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The integration of Multi-Objective Optimization (MOO) and Multi-Criteria Decision-Making (MCDM) has gathered significant attention across various scientific research domains to facilitate integrated sustainability assessment. Recently, there has been a growing interest in hybrid approaches that combine MCDM with MOO, aiming to enhance the efficacy of the final decisions. However, a critical gap exists in terms of providing clear methodological guidance, particularly when dealing with data uncertainties. To address this gap, this systematic review is designed to develop a generic decision tree that serves as a practical roadmap for practitioners seeking to perform MOO and MCDM in an integrated fashion, with a specific focus on accounting for uncertainties. The systematic review identified the recent studies that conducted both MOO and MCDM in an integrated way. It is important to note that this review does not aim to identify the superior MOO or MCDM methods, but rather it delves into the strategies for integrating these two common methodologies. The prevalent MOO methods used in the reviewed articles were evolution-based metaheuristic methods. TOPSIS and PROMETHEE II are the prevalent MCDM ranking methods. The integration of MOO and MCDM methods can occur either a priori, a posteriori, or through a combination of both, each offering distinct advantages and drawbacks. The developed decision tree illustrated all three paths and integrated uncertainty considerations in each path. Finally, a real-world case study for the pulse fractionation process in Canada is used as a basis for demonstrating the various pathways presented in the decision tree and their application in identifying the optimized processing pathways for sustainably obtaining pulse protein. This study will help practitioners in different research domains use MOO and MCDM methods in an integrated way to identify the most sustainable and optimized system. [ABSTRACT FROM AUTHOR]

Data in advanced manufacturing are often sparse and collected from various sensory devices in a heterogeneous and multi-modal fashion. Thus, for such intricate input spaces, learning robust and reliable predictive models for product quality assessments entails implementing complex nonlinear models such as deep learning. However, these 'data-greedy' models require massive datasets for training, and they tend to exhibit poor generalization performance otherwise. To address the data paucity and the data heterogeneity in smart manufacturing applications, this paper introduces a sim-to-real transfer-learning framework. Specifically, using a unified wide-and-deep learning approach, the model pre-processes structured sensory data (wide) as well as high-dimensional thermal images (deep) separately, and then passes the respective concatenated features to a regressor for predicting product quality metrics. Convolutional variational autoencoder (ConvVAE) is utilized to learn concise representations of thermal images in an unsupervised fashion. ConvVAE is trained via a sim-to-real transfer learning approach, backed by theory-based heat transfer simulations. The proposed metamodeling framework was evaluated in an industrial thermoforming process case study. The results suggested that ConvVAE outperforms conventional dimensionality reduction methods despite limited data. A model explainability analysis was conducted and the resulting SHAP values demonstrated the agreement between the model's predictions, theoretical expectations, and data correlation statistics. [ABSTRACT FROM AUTHOR]

Ice buildup can significantly and negatively impact system performance in various industrial sectors, and has remained a persistent challenge for decades. Many compliant materials exhibit excellent de‐icing performance but are easily eroded by impacts from supercooled water droplets, sand, dust, and debris. A composite panel inspired by animal skin, consisting of a facesheet protecting a nanofluid layer beneath, which exhibits durable anti‐icing and tunable photothermal properties is proposed. The viscous liquid layer beneath the facesheet increases flexural rigidity, preventing large deflections and increasing deformation resistance, which alters ice's adhesion to the surface. The non‐uniform fluid pressure exerted by the viscous nanofluid‐filled composite panels facilitates ice detachment, resulting in ice adhesion strengths as low as τice ≈ 10 kPa. Further, by altering the fluid properties, different additional functionalities can be endowed to the system. Incorporating fumed silica in a fluid‐filled composite panel results in rheopectic behavior, and this doubles their impact resistance when the shear thickening properties are properly tuned. Additionally, the combination of a transparent facesheet and a solar light absorbent nanofluid allows for tunable photothermal properties, further enhancing the anti‐icing performance of the system. This durable and tunable nanofluid‐filled composite panel shows great promise as a multifunctional de‐icing material. [ABSTRACT FROM AUTHOR]

The title of the current Special Issue, "Feature Papers in Materials Simulation and Design", has identified the aims of this collection since its opening: the gathering of research works and comprehensive review papers that advance the understanding and prediction of material behavior at different scales, from atomistic to macroscopic, through innovative modeling and simulation. The paper by Chen et al. [[4]] aimed at studying the thermal properties and thermoelectric performance of imidazole-graphyne (ID-GY) by combining first principle calculations with the Boltzmann transport theory. The last paper by Cheng and Vescovini [[16]] presented instead an accurate and efficient numerical method for the analysis and design of Variable Stiffness (VS) laminates. Civalek et al. [[11]] developed an efficient eigenvalue algorithm for the axial vibration analysis of embedded short-fiber-reinforced micro-/nano-composite rods subjected to arbitrary boundary conditions. [Extracted from the article]

In this study, an accurate and computationally efficient model for the heating process of thin thermoplastic sheets during thermoforming is developed. This model opens the door to efficient training of model-free control approaches in thermoforming applications, which often require extensive training data that would be significantly costly and time-consuming to generate using physical setups. This model takes into account heat transfer via radiation between heaters and the sheet, heat transfer via conduction through the sheet, and heat transfer via convection between the sheet and the ambient. In this paper, rather than using an analytical relationship for the view factor, an experiment is designed to determine the exact radiation pattern of the heater on the sheet and the fraction of infrared emission absorbed by the sheet. Comparing the output temperature profile on the sheet from the designed model to IR images from a laboratory-scale heating system indicates that the mean square error is reduced by around four times when compared to traditional models with analytical view factors. Moreover, a comparison of the computation time with COMSOL software for a scenario with the same configuration of computation hardware reveals that the designed model is almost ten times faster. [ABSTRACT FROM AUTHOR]