Robots are used as machine tools via some advanced properties such as versatility, adaptation and mobility with other control systems. These advantages can be the solution to overcome the encountered problems in electrochemical machining (ECM). In this manner, the usability of a new machining method that is Robotic electrochemical machining (RECM) in freeform surface machining is investigated. Firstly, ECM parameters and their effects on the material removal rate (MRR) is discussed and compared with the literature. Experimental results showed that MRR variation for RECM acts similar characteristics with conventional ECM processes. The increase in voltage, electrolyte conductivity and feed rate increase MRR. The highest effect on MRR for both materials is voltage, electrolyte conductivity, and feed rate, respectively. Secondly, three different machine learning models are designed as Gaussian Process Regression (GPR), regression tree and deep neural network (DNN) models and the relationships between their actual and predicted values are discussed. The performance of the DNN model is better than other models with a correlation of determination values equal for Inconel 718 and AISI 1040. Furthermore, the DNN model with four input parameters outperforms developed models such as GPR and DTR with optimized hyperparameters regarding the R², MSE, RMSE, and MAE. The change in height in the Z-axis and the surface roughness of the machined surfaces are also measured. The results show that the decrease in the Z axis is homogeneous, that is, uniform machining occurs and also the surface roughness has improved. Therefore, RECM can be a solution to machine free-form surfaces for different materials with high predictability.

Machining hard-to-cut materials, such as cobalt (Co)-based superalloys, is a common problem in manufacturing industries. Background: wire electrical discharge machining (W-EDM) is one of the widely used cutting processes that causes layer (white layer—WL and heat-affected zone—HAZ) formation, and microcracks on the material’s surface. Purpose: this study investigates the effects of WL and HAZ on the electrochemical grooving (EC grooving) performance of Co-based superalloys. Two different surface types (W-EDMed and VFed) were used in the experiments. Result: the experiments showed that material removal rate (MRR) values increased up to 212.49% and 122.23% for vibratory finished (VFed) and wire-electrical-discharge-machined (W-EDMed) surfaces, respectively. Conclusion: This result indicates the presence of HAZ and WL that prevent current transition between two electrodes. However, increased voltage causes an increase in surface roughness, with increment rates at 71.13% and 36.08% for VFed and W-EDMed surfaces, respectively. Moreover, for the VFed surfaces, the groove lost its flatness at the bottom after an approximately 100 µm depth due to the different electrochemical machineabilities of HAZ and real surface texture. This result can be attributed to the different microstructures (HAZ and surface texture) showing different electrochemical dissolution rates. Therefore, high-depth distance HAZ and WL must be removed from the workpiece.

This investigation aims to elucidate friction and wear features of additively manufactured recycled-ABS components by utilizing neural network algorithms. In that sense, it is the first initiative in the technical literature and brings fused deposition modeling (FDM) technology, recycled filament-based products, and artificial neural network strategies together to estimate the friction coefficient and volume loss outcomes. In the experimental stage, to provide the required data for five different neural algorithms, dry-sliding wear tests, and hardness measurements were conducted. As FDM printing variables, layer thickness (0.1, 0.2, and 0.3 mm), infill rate (40, 70, and 100 %), and building direction (vertical, and horizontal) were selected. The obtained results pointed out that vertically built samples usually had lower wear resistance than the horizontally built samples. This case can be clarified with the initially measured hardness levels of horizontally built samples and optical microscopic analyses. Besides, the Levenberg Marquard (LM) algorithm was the best option to foresee the wear outputs compared to other approaches. Considering all error levels in this paper, the offered results by neural networks are notably acceptable for the real industrial usage of material, mechanical, and manufacturing engineering areas.

The importance of recycling engineering components and thus obtaining low‐cost production solutions has become prominent in today's world. In this study, the mechanical and dynamic behaviors of three‐dimensional‐printed recycled polyethylene terephthalate glycol (RePET‐G) beams were investigated numerically and experimentally for the first time in the literature. Initially, the governing equations of the beams were determined according to the Bernoulli–Euler beam theory, and these equations were numerically solved using the differential quadrature method and ANSYS program. Subsequently, to validate the accuracy of the numerical models, the obtained natural frequencies were compared with experimental results. It was observed that the numerical results showed good agreement with the experimental results. Finally, the effects of beam length, infill rate, and building direction on the natural frequencies of RePET‐G beams were investigated. The outcomes showed that as the beam length changed, natural frequencies were significantly affected. Increasing the infill rate, especially for beams with vertical building direction, from 20% to 100% led to a slight decrease in the natural frequency values of the structure. Moreover, it was found that for beams with an infill rate of 100%, the natural frequency values obtained in the horizontal building direction were higher than those obtained in the vertical building direction. Highlights: Printable recycled filaments have great potential for vibration applications. Sample length affects the first natural frequency value of RePETG parts. Differential quadrature and ANSYS methods can be utilized for the vibration. For the 3D‐printed samples, rising infill rate causes a natural frequency drop.