Autonomous Robotic Feature-Based Freeform Fabrication Approach

Robotic additive manufacturing (AM) has gained much attention for its continuous material deposition capability with continuously changeable building orientations, reducing support structure volume and post-processing complexity. However, the current robotic additive process heavily relies on manual geometric reasoning that identifies additive features, related building orientations, tool approach direction, trajectory generation, and sequencing all features in a non-collision manner. In addition, multi-directional material accumulation cannot ensure the nozzle always stays above the building geometry. Thus, the collision between these two becomes a significant issue that needs to be solved. Hence, the common use of a robotic additive is hindered by the lack of fully autonomous tools based on the abovementioned issues. We present a systematic approach to the robotic AM process that can automate the abovementioned planning procedures in the aspect of collision-free. Typically, input models to robotic AM have diverse information contents and data formats, hindering the feature recognition, extraction, and relations to the robotic motion. Our proposed method integrates the collision-avoidance condition to the model decomposition step. Therefore, the decomposed volumes can be associated with additional constraints, such as accessibility, connectivity, and trajectory planning. This generates an entire workspace for the robotic additive building platform, rotatability, and additive features to determine the entire sequence and avoid potential collisions. This approach classifies the uniqueness of autonomous manufacturing on the robotic AM system to build large and complex metal components that are non-achievable through traditional one-directional AM in a computationally effective manner. This approach also paves the path in constructing an in situ monitoring and closed-loop control on robotic AM to control and enhance the build quality of the robotic metal AM process.

Control-based continuation: a new approach to prototype synthetic gene networks

Control-Based Continuation (CBC) is a general and systematic method to carry out the bifurcation analysis of physical experiments. CBC does not rely on a mathematical model and thus overcomes the uncertainty introduced when identifying bifurcation curves indirectly through modelling and parameter estimation. We demonstrate, in silico, CBC applicability to biochemical processes by tracking the equilibrium curve of a toggle switch which includes additive process noise and exhibits bistability. We compare results obtained when CBC uses a model-free and model-based control strategy and show that both can track stable and unstable solutions, revealing bistability. We then demonstrate CBC in conditions more representative of a real experiment using an agent-based simulator describing cells growth and division, cell-to-cell variability, spatial distribution, and diffusion of chemicals. We further show how the identified curves can be used for parameter estimation and discuss how CBC can significantly accelerate the prototyping of synthetic gene regulatory networks.

Incompatible Deformations in Additively Fabricated Solids: Discrete and Continuous Approaches

The present paper is intended to show the close interrelationship between non-linear models of solids, produced with additive manufacturing, and models of solids with distributed defects. The common feature of these models is the incompatibility of local deformations. Meanwhile, in contrast with the conventional statement of the problems for solids with defects, the distribution for incompatible local deformations in additively created deformable body is not known a priori, and can be found from the solution of the specific evolutionary problem. The statement of the problem is related to the mechanical and physical peculiarities of the additive process. The specific character of incompatible deformations, evolved in additive manufactured solids, could be completely characterized within a differential-geometric approach by specific affine connection. This approach results in a global definition of the unstressed reference shape in non-Euclidean space. The paper is focused on such a formalism. One more common factor is the dataset which yields a full description of the response of a hyperelastic solid with distributed defects and a similar dataset for the additively manufactured one. In both cases, one can define a triple: elastic potential, gauged at stress-free state, and reference shape, and some specific field of incompatible relaxing distortion, related to the given stressed shape. Optionally, the last element of the triple may be replaced by some geometrical characteristics of the non-Euclidean reference shape, such as torsion, curvature, or, equivalently, as the density of defects. All the mentioned conformities are illustrated in the paper with a non-linear problem for a hyperelastic hollow ball.

On the finiteness and tails of perpetuities under a Lamperti–Kiu MAP

Consider a Lamperti–Kiu Markov additive process $(J, \xi)$ on $\{+, -\}\times\mathbb R\cup \{-\infty\}$, where J is the modulating Markov chain component. First we study the finiteness of the exponential functional and then consider its moments and tail asymptotics under Cramér’s condition. In the strong subexponential case we determine the subexponential tails of the exponential functional under some further assumptions.

Use of 4D-printing and shape memory alloys to fabricate customized metal jewels with functional properties

Purpose This paper aims to examine customized NiTi jewels with functional properties fabricated through four-dimensional (4D)-printing. Design/methodology/approach Two opened rings are fabricated through selective laser melting starting from 55.2Ni-Ti (wt.%) micrometric powder. After the additive process the two rings present the one-way shape memory effect (OWSME). A specific training is accomplished on one of the two printed rings to promote the two-way shape memory effect (TWSME). Both the samples, namely, the rings, respectively, presenting the OWSME and TWSME property, follow a series of post-processing routes to improve the surface finish. Furthermore, a thermal treatment at high temperature is used to create a thin colored oxide layer on the sample surface. Findings Results show that the change of shape owing to the OWSME and TWSME properties allows the customized 4D-printed rings to be adaptable to environmental changes such as load and temperature variations. This adaptability improves comfort and fit of the jewels. Originality/value To the best of the authors’ knowledge, in this work, first cases of additively manufactured NiTi jewels are reported to propose innovative solutions in the design and processing industry of jewels.

Applications of Wrought Austenitic Stainless Steel Corrosion Testing to Laser Powder Bed Fusion 316L

Recent developments in the 3-D printing of austenitic stainless steels have led to the need for standardization of electrochemical techniques used to assess the corrosion performance of these alloys. Currently, ASTM standards for austenitic stainless focus on assessing their resistance to different modes of corrosion such as pitting, crevice, and intergranular corrosion. Due to the complexity of the additive process, selective corrosion occurs in microstructural features such as cellular structures and melt pool boundaries. Standardized corrosion testing needs to incorporate these microstructural features. This study characterizes the corrosion behavior of LPBF stainless steel in a variety of ASTM standards with special attention to melt pool boundary dissolution, cellular structures, and intergranular corrosion.

Process and Evaluation of Automated Robotic Fabrication System for In-Situ Structure Confinement

The additive manufacturing process is gaining momentum in the construction industry with the rapid progression of large-scale 3D printed technologies. An established method of increasing the structural performance of concrete is by wrapping it with Fibre Reinforced Polymer (FRP). This paper proposes a novel additive process to fabricate a FRP formwork by dynamic layer winding of the FRP fabric with epoxy resin paired with an industrial scale robotic arm. A range of prototypes were fabricated to explore and study the fabrication parameters. Based on the systemic exploration, the limitations, the scope, and the feasibility of the proposed additive manufacturing method is studied for large scale customisable structural formworks.

Squashed-Slice Algorithm Based on STEP-NC for Multi-Material and Multi-Directional Additive Processes

Even though additive manufacturing is receiving increasing interest from aerospace, automotive, and shipbuilding, the legacy approach using tessellated form representation and cross-section slice algorithm still has the essential limitation of its inaccuracy of geometrical information and volumetric losses of final outputs. This paper introduces an innovative method to represent multi-material and multi-directional layers defined in boundary-representation standard model and to process complex sliced layers without missing volumes by using the proposed squashing operation. Applications of the proposed method to a bending part, an internal structure, and an industrial moulding product show the assurance of building original shape without missing volume during the comparison with the legacy method. The results show that using boundary representation and te squashing algorithm in the geometric process of additive manufacturing is expected to improve the inaccuracy that was the barrier of applying additive process to various metal industries.

Hybrid Modeling of Melt Pool Geometry in Additive Manufacturing Using Neural Networks

Laser powder-bed fusion is an additive manufacturing (AM) process that offers exciting advantages for the fabrication of metallic parts compared to traditional techniques, such as the ability to create complex geometries with less material waste. However, the intricacy of the additive process and extreme cyclic heating and cooling leads to material defects and variations in mechanical properties; this often results in unpredictable and even inferior performance of additively manufactured materials. Key indicators for the potential performance of a fabricated part are the geometry and temperature of the melt pool during the building process, due to its impact upon the underlining microstructure. Computational models, such as those based on the finite element method, of the AM process can be used to elucidate and predict the effects of various process parameters on the melt pool, according to physical principles. However, these physics-based models tend to be too computationally expensive for real-time process control. Hence, in this work, a hybrid model utilizing neural networks is proposed and demonstrated to be an accurate and efficient alternative for predicting melt pool geometries in AM, which provides a unified description of the melting conditions. The results of both a physics-based finite element model and the hybrid model are compared to real-time experimental measurements of the melt pool during single-layer AM builds using various scanning strategies.