Artificial intelligence, especially machine learning (ML) and deep learning (DL) algorithms, is becoming an important tool in the fields of materials and mechanical engineering, attributed to its power to predict materials properties, design de novo materials and discover new mechanisms beyond intuitions. As the structural complexity of novel materials soars, the material design problem to optimize mechanical behaviors can involve massive design spaces that are intractable for conventional methods. Addressing this challenge, ML models trained from large material datasets that relate structure, properties and function at multiple hierarchical levels have offered new avenues for fast exploration of the design spaces. The performance of a ML-based materials design approach relies on the collection or generation of a large dataset that is properly preprocessed using the domain knowledge of materials science underlying chemical and physical concepts, and a suitable selection of the applied ML model. Recent breakthroughs in ML techniques have created vast opportunities for not only overcoming long-standing mechanics problems but also for developing unprecedented materials design strategies. In this review, we first present a brief introduction of state-of-the-art ML models, algorithms and structures. Then, we discuss the importance of data collection, generation and preprocessing. The applications in mechanical property prediction, materials design and computational methods using ML-based approaches are summarized, followed by perspectives on opportunities and open challenges in this emerging and exciting field.

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Artificial intelligence and machine learning in design of mechanical materials

This review article summarizes the current state-of-the-art for biomimicry in additive manufacturing. Biomimicry is the practice of learning from and emulating nature - which can be increasingly realized in engineering applications due to progress in additive manufacturing (AM). AM has grown tremendously in recent years, with improvements in technology and resulting material properties sometimes exceeding those of equivalent parts produced by traditional production processes. This has led to the industrial use of AM parts even in highly critical applications, most notably in aerospace, automotive and medical applications. The ability to create parts with complex geometries is one of the most important advantages of this technology, allowing the production of complex functional objects from various materials including plastics and metals that cannot be easily produced by any other means. Utilizing the full complexity allowed by AM is the key to unlocking the huge potential of this technology for real world applications – and biomimicry might be pivotal in this regard. Biomimicry may take different forms in AM, including customization of parts for individuals (e.g. medical prosthesis, implants or custom sports equipment), or optimization for specific properties such as stiffness and light-weighting (e.g. lightweight parts in aerospace or automotive applications). The optimization process often uses an iterative simulation-driven process analogous to biological evolution – with an improvement in every iteration. Other forms of biomimicry in AM include the incorporation of real biological inputs into designs (i.e. emulating nature for its unique properties); the use of cellular or lattice structures – for various applications and customized to the application; incorporating multi-functionality into designs; the consolidation of numerous parts into one and the reduction of waste, amongst others. Numerous biomimetic design approaches may be used – broadly categorized into customized/freeform, simulation-driven and lattice designs. All these approaches may be used in combination with one another, and in all cases with or without direct input from nature. The aim of this review is to unravel the different forms of biomimetic engineering that are now possible – focusing mainly on functional mechanical engineering for end-use parts, i.e. not for prototyping. The current limits of each design approach are discussed and the most exciting future opportunities for biomimetic AM applications are highlighted.

Beautiful and Functional: A Review of Biomimetic Design in Additive Manufacturing

Effects of porosity on the mechanical properties of additively manufactured components: a critical review

\n Additive manufacturing (AM) is a set of manufacturing processes that are capable of producing complex parts directly from a computer model of the part. This review provides a history of the early antecedents of these processes. In addition, the different classes of AM processes and their commercialization are presented and discussed along with their fields of use. This paper emphasizes AM processes that produce production quality parts. The review also addresses design issues and the commercial state of the art for production of polymer, metal, and ceramic parts. A main emphasis of this paper is the development and motivations for AM especially during its nascent years. The paper is written for the general readership of manufacturing professionals and researchers.

Additive Manufacturing Review: Early Past to Current Practice

Abstract Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative machine learning to design engineering may enable such automated design synthesis and is a research subject of great importance. We present a review and analysis of deep generative machine learning models in engineering design. Deep generative models (DGMs) typically leverage deep networks to learn from an input dataset and synthesize new designs. Recently, DGMs such as feedforward neural networks (NNs), generative adversarial networks (GANs), variational autoencoders (VAEs), and certain deep reinforcement learning (DRL) frameworks have shown promising results in design applications like structural optimization, materials design, and shape synthesis. The prevalence of DGMs in engineering design has skyrocketed since 2016. Anticipating the continued growth, we conduct a review of recent advances to benefit researchers interested in DGMs for design. We structure our review as an exposition of the algorithms, datasets, representation methods, and applications commonly used in the current literature. In particular, we discuss key works that have introduced new techniques and methods in DGMs, successfully applied DGMs to a design-related domain, or directly supported the development of DGMs through datasets or auxiliary methods. We further identify key challenges and limitations currently seen in DGMs across design fields, such as design creativity, handling constraints and objectives, and modeling both form and functional performance simultaneously. In our discussion, we identify possible solution pathways as key areas on which to target the future work. 

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Deep Generative Models in Engineering Design: A Review

\n Supervised machine learning techniques have proven to be effective tools for engineering design exploration and optimization applications, in which they are especially useful for mapping promising or feasible regions of the design space. The design space mappings can be used to inform early-stage design exploration, provide reliability assessments, and aid convergence in multiobjective or multilevel problems that require collaborative design teams. However, the accuracy of the mappings can vary based on problem factors such as the number of design variables, presence of discrete variables, multimodality of the underlying response function, and amount of training data available. Additionally, there are several useful machine learning algorithms available, and each has its own set of algorithmic hyperparameters that significantly affect accuracy and computational expense. This work elucidates the use of machine learning for engineering design exploration and optimization problems by investigating the performance of popular classification algorithms on a variety of example engineering optimization problems. The results are synthesized into a set of observations to provide engineers with intuition for applying these techniques to their own problems in the future, as well as recommendations based on problem type to aid engineers in algorithm selection and utilization.

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A Comparative Evaluation of Supervised Machine Learning Classification Techniques for Engineering Design Applications

Advances in additive manufacturing processes have made it possible to build mechanical metamaterials with bulk properties that exceed those of naturally occurring materials. One class of these metamaterials is structural lattices that can achieve high stiffness to weight ratios. Recent work on geometric projection approaches has introduced the possibility of optimizing these architected lattice designs in a drastically reduced parameter space. The reduced number of design variables enables application of a new class of methods for exploring the design space. This work investigates the use of Bayesian optimization, a technique for global optimization of expensive non-convex objective functions through surrogate modeling. We utilize formulations for implementing probabilistic constraints in Bayesian optimization to aid convergence in this highly constrained engineering problem, and demonstrate results with a variety of stiff lightweight lattice designs.

Design of Mechanical Metamaterials via Constrained Bayesian Optimization

Design for additive manufacturing (DFAM) guidelines are important for helping designers avoid iterations and leverage the design freedoms afforded by additive manufacturing (AM). Comprehensive design guidelines should incorporate a variety of features of interest to designers, and given the wide variety of AM processes and their associated capabilities and limitations, those guidelines may need to be process- or even machine-specific. One way to generate detailed DFAM guidelines is to implement a metrology study focused on a strategically designed test part. This paper describes how quantitative design guidelines are compiled for a polymer selective laser sintering (SLS) process via a metrology study. As part of the metrology study, a test part is designed to focus specifically on geometric resolution and accuracy of the polymer SLS process. The test part is compact, allowing it to be easily inserted into existing SLS builds and therefore eliminating the need for dedicated metrology builds. To build a statistical foundation upon which design guidelines can be compiled, multiple copies of the test part are fabricated within existing commercial builds in a factorial study with materials, build orientations, and locations within the build chamber as control factors. Design guidelines are established by measuring and analyzing the as-built test parts. The guidelines are summarized in this paper and documented in a publicly accessible, online web tool.

Powder bed fusion metrology for additive manufacturing design guidance

As interest in additive manufacturing (AM) grows, design guides are needed for helping designers conceptualize and embody products that are suitable for AM. As these guides begin to emerge, they are focused primarily on the limitations of AM, including the types of features that can and cannot be built with a particular process and the dimensional limitations on those features. To design for AM effectively, however, designers need guides that help them understand not only the limitations of a particular AM process but also the design opportunities and freedoms afforded by the process. Furthermore, developing a basic understanding of the AM process and its relationship to those limitations and capabilities helps designers translate their knowledge to new applications. An expanded type of design guide is needed that fulfills all of these functions for the designer.

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The need for effective design guides in additive manufacturing


 Deep convolutional neural networks have gained significant traction as effective approaches for developing detailed but compact representations of complex structured data. Generative networks in particular have become popular for their ability to mimic data distributions and allow further exploration of them. This attribute can be utilized in engineering design domains, in which the data structures of finite element meshes for analyzing potential designs are well suited to the deep convolutional network approaches that are being developed at a rapid pace in the field of image processing. This paper explores the use of conditional generative adversarial networks (cGANs) as a means of generating a compact latent representation of structures resulting from classical topology optimization techniques. The constraints and contextual factors of a design problem, such as mass fraction, material type, and load location, can then be specified as input conditions to generate potential topologies in a directed fashion. The trained network can be used to aid concept generation, such that engineers can explore a variety of designs relevant to the problem at hand with ease. The latent variables of the generator can also be used as design parameters, and the low dimensionality enables tractable computational design without analytical sensitivities. This paper demonstrates these capabilities and discusses avenues for further developments that would enable the engineering design community to further leverage generative machine learning techniques to their full potential.

Topology Design With Conditional Generative Adversarial Networks

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