International Journal of Advanced Design and Manufacturing Technology
Volume:18 Issue: 4, Winter 2026

Incremental Surface Mechanical Attrition Treatment (SMAT) is a process in which the surface of a component is enhanced by the impact of small steel shots, creating a thin nanostructured layer that improves the mechanical properties of metallic materials. In this process, significant plastic deformation initially occurs due to the impact of steel shots on the surface, and after each shot rebounds, compressive residual stress is generated on the surface. This study numerically investigates the effect of shot size and velocity during the SMAT process on the maximum equivalent stress, equivalent plastic strain profiles, residual stress depth, and maximum compressive residual stress using the finite element method (FEM). The plastic deformation process during SMAT was analyzed using ABAQUS Explicit Software. The Explicit Dynamic solver was employed to analyze the effects of shot velocity and diameter using FEM. Deformation behavior was evaluated under two conditions. The results indicated that the maximum compressive residual stress increased from 202 MPa to 205 MPa as the shot diameter increased from 1 mm to 3 mm at a velocity of 10 m/s, while an increase in velocity from 4 m/s to 10 m/s at a shot diameter of 1 mm resulted in an increase in maximum compressive residual stress from 155 MPa to 202 MPa. The results suggest that shot velocity has a significant effect on residual stress, whereas shot diameter has the less impact. The change in plastic strain due to shot diameter is not as influential as shot velocity.

This study introduces a new method for identifying trading signals in the financial market using machine learning techniques. It employs graphical and correlational research methods, utilizing support learning techniques in MATLAB to test hypotheses. The study proposes an automated trading system combining deep learning and reinforcement learning to determine trade signals and position sizes. The framework combines an LSTM network with Q-learning, an out-of-policy reinforcement learning algorithm. Q-learning aims to maximize overall reward by learning from actions that deviate from the current policy. This study introduces a new framework that utilizes the collective intelligence of multiple expert traders to learn across different time frames. It shows that using Fundamental and technical indicators independently or in combination to train LSTMs for predicting currency movements in Forex significantly improves prediction accuracy. The study introduces a third class to represent small changes in currency pair prices between two consecutive days, improving prediction accuracy. It also describes a new method for determining the most appropriate threshold value for defining the unchanged class. Additionally, the study trains LSTMs to predict values k days into the future, and explores the impact of different training iterations on accuracy values.

Atomic Force Microscopes (AFM) are reliable and accurate tools for surface imaging, mechanical properties detection and measuring particle motion in nanoscale. The vibration behavior of the microcantilver (MC) in an AFM is an extremely crucial factor for its performance. Also, the dimensions of the MC contribute to its vibratory behavior. The exact surface topography of the sample and determining its mechanical properties and behavior requires thorough knowledge of the effects of different geometric parameters on the coefficients of the interaction forces and the vibration of the MC. In this paper, the authors analyze the dynamic behavior of an air piezoelectric MC under electromagnetic actuation. For this purpose, at first a dynamic model of the system was developed using the equation of motion of a continuous beam under vibrations. Then, the effects of the surface interaction force on the behavior of the MC under nonlinear vibrations is investigated. Also, a sensitivity analysis is carried out using Sobol method to study how the dimensions of a MC affect its nonlinear frequency.

The purpose of this article is to recognize and understand the views and mental models of managers of research and technology funds in line with the new concepts of behavioral economics. Research and technology funds are considered as one of the facilitating arms in the economic system, which have a significant contribution to the sustainable development of the country. The main idea is to recognize the viewpoints and subjectivities of these managers as scientific human resources, who influence the advancement of technology and research of the country, in line with the concepts of behavioral economics. The present study includes 13 senior managers of research and technology funds in 2022, which were obtained by purposive sampling and through in-depth interviews to the propositions resulting from Q methodology. The results show 17 verified propositions and three mental models of managers of research and technology funds related to behavioral economics concepts. The results indicate the existence of cognitive and behavioral bias of representativeness, competence, and confirmation in the first mental model, anchoring and adjustment bias, as well as overconfidence bias in the second mental model. In the third mental model obtained from managers, there are bias of eventuality and data dilution. Finally, by providing nudges (suggestions), an attempt has been made to reduce these biases and to see more efficient management decisions.

Many animals utilize jumping as a means of traversing uneven terrain in nature. In fact, animal jumping enables them to overcome obstacles that exceed their body size .Today, there is a clear need for mobile robots that can perform human missions in complex environments instead of humans. Performing these tasks by humans is either risky or costly. Legged robots are more capable of performing missions than wheeled robots. The mechanism of these robots allows them to traverse inaccessible surfaces. In this paper, a two-dimensional four-legged robot model has been introduced. In the next step, the four-legged robot and the walking path are simulated using Adams software. All the physical properties of the four-legged robot, as well as parameters related to jumping, are inputted into the software. Following that, the simulated model is implemented in Adams software, and the four-legged robot jumps on the stairs. The Adams software is used to review jumping path diagrams, robot joint changes, contact forces, as well as kinetic and potential energy.

Epoxy matrix composites reinforced with carbon dot nanoparticles are increasingly utilized in industries due to their enhanced mechanical, thermal, and electrical properties. Drilling, a critical machining process for assembling these composites, often induces defects such as delamination and stress concentration, impacting structural integrity. This study systematically investigates the influence of machining parameters—spindle speed, feed rate, and drill bit diameter—on the thrust force during drilling of carbon dot-reinforced epoxy composites. A full factorial design of experiments was employed, with thrust forces measured using a high-precision load cell. Statistical analysis, conducted via Minitab software, revealed that drill bit diameter is the dominant factor, contributing 66.18% to thrust force variation, followed by spindle speed (19.90%) and feed rate (7.16%). The regression model, with an R-squared value of 98.90%, highlights significant linear and nonlinear interactions among parameters. Increasing feed rate and tool diameter markedly elevate drilling forces, while higher spindle speeds slightly reduce them. The incorporation of carbon dots up to 1 wt.% reduces thrust force by enhancing interfacial bonding, though excessive concentrations may induce embrittlement. Optimization results identify the ideal settings as spindle speed at 2500 rpm, feed rate at 10 mm/min, drill bit diameter at 0.3 mm, and carbon dot concentration at 1 wt.%, achieving a minimal thrust force.