This research studied the effect of recycled steel fibers extracted from twisted cable waste on the fresh and hardened states of high-performance concretes. Accordingly, slump, water absorption (WA), compressive strength (CS), flexural strength (FS), and split tensile strength (STS) were measured in the laboratory using mixtures generated by the response surface methodology (RSM). The RSM-based central composite design (CCD) was used to assess the influence of water-to-binder (W/B) ratios from 0.27 to 0.31, length-to-diameter (L/d = 46 to 80) and steel fiber content (SFC) in the range of 19 to 29 kg/m³ on the behavior of high-performance fiber-reinforced concrete (HPFRC). The accuracy and validation of the generated models were evaluated by employing analysis of variance (ANOVA) and optimal parameters. The experimental findings revealed that the use of an L/d ratio of 63, a W/B ratio of approximately 0.28, and an SFC of around 22 kg/m³ resulted in high workability in terms of slump. While a notable increase in compressive strength was observed when employing an L/d ratio of approximately 70, a W/B ratio of around 0.28, and the maximum SFC of 29 kg/m³, as confirmed by scanning electron microscopy (SEM) and X-ray diffraction (XRD) analysis.

This study aims to optimize the turning parameters for EN-GJL-250 grey cast iron using hybrid machine learning techniques integrated with multi-objective optimization algorithms. The experimental design focused on evaluating the impact of cutting tool type, testing three tools: uncoated and coated silicon nitride (Si₃N₄) ceramic inserts and coated cubic boron nitride (CBN). Key cutting parameters such as depth of cut (ap), feed rate (f), and cutting speed (Vc) were varied to examine their effects on surface roughness (Ra), cutting force (Fr), and power consumption (Pc). The results showed that the coated Si₃N₄ tool achieved the best surface finish, with minimal cutting force and power consumption, while the uncoated Si₃N₄ and CBN tools performed slightly worse. Advanced optimization models including improved grey wolf optimizer–deep neural networks (DNN-IGWOs), genetic algorithm–deep neural networks (DNN-GAs), and deep neural network–extended Kalman filters (DNN-EKF) were compared with traditional methods like Support Vector Machines (SVMs), Decision Trees (DTs), and Levenberg–Marquardt (LM). The DNN-EKF model demonstrated exceptional predictive accuracy with an R² value of 0.99. The desirability function (DF) method identified the optimal machining parameters for the coated Si₃N₄ tool: ap = 0.25 mm, f = 0.08 mm/rev, and Vc = 437.76 m/min. At these settings, Fr ranged between 46.424 and 47.405 N, Ra remained around 0.520 µm, and Pc varied between 386.518 W and 392.412 W. The multi-objective grey wolf optimization (MOGWO) further refined these parameters to minimize Fr, Ra, and Pc. This study demonstrates the potential of integrating machine learning and optimization techniques to significantly enhance manufacturing efficiency.