ICME 2023: ICME Non-Metals: II
Program Organizers: Charles Ward, AFRL/RXM; Heather Murdoch, U.S. Army Research Laboratory

Monday 3:20 PM
May 22, 2023
Room: Caribbean VI & VII
Location: Caribe Royale

Session Chair: Karthik Rajan Venkatesan, Eaton

3:20 PM
Data-driven Modeling for Service Lifetime Prediction of Acrylic Polymers: Hein Htet Aung¹; Jayvic Cristian Jimenez¹; Leean Jo¹; Roger French¹; Laura Bruckman¹; ¹Case Western Reserve University
    Polymethyl methacrylate (PMMA) is a thermoplastic polymer known for its optical clarity and hardness, among other properties, making it a commodity polymer. However, PMMA is susceptible to degradation arising from environmental factors such as UV, heat, and humidity. These stressors lead to performance loss and reduced lifetime service. In our work, six different formulations of PMMA containing various UV absorbers and stabilizers are exposed under three accelerated conditions, as per ASTM G154 and G155, in a longitudinal stepwise manner. We then quantified their performance metrics using colorimetric methods such as the Yellowness Index and obtaining UV-vis, fluorescence and Fourier-transform infrared (FTIR) spectra. The data collected are then modeled with netSEM (Network Structural Equation Modeling), which provides inferential and predictive data-driven models. These data-driven models are selected based on adjusted R2 values and stepwise regression is performed using either Akaike (AIC) or Bayesian (BIC) Information Criteria to avoid complexity and overfitting.

3:40 PM
Multilevel Modelling and Optimization for Large Scale Additive Manufacturing: Christopher Bock¹; Masoud Rais-Rohani¹; Brett Ellis¹; ¹University of Maine
    Large scale additive manufacturing (LSAM) of thermoplastics is a manufacturing process that is being adopted by industry for different manufacturing applications. However, LSAM faces multiple design challenges, including process dependence of the material properties resulting from fiber orientation, inter- and intra-bead voids, and design constraints from the deposition process. This work seeks to address these challenges via Analytical Target Cascading (ATC) multilevel optimization algorithm. Utilizing a process-informed simulation of a finite element model of a stiffened panel, ATC optimizes the process and material properties of the part coupled with the part design. The ATC optimization algorithm is demonstrated for mass minimization of a 3D-printed rectangular stiffened plate within a 4000 × 1000 × 500 mm3 design volume, with simple-support boundary conditions along all edges, loaded in uniaxial compression, and modeled using classical lamination theory. Results indicate mass reductions exceeding 20% compared to the baseline model, while keeping manufacturing feasible.

4:00 PM
Automation of the ICME Workflow Incorporating Material Digital Twins at Different Length Scales Within a Robust Information Management System: Brandon Hearley¹; Steven Arnold¹; Marianna Maiaru²; ¹NASA Glenn Research Center; ²University of Massachusetts Lowell
    Recent successes in Integrated Computational Materials Engineering (ICME) have demonstrated the potential in designing fit-for-purpose materials for a given application in a cost and time efficient manner. However, the material design process must contain a level of automation in the material decision process, implementing some optimization algorithms, to truly enable the full benefits of ICME, particularly when considering materials at multiple length/time scales. In this work, we will demonstrate how the GRC ICME schema and Python framework automates a workflow that captures, analyzes, maintains, and disseminates the digital footprint in the context of tailoring resin material at the nanoscale of a woven composite Y-joint at the macroscale for an Aurora D8 double-bubble fuselage. This digital footprint incorporates the interaction of both structural digital twins and material twins at various length scales.

4:20 PM
Design of Manufacturing Process of Polymer Composite Through Multiscale Cure Analysis Using Bayesian Optimization: Yagnik Kalariya¹; Soban Babu Beemaraj¹; Amit Salvi¹; ¹Tata Consultancy Services
    Polymer composite structures are heavily used in aerospace, defense, transport, and energy sector due to their lightweight and high-performance behavior. The behavior of these structures highly depends on curing process as it affects evolution of material properties, residual stresses, deformation, etc. Various cure process parameters, mainly temperature cycle with respect to time, need to be optimized to get the desired characteristics for these structures. In this paper, the cure process is explicitly modeled through finite element method. Its effects are captured by modeling thermo-chemical-mechanical analysis through multiple length scales. Traditional optimization techniques are time-consuming due to the unavailability of gradients, larger simulation time and exploration space. Bayesian optimization algorithms used in this study overcome these challenges pertaining to cure process optimization. Insights from such optimization can be utilized by product designers as well as manufacturers to take timely decisions to improve the performance of these composite structures.