioning

confidence: 99%

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Development and analysis of polymer-based wave spring response with different infill density

Gouda

2023

Preprint

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The development of wave spring has enhanced performance characteristics like load bearing capability, stiffness, energy absorption and energy release technique in a constrained space. The wave springs can be developed by considering the number of turns, waves, thickness, which can be easily adjusted to accommodate stronger force and to meet specific technical requirements as per the area of applications. Wave springs are complex in design and can be fabricated by additive manufacturing technique. Advanced manufacturing methods provides full design freedom to develop the wave spring of various geometry. Present work is focused to evaluate the Polypropylene wave spring stiffness and its load bearing capacity, which may be helpful in the application of smooth landing of drones and robotic structures, mechanical pressure valves and washers. Limited study of the polymer-based wave spring properties and its material behavior has been studied. The contemporary work establishes the computational and experimental analysis of crest-to-crest wave springs which are designed with honeycomb infill pattern with infill density of 100%, 90% and 80%. Maximum load bearing capacity and stiffness of the different designed wave springs are obtained through universal testing machine. From the experimental results the average load bearing capacity of PP 100%, PP 90% and PP 80% at 75% of compressible distance of crest-to-crest wave spring from cycle 1 to 10. The results of the 1st cycle load bearing capacity are 234.075 N, 181.31 N and 178.58N respectively.

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Section: Energy Loss Percentagementioning

confidence: 99%

“…This includes contact and non-contact type CTC wave spring. Among different geometric configuration, taper spring has highest energy loss capacity [15]. This CTC type wave spring which is developed to fabricate in different parametric for investigation [16].…”

Section: Introductionmentioning

confidence: 99%

Development and analysis of polymer-based wave spring response with different infill density

Gouda

2023

Preprint

View full text Add to dashboard Cite

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“…Yang et al [ 50 ] used the Gaussian random field (GRF) method to create a synthetic microstructure image dataset of materials with different compositions and dispersion patterns. In addition, there are several common means of generating data today, such as the use of the finite element method (FEM) to generate high-contrast composites [ 51 ], two-dimensional (2D) mosaic composites [ 52 , 53 , 54 ], as well as microstructure [ 55 , 56 , 57 , 58 ] and datasets [ 59 , 60 , 61 , 62 , 63 ] of three-dimensional (3D) materials. The existing studies suggest that combining diverse knowledge from materials science, solid mechanics, and other related fields can generate datasets that are more representative of the design space and thus give better results with the applied models.…”

Section: Datamentioning

confidence: 99%

A Review of Performance Prediction Based on Machine Learning in Materials Science

Liu

Huang

et al. 2022

Nanomaterials

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With increasing demand in many areas, materials are constantly evolving. However, they still have numerous practical constraints. The rational design and discovery of new materials can create a huge technological and social impact. However, such rational design and discovery require a holistic, multi-stage design process, including the design of the material composition, material structure, material properties as well as process design and engineering. Such a complex exploration using traditional scientific methods is not only blind but also a huge waste of time and resources. Machine learning (ML), which is used across data to find correlations in material properties and understand the chemical properties of materials, is being considered a new way to explore the materials field. This paper reviews some of the major recent advances and applications of ML in the field of properties prediction of materials and discusses the key challenges and opportunities in this cross-cutting area.

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“…The soft grid structure [111] is manufactured by adding materials, which is applied in many applications such as soft robots, medical care, personal protection, energy absorption, fashion and design. The frame design based on adding materials meets [112] the bending of free geometry, and the soft grid with variable and gradual change of component thickness and materials. The application of wave spring has better performance than spiral spring.…”

Section: Analysis Of Application Fieldsmentioning

confidence: 99%

Considerations for the Variable Density Lattice Structure of Additive Manufacturing: A Review

et al. 2022

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In recent decades, the additive manufacturing technology has made great progress in software and methods in various fields, and gradually explored in a deeper and broader manner. It has changed from the mature homogenized lattice type and model design to a non-uniform direction. It has also started to improve from the aspects of material innovation, additive manufacturing printing technology, etc., to change the additive manufacturing technology and control parameters in the manufacturing process, Furthermore, the model or part can be improved to have better mechanical properties, such as stiffness, strength and wear resistance, which provides an important research methodology for the better development of this direction. These aspects include the software used, the type of structural analysis, the software used and verification, as well as the methods applied in the study of variable density lattices and the application and verification of improved research methods. In addition, there are density design optimization, variable density lattice design and lattice geometric characteristics’ design in geometric topology optimization design. The expected design of the model or part at the design level has reached the ideal model or part, which provides both a framework and ideas for the future research direction of non-uniform lattice design and a broader field of application, and will promote the future research and development prospects of variable density lattices.

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Design for additive manufacturing of variable dimension wave springs analyzed using experimental and finite element methods

Cited by 11 publications

References 34 publications

Development and analysis of polymer-based wave spring response with different infill density

Development and analysis of polymer-based wave spring response with different infill density

A Review of Performance Prediction Based on Machine Learning in Materials Science

Considerations for the Variable Density Lattice Structure of Additive Manufacturing: A Review

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