Effects of wall thickness variation on hydrogen embrittlement susceptibility of additively manufactured 316L stainless steel with lattice auxetic structures

Abstract In the present study, the hydrogen embrittlement (HE) susceptibility of an additively manufactured (AM) 316L stainless steel (SS) was investigated. The materials were fabricated in the form of a lattice auxetic structure with three different strut thicknesses, 0.6, 1, and 1.4 mm, by the laser powder bed fusion technique at a volumetric energy of 70 J·mm⁻³. The effect of H charging on the strength and ductility of the lattice structures was evaluated by conducting tensile testing of the H-charged specimens at a slow strain rate of 4 × 10⁻⁵ s⁻¹. Hydrogen was introduced to the specimens via electrochemical charging in an NaOH aqueous solution for 24 h at 80 °C before the tensile testing. The microstructure evolution of the H-charged materials was studied using the electron backscattered diffraction (EBSD) technique. The study revealed that the auxetic structures of the AM 316L-SS exhibited a slight reduction in mechanical properties after H charging. The tensile strength was slightly decreased regardless of the thickness. However, the ductility was significantly reduced with increasing thickness. For instance, the strength and uniform elongation of the auxetic structure of the 0.6 mm thick strut were 340 MPa and 17.4% before H charging, and 320 MPa and 16.7% after H charging, respectively. The corresponding values of the counterpart’s 1.4 mm thick strut were 550 MPa and 29% before H charging, and 523 MPa and 23.9% after H charging, respectively. The fractography of the fracture surfaces showed the impact of H charging, as cleavage fracture was a striking feature in H-charged materials. Furthermore, the mechanical twins were enhanced during tensile straining of the H-charged high-thickness material

Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data

Modeling of soft robotics systems proves to be an extremely difficult task, due to the large deformation of the soft materials used to make such robots. Reliable and accurate models are necessary for the control task of these soft robots. In this paper, a data-driven approach using machine learning is presented to model the kinematics of Soft Pneumatic Actuators (SPAs). An Echo State Network (ESN) architecture is used to predict the SPA’s tip position in 3 axes. Initially, data from actual 3D printed SPAs is obtained to build a training dataset for the network. Irregular-intervals pressure inputs are used to drive the SPA in different actuation sequences. The network is then iteratively trained and optimized. The demonstrated method is shown to successfully model the complex non-linear behavior of the SPA, using only the control input without any feedback sensory data as additional input to the network. In addition, the ability of the network to estimate the kinematics of SPAs with different orientation angles θ is achieved. The ESN is compared to a Long Short-Term Memory (LSTM) network that is trained on the interpolated experimental data. Both networks are then tested on Finite Element Analysis (FEA) data for other θ angle SPAs not included in the training data. This methodology could offer a general approach to modeling SPAs with varying design parameters

Underwater Soft Robotics: A Review of Bioinspiration in Design, Actuation, Modeling, and Control

Nature and biological creatures are some of the main sources of inspiration for humans. Engineers have aspired to emulate these natural systems. As rigid systems become increasingly limited in their capabilities to perform complex tasks and adapt to their environment like living creatures, the need for soft systems has become more prominent due to the similar complex, compliant, and flexible characteristics they share with intelligent natural systems. This review provides an overview of the recent developments in the soft robotics field, with a focus on the underwater application frontier

Workspace Analysis and Path Planning of a Novel Robot Configuration with a 9-DOF Serial-Parallel Hybrid Manipulator (SPHM)

The development of serial or parallel manipulator robots is constantly increasing due to the need for faster productivity and higher accuracy. Therefore, researchers have turned to combining both mechanisms, sharing the advantage from serial to parallel or vice versa. This paper proposes a new configuration design for a serial-parallel hybrid manipulator (SPHM) using the industrial robotic KUKA Kr6 R900 and 3-DOF parallel spherical mechanism. The Kr6 R900 has six degrees of freedom (6-DOF) divided into three joints for translation (x, y, z) and another three joints for orientation (A, B, C) of the end-effector and the 3-DOF parallel spherical mechanism with three paired links. On the contrary, each limb of the parallel spherical mechanism consists of revolute–revolute–spherical joints (3-RRS). This mechanism allows translation movement along the Z-axis and orientation movements about the X- and Y- axes. The new hybrid will enrich the serial manipulator in movement flexibility and expand the workspace for serial and parallel manipulator robots. In addition, a complete conceptual design is presented in detail for the new robot configuration with a schematic and experimental setup. Then, a comprehensive mathematical model was derived and solved. The forward, inverse kinematics, and workspace analyses were derived using the graphical solution. Additionally, the new hybrid manipulator was tested for path planning. Moreover, an experimental setup was prepared to test the selected path. Finally, the new robot configuration can enlarge the workspace of both manipulators and the selected path matched to the experimental test