Innovative Multi-Physics-based Tool to Minimize Residual Stress / Distortion in Large Aerospace Aluminum Forging Parts

Navy SBIR 20.2 - Topic N202-122

Naval Air Systems Command (NAVAIR) - Ms. Donna Attick [email protected]

Opens: June 3, 2020 - Closes: July 2, 2020 (12:00 pm ET)

 

 

N202-122       TITLE: Innovative Multi-Physics-based Tool to Minimize Residual Stress / Distortion in Large Aerospace Aluminum Forging Parts

 

RT&L FOCUS AREA(S): General Warfighting Requirements (GWR)

TECHNOLOGY AREA(S): Air Platform, Materials

 

OBJECTIVE: Develop a tool to optimize the quenching process by understanding and addressing the multi-physics challenges in the inter-relationship among the stress/strain, heat, and phase transformation in order to control residual stress and reduce distortion in large aerospace aluminum forging parts.

 

DESCRIPTION: Naval Aviation aircraft procurement faces cost and schedule challenges where one of the major contributors is the high scrap rate of large airframe aluminum forging parts. For example, a 22% scrap rate was observed on a NAVAIR low rate initial production (LRIP) Helicopter program in 2017.

 

The parts were rejected for geometrical non-conformance, due to distortion induced during production stages, but mostly right after the quenching step, or post-quenching. Typical production stages start with rough machining of the forging, followed by quenching, aging, removing braces, chemical milling, semi-finish machining, finish machining, and final inspection.

�

To reduce the post-quenching distortion, there are two approaches: 1) Do trial-and-error runs, then pick the best one. This approach is cost prohibitive since there are endless combinations of quenching set ups, or 2) Use a prediction tool to run simulations with optimized quenching parameters yielding least distortion.

 

Currently available tools for reducing post-quenching distortion in large aircraft aluminum forging parts are often a set of Finite Element Analysis (FEA) software with input consist of a) geometry of parts and quench tank, and b) thermal characteristics of parts and quenching medium. Thermal parameters are entered to represent the heat transfer characteristics, but that is not sufficiently accurate, since the mechanical and metallurgical aspects of the parts are changing as well in the process, and need to be concurrently considered. Acceptable accuracy would be when all three input types (i.e., thermal, mechanical, and metallurgical) are entered into the FEA before the simulation process.

 

To effectively reduce post-quenching distortion in large aircraft aluminum forging parts, an innovative multi-physics-based and machine learning tool must be designed to optimize the quenching process, where the model inputs will cover all three fields: thermal, mechanical, and metallurgical, with a comprehensive understanding and control of residual stresses.

 

PHASE I: Develop the concept for one or more physics-based options where heat transfer, stress/strain evolution, and phase transformation can be modelled. Demonstrate feasibility of the model design. Perform a proof-of-concept demonstration that assesses the design�s Technology Readiness Level (TRL)/Manufacturing Readiness Level (MRL). The Phase I effort will include prototype plans to be developed under Phase II.

 

PHASE II: Develop the physics-based conceptual prototype model, and verify/validate the prototype with coupon/component/full-scale testing. Demonstrate the transition feasibility. Update TRL/MRL assessment.

 

PHASE III DUAL USE APPLICATIONS: Commercialize and transition the developed tool as an analytical software package. Detail a verification and validation plan, along with a demonstration of application capacity for the selected airframe components of any interested aircraft platform.

Methods and techniques developed can be included for broad use in the aerospace industry in a commercial software package for optimized quenching and to minimize residual stress/distortion in large aerospace aluminum forging parts.

 

REFERENCES:

1. Robinson, J., Tanner, D., & Van Petegem, S. �Influence of Quenching and Aging on Residual Stress in Al-Zn-Mg-Cu Alloy 7449.� Materials Science and Technology, Volume 28, Issue 4, April 2012, pp. 420-430. https://www.researchgate.net/publication/233717127_Influence_of_quenching_and_aging_on_residual_stress_in_Al-Zn-Mg-Cu_alloy_7449

 

2. Watton, J. �Computational Modeling and Optimization of Residual Stress for Large Structural Forgings.� Aeromat 21 Conference and Exposition, American Society for Metals, June 2012. https://www.researchgate.net/publication/267900582_Computational_Modeling_and_Optimization_of_Residual_Stress_for_Large_Structural_Forgings

 

3. Yang, X., Zhu, J.-C., Lai, Z.-H., & Liu, Y. �Finite Element Analysis of Quenching Temperature Field, Residual Stress and Distortion in A357 Aluminum Alloy Large Complicated Thin-wall Workpieces.� Transactions of Nonferrous Metals Society of China, Volume 23, Issue 6, pp. 1751-1760. https://www.researchgate.net/publication/275129961_Finite_element_analysis_of_quenching_temperature_field_residual_stress_and_distortion_in_A357_aluminum_alloy_large_complicated_thin-wall_workpieces

 

KEYWORDS: Optimized Quenching, Large Aerospace Aluminum Forging Parts, Computational Modelling, Residual Stress, Phase Transformation, Micro Structures