Process Understanding of Hybrid Multi-Step Incremental Sheet Forming

Jiang, Zilin

doi:https://doi.org/10.21985/n2-z9vv-y922

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Process Understanding of Hybrid Multi-Step Incremental Sheet Forming

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With the increasing demand for customized products, rapid prototyping, and small-batch production, incremental sheet forming (ISF) is gaining ever-growing attention. The potential of ISF to produce parts without the use of geometry-specific dies has revolutionized the sheet forming processes. Despite its promising characteristics, ISF thus far has not been extensively adopted in practice due to the knowledge gap relating to how process parameters influence the resultant properties, its unsatisfactory geometric accuracy, and the lack of a robust process planning strategy. In addition, current research on ISF has placed most emphasis on the forming of simple geometries using general aluminum and steel sheets. However, modern-day high-performance industries require complex shaped parts and structures made of advanced materials that commonly exhibit high strength and low formability at room temperature. This dissertation aims to improve the performance of the ISF process by addressing the aforementioned issues, with particular focus on 1) exploring toolpath strategies for forming complex geometries, 2) using electrically-assisted (EA) forming to alter the deformation behavior of hard-to-form materials, 3) exploring the process mechanics of EA forming, 4) estimating the forming temperature during EA-ISF for a more precise representation of the process. The thesis overview and contributions are briefly discussed below. Toolpath planning is especially challenging for multi-feature parts. Due to the interactions between features, different feature forming sequences lead to different formed profiles. The selection of the best-forming sequence in the literature remains unclear and sometimes contradictory. Motivated by this challenge, a feature forming sequence selection criteria was proposed based on the analysis of part surface gradients. The proposed criteria were assessed using a complex air intake with three individual features. The air intake was successfully formed using the recommended toolpath strategy using the highly ductile DDQ steel instead of low ductility materials like AA 7075-O aluminum alloy. Existing research on EA forming lacks investigations of the oxide dispersion strengthened (ODS) steels. The presence of nano-sized oxide particles provides high strength, creep resistance, and excellent irradiation damage tolerance. However, its high strength makes the fabrication of components difficult. The effects of electric currents on the deformation behavior of ODS steels, especially in terms of the stability of the oxide particles under high current density, are not well known. In this work, the mechanical and microstructural properties of the ODS steel under EA forming were experimentally investigated. The results show that the flow stress significantly decreases when an electric current is applied during plastic deformation; also, oxide particles survive very high current densities. ODS steel, therefore, is a suitable material for EA forming. The process mechanics of EA forming was investigated through analytical models and experiments. The analytical results show that the temperature-dependent hardening model can predict the yield strength subjected to EA uniaxial loading with reasonable accuracy without concerning the non-thermal current effects. This implies that stress reductions during EA deformation are predominately due to Joule heating. The experimental results clearly confirm this, since the deformation behavior under EA tension and oven-heated test are nearly identical at similar temperatures. Based on the promising results of EA-deformation, the use of electricity was then integrated with ISF to establish the EA-ISF process. The influence of electric currents on the resulting part properties was investigated in detail. The results show outstanding positive effects of the application of electric currents in ISF in terms of geometric accuracy and surface finish. Different current intensities (or Joule heating temperatures) were also shown to affect the depth of the deformed layer. As the forming zone is blocked by the tools, it is not possible to experimentally measure the forming temperatures in EA-ISF which is a prerequisite for efficient process control. Recent developments in the field of artificial intelligence (AI), especially machine learning, offer a great potential to solve manufacturing challenges. In this work, a neural network model was coupled with a finite element (FE) model of the EA-ISF process to predict the actual forming temperature. The obtained results show the ability of the AI approach to predict workpiece forming temperatures in the forming zone. Overall, the findings of this thesis help ISF achieve greater process flexibility in material selection and part geometry, as well as higher quality products with targeted material properties. In addition, the understanding of EA deformation behavior will also be of great help in improving other forming processes.

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