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Recent advancements have significantly enhanced the capabilities for in-space servicing, assembly, and manufacturing (ISAM), to develop infrastructure in orbit and on the surface of celestial bodies. This progress is a departure from the traditional sustainability paradigm focused solely on Earth, highlighting the urgent need to define and operationalize the concept of “space sustainability” along with the development of an evaluation framework. The expansion of human activity into space, particularly in low-earth orbit, cis-lunar space, and beyond, underscores the critical importance of considering sustainability implications. Leveraging space resources offers economic growth and sustainable development opportunities, while reducing pressure on Earth’s ecosystems. This paradigm shift requires responsible and ethical utilization of space resources. A space sustainability assessment framework is essential for guiding ISAM capabilities, operations, missions, standards, and policies. This paper introduces an initial framework encompassing (1) pollution, (2) resource depletion, (3) landscape alteration, and (4) space environmental justice, with potential metrics (resources use and emissions, midpoint, and endpoint indicators) to measure impacts in the four domains.

 

This study presents a novel machine learning approach for predicting the anisotropic parameters of the Yld20002d non-quadratic yield function using a hole expansion test. Heterogeneous stress-strain fields during the test substitute for multiple experiments required in the conventional parameter identification approach. An artificial neural network model for the parameter prediction is developed using a virtually generated training dataset composed of strains from hole expansion simulations, performed using randomly selected anisotropic parameters. The developed model predicts the Yld20002d parameters for AA6022-T4 based on the measured strain field from a hole expansion experiment, and the parameter results are evaluated by comparing anisotropy in uniaxial tension tests, the yield locus, and thinning variation in hole expansion test. 

A multi-interpolation method is proposed to determine the displacement trajectory along each axis of a cruciform specimen with the goal of achieving a linear stress path, corresponding to a constant stress triaxiality, in the center of the custom-designed, non-standard specimen during in-plane biaxial testing. Finite element simulations are used to obtain the stress path from the given displacement trajectory, which is the displacement histories imposed on the specimen loading arms. In every iteration, the displacement trajectory is updated using the interpolation between the target stress path and adjacent ones on each side of the curve. The iterations are repeated until a linearity tolerance is satisfied. In this study, the material is an austenitic stainless steel, SS316L, with the Hockett–Sherby isotropic hardening model and Yld2004-18p non-quadratic anisotropic yield function. The method is demonstrated for five stress states between pure shear and equibiaxial tension. The results show the successful determination of a displacement trajectory for the non-standard cruciform specimen so that a linear stress path and constant triaxiality at the area of interest are achieved. 

Double-sided incremental forming (DSIF) is a die-less sheet metal forming process capable of fabricating complex parts. The flexibility of DSIF can be used for in-situ mechanical properties alteration, e.g., by controlling deformation-induced martensite transformation of austenitic stainless steels. In this paper, SS304L is deformed using DSIF at three different cooling conditions and two different tool paths to affect the martensite transformation. Additionally, finite element analyses were used to understandthe effect of tool paths on springback and plastic strain. Implementing a reforming tool path at the lowest achievable temperature resulted in a martensite volume fraction as high as 95%. 

The automotive industry relies heavily on sheet metal forming processes for many components. Material data solely from uniaxial testing is insufficient to fully define the material behavior of the complex plastic deformation during numerical simulations of the forming processes. In-plane biaxial testing using a cruciform type specimen is a more comprehensive representation than the traditional uniaxial testing alone. Wide ranging biaxial stress states can be imposed by applying different loading conditions on each cruciform axis. However, this can create a challenge to achieve desired deformation paths due to the non-linear relationship between the control parameter, e.g., displacement, and the output of interest, e.g., strain path. In this paper, an interpolation method to develop the displacement control that produces a linear strain path with a desired strain ratio is revisited and expanded upon from the authors’ previous work [1,2]. In the first iteration, linear biaxial displacements were applied to the specimen and the corresponding strain paths were obtained from the numerical simulations. The non-linear strain paths, due to geometry effects of the specimen, were used to reverse engineer a new displacement path that results in a linear strain path. Interpolation is revisited to show increased success with a second iteration. Analysis of the simulation results shows that linear strain paths of a given model can be determined and improved by successive iterations of interpolating the strain data from adjacent deformation paths.