Craig Hamel - Profile on Academia.edu (original) (raw)
Papers by Craig Hamel
Novel Physics-Informed Neural Network Approach for Large-Deformation Mechanics Constitutive Model Calibration
Characterization of Elastomeric Polyurethane Foam of Various Densities
Proposed for presentation at the Society of Experimental Mechanics Annual Conference 2021 ? Virtual held June 14-17, 2021 in Bethel, CT, United States., May 1, 2021
Perspective: Machine learning in experimental solid mechanics
Journal of the Mechanics and Physics of Solids
Data Stewardship and Validation Methods for Mechanics of Materials at Sandia
Proposed for presentation at the Society of Engineering Science Annual Technical Meeting (SES 2022) in ,
Modeling Influences of Printing Defects on Mechanical Properties of Additively Manufactured Silicone Structures
Proposed for presentation at the The 8th European Congress on Computational Methods in Applied Sciences and Engineering held June 5-9, 2022 in Oslo, Norway
Computer Methods in Applied Mechanics and Engineering
The development of highly accurate constitutive models for materials that undergo path-dependent ... more The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.
Strain
The calibration of solid constitutive models with full‐field experimental data is a long‐standing... more The calibration of solid constitutive models with full‐field experimental data is a long‐standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics‐informed deep‐learning framework for the discovery of hyperelastic constitutive model parameterizations given full‐field surface displacement data and global force‐displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for g...
Stabilized Hyperfoam Modeling of the General Plastics EF4003 (3 PCF) Flexible Foam
Mechanics of Materials Utilizing Machine Learning: Examples at Sandia National Laboratories
Proposed for presentation at the Society of Experimental Mechanics Annual Conference 2021 - Virtual held June 14-17, 2021 in Bethel, CT, United States.
Developing Intelligent Structures and Devices Using Novel Smart Materials and Multi-Material Multi-Method (m4) 3D Printing
Structural Health Monitoring 2019
The advent of additive manufacturing (AM), commonly known as 3D printing, has enabled the rapid f... more The advent of additive manufacturing (AM), commonly known as 3D printing, has enabled the rapid fabrication of complex structures previously unrealizable with traditional manufacturing techniques. Current approaches, however, are limited to single materials or single methodologies greatly limiting the potential scope of manufacturable products and components. Recently, our group has developed a novel multi-material multi-method (m4) 3D printer which integrates four AM technologies and two complementary technologies into one single platform. This allows for the fabrication of complex devices able to provide a wide range of functionalities ranging from stretchable electronics to self-sensing devices. To demonstrate these functionalities in the realm of printable electronics, multiple proof of concept printed circuit boards (PCBs) were fabricated which solve issues commonly encountered in 3D printed electronics such as high resolution or vertically integrated access (VIA) circuits. In addition, 3D printed smart structures able to respond to external stimulus, such as light or heat, have become highly desirable for applications ranging from soft robotics to implantable medical devices. Recently, our group has turned to liquid crystal elastomers (LCE), a class of active material able to generate large, rapid, and reversible actuations. Therefore, using the m4 3D printer, LCE-based smart structures requiring complex electronics were fabricated which can change their shape in response to an applied current. To demonstrate this, a smart, reconfigurable radio frequency (RF) antenna was 3D printed which can change its shape and operating frequency as a function of the applied current. These examples demonstrate the vast potential of m4 3D printing for creating smart, reconfigurable, and multi-functional structures.
Machine learning constitutive models of elastomeric foams
Computer Methods in Applied Mechanics and Engineering
Constitutive Modeling of Anisotropic Flexible Foams
An Isotropic Large Deformation Viscoplastic-Damage Model for Flexible Foams Across a Range of Relative Densities
The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance
Extreme Mechanics Letters, 2021
Abstract Mechanical impact protection is an important consideration in many applications, ranging... more Abstract Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under both drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.
Thermomechanical behaviors of polyether ether ketone (PEEK) with stretch-induced anisotropy
Journal of the Mechanics and Physics of Solids, 2021
Abstract Polyether ether ketone (PEEK) is a semi-crystalline thermoplastic polymer with excellent... more Abstract Polyether ether ketone (PEEK) is a semi-crystalline thermoplastic polymer with excellent thermo-mechanical properties, bio-compatibility, corrosion resistance, and 3D printability. Due to these merits, it has wide applications in aeronautics and biomedical devices. However, PEEK's excellent thermo-mechanical properties come from its complicated crystalline domains, making it hard to predict and to design PEEK structures under complex service conditions. In this paper, we studied the thermomechanical behaviors of PEEK with stretch-induced anisotropy and developed a constitutive model to incorporate the influence of the complex loading history along different loading axes. From the experiments, it was found that when it is stretched, PEEK demonstrates viscoplastic behaviors with reduced transversal modulus and yield stress in the subsequent loading, due to the initiation and growth of voids during stretching. The tensile sample also shows a necking behavior at relatively low temperature. To capture these behaviors, the constitutive model consists of two main parts. The undamaged part has three branches, one hyperelastic branch for the nonlinear elastic behavior, one viscoelastic branch for glass transition and relaxation in the amorphous domains, and one plastic branch for yielding and hardening in the crystalline domains. The damaged loose-chain part with history-dependent reduced relaxation time is used to capture the microscopic interface debonding between the crystallites and the amorphous domains. Compared with the experimental results, this model captures the stretch-induced volume expansion and the anisotropic evolution of material properties. This developed model is also able to capture the temperature-dependent necking phenomenon and the corresponding nominal stress-strain behaviors in the uniaxial tensile tests at different strain rates and temperatures. The developed model can be used to facilitate the design of PEEK-based structures under complicated loading conditions.
DEVELOPMENT OF A FINITE ELEMENT METHOD FOR LIGHT ACTIVATED POLYMERS by Craig Hamel Traditional Sh... more DEVELOPMENT OF A FINITE ELEMENT METHOD FOR LIGHT ACTIVATED POLYMERS by Craig Hamel Traditional Shape Memory Polymers (SMPs) belong to a class of smart materials which have shown promise for a wide range of applications. They are characterized by their ability to maintain a temporary deformed shape and return to an original parent permanent shape. The first SMPs developed responded to changes in temperature by exploiting the difference in modulus and chain mobility through the glass transition temperature. However, in recent years, new SMPs have been developed that respond to other stimuli besides temperature; these can include electricity, magnetism, changes in chemical concentration, and even light. In this thesis, we consider the photo-mechanical behavior of Light Activated Shape Memory Polymers (LASMPs), focusing on the numerical aspects. The mechanics behind LASMPS is rather abstract and cumbersome, even for simple geometries. In order to move these materials out of the lab and ...
Materials, design, and fabrication of shape programmable polymers
Multifunctional Materials, 2020
Programmable matter is a class of materials whose properties can be programmed to achieve a speci... more Programmable matter is a class of materials whose properties can be programmed to achieve a specific state upon a stimulus. Among them, shape programmable materials can change their shape, topographical architecture, or dimension triggered by external stimuli after material fabrication, finding broad applications in smart devices, soft robotics, actuators, reconfigurable metamaterials, and biomedical devices. Shape programmable polymers (SPPs) possess the advantages of low cost, the ability to achieve widely tunable stimuli response, and synthetic flexibility. Recent development has resulted in various new materials and fabrication techniques for SPPs. However, to better design and fabricate SPPs to satisfy specific applications, a more comprehensive understanding of SPPs is required. In this review, we provide state-of-the-art advances in materials, design methods, and fabrication techniques for SPPs. Based on different shape-shifting mechanisms, four most widely studied shape-shif...
The modelling and 3D printing of functionally graded foams for tunable crushing performance
Proposed for presentation at the Society of Engineering Sciences held September 28 - October 1, 2020 in Virtual., 2020
Advanced Intelligent Systems, 2020
Hard‐magnetic soft active materials (hmSAMs), embedding hard‐magnetic particles in soft polymeric... more Hard‐magnetic soft active materials (hmSAMs), embedding hard‐magnetic particles in soft polymeric matrices, have attracted a great number of research interests due to their fast‐transforming, untethered control, as well as excellent programmability. However, the current direct‐ink‐write (DIW) printing‐based fabrication of hmSAM parts and structures only permits programmable magnetic direction with a constant magnetic density. Also, the existing designs rely on the brute‐force approach to generate the assignment of magnetization direction distribution, which can only produce intuitional deformations. These two factors greatly limit the design space and the application potentials of hmSAMs. Herein, a “voxel‐encoding DIW printing” method to program both the magnetic density and direction distributions during hmSAM printing is introduced. The voxel‐encoding DIW printing is then integrated with an evolutionary algorithm (EA)‐based design strategy to achieve the desired magnetic actuation...
Integrating digital light processing with direct ink writing for hybrid 3D printing of functional structures and devices
Additive Manufacturing, 2021
Abstract As an emerging branch of additive manufacturing, multi-material 3D printing has drawn tr... more Abstract As an emerging branch of additive manufacturing, multi-material 3D printing has drawn tremendous attention as it offers more design flexibility that can combine materials with various mechanical, chemical, thermal-mechanical or electrical properties. However, low cost, high-speed, high-resolution, and versatile multi-material 3D printing methods are still lacking. In this paper, we present a new hybrid multi-material 3D printing system that consists of a top-down digital light processing (DLP) printing and a direct ink writing (DIW) printing to fabricate composite structures and unique devices in a single printing job. The vat photopolymerization-based DLP printing allows for high-speed and high-resolution printing of a material matrix with complex geometry. The material extrusion-based DIW printing enables the printing of functional material, including liquid crystal elastomers (LCEs) and conductive silver inks. With this hybrid 3D printing system, a wide choice of inks and resins can be used to print functional composites with tunable mechanical properties, enhanced interfacial bonding, and multifunctionality. We demonstrate that composites prototype, active soft robots, circuit-embedding architectures, and strain sensors can be successfully printed. This work provides a new and robust approach for 3D printing of multi-functional devices for broad applications in soft robotics, electronics, active metamaterials, and biomedical devices.
Novel Physics-Informed Neural Network Approach for Large-Deformation Mechanics Constitutive Model Calibration
Characterization of Elastomeric Polyurethane Foam of Various Densities
Proposed for presentation at the Society of Experimental Mechanics Annual Conference 2021 ? Virtual held June 14-17, 2021 in Bethel, CT, United States., May 1, 2021
Perspective: Machine learning in experimental solid mechanics
Journal of the Mechanics and Physics of Solids
Data Stewardship and Validation Methods for Mechanics of Materials at Sandia
Proposed for presentation at the Society of Engineering Science Annual Technical Meeting (SES 2022) in ,
Modeling Influences of Printing Defects on Mechanical Properties of Additively Manufactured Silicone Structures
Proposed for presentation at the The 8th European Congress on Computational Methods in Applied Sciences and Engineering held June 5-9, 2022 in Oslo, Norway
Computer Methods in Applied Mechanics and Engineering
The development of highly accurate constitutive models for materials that undergo path-dependent ... more The development of highly accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions and from the viewpoint of data availability, verification, and validation. Recently, data-driven modeling approaches have been proposed that aim to establish stress-evolution laws that avoid user-chosen functional forms by relying on machine learning representations and algorithms. However, these approaches not only require a significant amount of data but also need data that probes the full stress space with a variety of complex loading paths. Furthermore, they rarely enforce all necessary thermodynamic principles as hard constraints. Hence, they are in particular not suitable for low-data or limited-data regimes, where the first arises from the cost of obtaining the data and the latter from the experimental limitations of obtaining labeled data, which is commonly the case in engineering applications. In this work, we discuss a hybrid framework that can work on a variable amount of data by relying on the modularity of the elastoplasticity formulation where each component of the model can be chosen to be either a classical phenomenological or a data-driven model depending on the amount of available information and the complexity of the response. The method is tested on synthetic uniaxial data coming from simulations as well as cyclic experimental data for structural materials. The discovered material models are found to not only interpolate well but also allow for accurate extrapolation in a thermodynamically consistent manner far outside the domain of the training data. This ability to extrapolate from limited data was the main reason for the early and continued success of phenomenological models and the main shortcoming in machine learning-enabled constitutive modeling approaches. Training aspects and details of the implementation of these models into Finite Element simulations are discussed and analyzed.
Strain
The calibration of solid constitutive models with full‐field experimental data is a long‐standing... more The calibration of solid constitutive models with full‐field experimental data is a long‐standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics‐informed deep‐learning framework for the discovery of hyperelastic constitutive model parameterizations given full‐field surface displacement data and global force‐displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for g...
Stabilized Hyperfoam Modeling of the General Plastics EF4003 (3 PCF) Flexible Foam
Mechanics of Materials Utilizing Machine Learning: Examples at Sandia National Laboratories
Proposed for presentation at the Society of Experimental Mechanics Annual Conference 2021 - Virtual held June 14-17, 2021 in Bethel, CT, United States.
Developing Intelligent Structures and Devices Using Novel Smart Materials and Multi-Material Multi-Method (m4) 3D Printing
Structural Health Monitoring 2019
The advent of additive manufacturing (AM), commonly known as 3D printing, has enabled the rapid f... more The advent of additive manufacturing (AM), commonly known as 3D printing, has enabled the rapid fabrication of complex structures previously unrealizable with traditional manufacturing techniques. Current approaches, however, are limited to single materials or single methodologies greatly limiting the potential scope of manufacturable products and components. Recently, our group has developed a novel multi-material multi-method (m4) 3D printer which integrates four AM technologies and two complementary technologies into one single platform. This allows for the fabrication of complex devices able to provide a wide range of functionalities ranging from stretchable electronics to self-sensing devices. To demonstrate these functionalities in the realm of printable electronics, multiple proof of concept printed circuit boards (PCBs) were fabricated which solve issues commonly encountered in 3D printed electronics such as high resolution or vertically integrated access (VIA) circuits. In addition, 3D printed smart structures able to respond to external stimulus, such as light or heat, have become highly desirable for applications ranging from soft robotics to implantable medical devices. Recently, our group has turned to liquid crystal elastomers (LCE), a class of active material able to generate large, rapid, and reversible actuations. Therefore, using the m4 3D printer, LCE-based smart structures requiring complex electronics were fabricated which can change their shape in response to an applied current. To demonstrate this, a smart, reconfigurable radio frequency (RF) antenna was 3D printed which can change its shape and operating frequency as a function of the applied current. These examples demonstrate the vast potential of m4 3D printing for creating smart, reconfigurable, and multi-functional structures.
Machine learning constitutive models of elastomeric foams
Computer Methods in Applied Mechanics and Engineering
Constitutive Modeling of Anisotropic Flexible Foams
An Isotropic Large Deformation Viscoplastic-Damage Model for Flexible Foams Across a Range of Relative Densities
The 3D printing and modeling of functionally graded Kelvin foams for controlling crushing performance
Extreme Mechanics Letters, 2021
Abstract Mechanical impact protection is an important consideration in many applications, ranging... more Abstract Mechanical impact protection is an important consideration in many applications, ranging from product transportation to sports. Cellular materials are typically used due to their desirable energy absorption properties and light weight. However, their large deformation and rate dependent responses (especially of polymer foams) are challenging to consider in design. Additionally, the use of foams with uniform properties, such as uniform density and uniform stiffness, often restricts the designed foams to only be suitable for a narrow range of mechanical impact conditions whereas real applications commonly face unpredictable situations. 3D printing offers fabrication flexibility and thus opens the door to create foams with tailored properties. In this work, we investigate the feasibility of using 3D printing for functionally graded foams (FGFs) that are optimal over a broad range of mechanical environments. The foams are fabricated by the recently developed grayscale digital light processing (g-DLP) method which can print parts with locally designed properties. These foams are tested under both drop test conditions and with slower displacement control. We also model the large deformation behavior of FGFs using finite element analysis in which we account for the different viscoelastic behaviors of the distinct grayscale regions. We then use the model to examine the impact mitigation capabilities of FGFs in different loading scenarios. Finally, we show how FGFs can be used to satisfy real-world design goals using the case study of a motorcycle helmet. In contrast to prior work, we investigate continuous, functionally graded foams of a single density that differ in their viscoelastic responses. This work provides further insight into the benefits of viscoelastic properties and modulus graded foams and presents a manufacturing approach that can be used to produce the next generation of flexible lattice foams as mechanical absorbers.
Thermomechanical behaviors of polyether ether ketone (PEEK) with stretch-induced anisotropy
Journal of the Mechanics and Physics of Solids, 2021
Abstract Polyether ether ketone (PEEK) is a semi-crystalline thermoplastic polymer with excellent... more Abstract Polyether ether ketone (PEEK) is a semi-crystalline thermoplastic polymer with excellent thermo-mechanical properties, bio-compatibility, corrosion resistance, and 3D printability. Due to these merits, it has wide applications in aeronautics and biomedical devices. However, PEEK's excellent thermo-mechanical properties come from its complicated crystalline domains, making it hard to predict and to design PEEK structures under complex service conditions. In this paper, we studied the thermomechanical behaviors of PEEK with stretch-induced anisotropy and developed a constitutive model to incorporate the influence of the complex loading history along different loading axes. From the experiments, it was found that when it is stretched, PEEK demonstrates viscoplastic behaviors with reduced transversal modulus and yield stress in the subsequent loading, due to the initiation and growth of voids during stretching. The tensile sample also shows a necking behavior at relatively low temperature. To capture these behaviors, the constitutive model consists of two main parts. The undamaged part has three branches, one hyperelastic branch for the nonlinear elastic behavior, one viscoelastic branch for glass transition and relaxation in the amorphous domains, and one plastic branch for yielding and hardening in the crystalline domains. The damaged loose-chain part with history-dependent reduced relaxation time is used to capture the microscopic interface debonding between the crystallites and the amorphous domains. Compared with the experimental results, this model captures the stretch-induced volume expansion and the anisotropic evolution of material properties. This developed model is also able to capture the temperature-dependent necking phenomenon and the corresponding nominal stress-strain behaviors in the uniaxial tensile tests at different strain rates and temperatures. The developed model can be used to facilitate the design of PEEK-based structures under complicated loading conditions.
DEVELOPMENT OF A FINITE ELEMENT METHOD FOR LIGHT ACTIVATED POLYMERS by Craig Hamel Traditional Sh... more DEVELOPMENT OF A FINITE ELEMENT METHOD FOR LIGHT ACTIVATED POLYMERS by Craig Hamel Traditional Shape Memory Polymers (SMPs) belong to a class of smart materials which have shown promise for a wide range of applications. They are characterized by their ability to maintain a temporary deformed shape and return to an original parent permanent shape. The first SMPs developed responded to changes in temperature by exploiting the difference in modulus and chain mobility through the glass transition temperature. However, in recent years, new SMPs have been developed that respond to other stimuli besides temperature; these can include electricity, magnetism, changes in chemical concentration, and even light. In this thesis, we consider the photo-mechanical behavior of Light Activated Shape Memory Polymers (LASMPs), focusing on the numerical aspects. The mechanics behind LASMPS is rather abstract and cumbersome, even for simple geometries. In order to move these materials out of the lab and ...
Materials, design, and fabrication of shape programmable polymers
Multifunctional Materials, 2020
Programmable matter is a class of materials whose properties can be programmed to achieve a speci... more Programmable matter is a class of materials whose properties can be programmed to achieve a specific state upon a stimulus. Among them, shape programmable materials can change their shape, topographical architecture, or dimension triggered by external stimuli after material fabrication, finding broad applications in smart devices, soft robotics, actuators, reconfigurable metamaterials, and biomedical devices. Shape programmable polymers (SPPs) possess the advantages of low cost, the ability to achieve widely tunable stimuli response, and synthetic flexibility. Recent development has resulted in various new materials and fabrication techniques for SPPs. However, to better design and fabricate SPPs to satisfy specific applications, a more comprehensive understanding of SPPs is required. In this review, we provide state-of-the-art advances in materials, design methods, and fabrication techniques for SPPs. Based on different shape-shifting mechanisms, four most widely studied shape-shif...
The modelling and 3D printing of functionally graded foams for tunable crushing performance
Proposed for presentation at the Society of Engineering Sciences held September 28 - October 1, 2020 in Virtual., 2020
Advanced Intelligent Systems, 2020
Hard‐magnetic soft active materials (hmSAMs), embedding hard‐magnetic particles in soft polymeric... more Hard‐magnetic soft active materials (hmSAMs), embedding hard‐magnetic particles in soft polymeric matrices, have attracted a great number of research interests due to their fast‐transforming, untethered control, as well as excellent programmability. However, the current direct‐ink‐write (DIW) printing‐based fabrication of hmSAM parts and structures only permits programmable magnetic direction with a constant magnetic density. Also, the existing designs rely on the brute‐force approach to generate the assignment of magnetization direction distribution, which can only produce intuitional deformations. These two factors greatly limit the design space and the application potentials of hmSAMs. Herein, a “voxel‐encoding DIW printing” method to program both the magnetic density and direction distributions during hmSAM printing is introduced. The voxel‐encoding DIW printing is then integrated with an evolutionary algorithm (EA)‐based design strategy to achieve the desired magnetic actuation...
Integrating digital light processing with direct ink writing for hybrid 3D printing of functional structures and devices
Additive Manufacturing, 2021
Abstract As an emerging branch of additive manufacturing, multi-material 3D printing has drawn tr... more Abstract As an emerging branch of additive manufacturing, multi-material 3D printing has drawn tremendous attention as it offers more design flexibility that can combine materials with various mechanical, chemical, thermal-mechanical or electrical properties. However, low cost, high-speed, high-resolution, and versatile multi-material 3D printing methods are still lacking. In this paper, we present a new hybrid multi-material 3D printing system that consists of a top-down digital light processing (DLP) printing and a direct ink writing (DIW) printing to fabricate composite structures and unique devices in a single printing job. The vat photopolymerization-based DLP printing allows for high-speed and high-resolution printing of a material matrix with complex geometry. The material extrusion-based DIW printing enables the printing of functional material, including liquid crystal elastomers (LCEs) and conductive silver inks. With this hybrid 3D printing system, a wide choice of inks and resins can be used to print functional composites with tunable mechanical properties, enhanced interfacial bonding, and multifunctionality. We demonstrate that composites prototype, active soft robots, circuit-embedding architectures, and strain sensors can be successfully printed. This work provides a new and robust approach for 3D printing of multi-functional devices for broad applications in soft robotics, electronics, active metamaterials, and biomedical devices.