Intelligent Toolpathing for Part Repair via Hybrid Wire Arc Additive Manufacturing
Abstract
Hybrid Wire Arc Additive Manufacturings (hWAAM) hybridization of a metal deposition and a subtractive machining tool presents a unially manufactured components, and (iii) for repair of damaged parts in expeditionary environments. However, the hWAAM process has not yet realized its full potential due to (i) uncontrolled variation during the welding additive process, (ii) inefficient cutting passes due to the lack of real-time control of the machining subtractive process, and (iii) a labor-intensive, manual workflow for part repair applications. The overall goal of this research is to improve the overall efficiency of the hWAAM process and the quality of the resultant parts and repairs by adding intelligence to, and automating the, generation of additive and subtractive toolpaths and associated process parameters.To improve the efficiency and quality of hWAAM processing, and to enable the processing of flux-cored materials, the research team proposes a research plan focused on three primary, sequential aims focused on (i) automating the toolpath planning of repair operations, and incorporating in-sy exploring the use of implicit modeling techniques for rapidly comparing as-built and as-designed part geometries for automated generation of both subtractive and additive toolpaths for part repair applications of hWAAM. Using in-situ 3D scanning and a user-identified defective region of interest, the resultant algorithm will automatically plan baseline toolpaths for defect removal, materialin-fill (repair), and surface machining to remove slag (critical for processing flux-cored materials). In Aim 2, the team will thenuse the in-situ monitoring system to generate, in real-time, modify the baseline machining toolpaths to adapt to the actual geometry of the as-deposited weld beads. This is a critical capability as it will enable efficient elimination of slag from processing self-shielding flux-cored weld consumables. To intelligently plan the additive process parameters, the team will use machine learning techniques on acquired in-situ sensing data to pre-define optimum deposition process parameters. Subsequently, the team will incorporate this learning with in-situ bead watching algorithm to create an online closed-loop control capability that will provide real-timeadjustment of weld process parameters to minimize the deviation of the deposition from the as-designed target and mitigate the possible deposition defects such as porosity or cracking. Finally, the team will implement adaptive motion planning and execution algorithms to enable real-time creation of deposition toolpaths that dynamically adapt to previously deposited beads to ensure part quality.The proposed research and development of automated, intelligent toolpathing strategies for part repair and part fabrication via hWAAM will enhance its ability to be deployed and operated in expeditionary environments and shipborne environments. In addition to alleviating an operators burden of manually programming repair toolpaths, the ability to adaptively define additive and subtractive toolpathing for hWAAM presents an opportunity to process self-shielding flux-cored weld consumables that, while offer a substantial advantage for use in expeditionary environments due to their processability without shielding gases, generate significant slag duringwelding. The integrated sensing and toolpath generation method proposed here enables toolpath creation to take slag removal into account, and allows the machine to ensure slag is fully removed before further welding occurs.In support of the proposed work, NSWC Carderock Division and Naval Sea Systems
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 05, 2021
- Source ID
- N000142112708
Entities
People
- Christopher B Williams
Organizations
- Office of Naval Research
- United States Navy
- Virginia Tech