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In this study, Al-4.5Cu alloy was reinforced with varying weight percentages of SiC particles (2%, 4%, 6%, and 8%) to create metal matrix composites via the stir casting method. The formation of intermetallic compounds was confirmed through energy dispersive spectroscopy and x-ray diffraction analysis. This article compares the performance of Artificial Neural Network (ANN) and Gene Expression Programming (GEP) models in predicting the Metal Removal Rate (MRR), tool wear rate, and surface roughness in the die-sinking electro-discharge machining (EDM) process of the ex-situ developed Al-4.5%Cu–SiC composites. The study considers three machine parameters—pulse on time (TON), pulse off time (TOFF), and current (I)—along with the weight fraction of SiC particles as input variables for the models. Both ANN and GEP models demonstrated high predictive accuracy for the EDM performance metrics, with correlation coefficients (R) ranging from 0.973 68 to 0.980 65 for the ANN model and 0.980 11 to 0.982 59 for the GEP model. Notably, the GEP model exhibited superior predictive capability, as evidenced by its higher correlation coefficients and lower root mean square error, indicating greater effectiveness in predicting the EDM process outcomes than the ANN model. [ABSTRACT FROM AUTHOR]

The present research focuses on establishing a sustainable manufacturing paradigm through the development of a model for optimizing machining parameters under a minimum quantity lubricating environment. The variables considered for investigation encompass cutting speed, feed, and depth of cut. To gauge the sustainability, five key indicators were assessed: total energy consumption, total carbon emission, total machining cost, surface roughness, and tool wear incurred during the machining of Inconel 690. Besides, in order to proficiently oversee machining parameters, the essential integration of modeling and optimization strategies assumes paramount significance. Consequently, a combined approach employing Gene Expression Programming (GEP) in conjunction with Particle Swarm Optimization (PSO) was adopted. GEP was employed to model the observed outcomes in relation to the design variables. Subsequently, the PSO technique was applied to ascertain the optimal configuration of machining parameters. Notably, a confirmation test demonstrated a minimal average discrepancy (less than 3%) between the experimental and GEP-PSO predicted results, showcasing the robustness of the hybrid optimization approach. Finally, machining with silica-doped sunflower oil in optimized machining conditions yields 20% energy reduction, 18.68% lower carbon emissions, an 11.76% cost decrement, a 20.21% reduction in surface roughness, and a 31.71% drop in tool wear, as compared to dry machining, making it an eco-friendly and cost-effective option for superalloy machining. [ABSTRACT FROM AUTHOR]

With the increasing emphasis on sustainable manufacturing practices, eco-friendly lubricants have gained significant attention to moderate the friction coefficient at the tool-work interface. In line with this, the contemporary study aimed to examine the viability of Alumina-enriched sunflower bio-oil as a metalworking fluid. Different volume fractions of Alumina nanoparticles (varying from 0 to 1 vol%) were mixed with sunflower bio-oil, and the physical properties, for instance, contact angle and dynamic viscosity, were analyzed to determine the optimal concentration of Alumina. Subsequently, machining experiments were executed on Hastelloy C-276 under various lubricating conditions, including dry cutting, compressed air, sunflower bio-oil, and 0.6 vol% Alumina-sunflower bio-oil. A comparative analysis among these lubricating mediums demonstrated that sunflower bio-oil with a 0.6 vol% Alumina concentration outperformed others, resulting in a significant reduction of surface roughness, and tool wear by 73.31%, and 82.14% respectively when compared to dry machining. Besides, the utilization of 0.6 vol% Alumina-sunflower bio-oil has demonstrated a reduction of 17.86% in total machining cost, along with reductions of 15.44% in energy consumption and carbon emissions, when compared to dry machining. Finally, a Taguchi-designed experiment consisting of sixteen trials was performed in different lubricating conditions, and a Fuzzy-Mamdani model was employed to achieve a sustainable machining environment. The sustainability assessment results indicated that a cutting speed of 75 m/min, feed of 0.05 mm/tooth, depth of cut of 0.15 mm, and the utilization of the 0.6 vol% Alumina-sunflower bio-oil resulted in the most sustainable machining environment, with the highest Multi-Performance Characteristics Index of 0.75. [ABSTRACT FROM AUTHOR]

Quantum computers will eventually reach a size at which quantum error correction becomes imperative. Quantum information can be protected from qubit imperfections and flawed control operations by encoding a single logical qubit in multiple physical qubits. This redundancy allows the extraction of error syndromes and the subsequent detection or correction of errors without destroying the logical state itself through direct measurement. We show the encoding and syndrome measurement of a fault-tolerantly prepared logical qubit via an error detection protocol on four physical qubits, represented by trapped atomic ions. This demonstrates the robustness of a logical qubit to imperfections in the very operations used to encode it. The advantage persists in the face of large added error rates and experimental calibration errors. [ABSTRACT FROM AUTHOR]

We run a selection of algorithms on two state-of-the-art 5-qubit quantum computers that are based on different technology platforms. One is a publicly accessible superconducting transmon device (www.research.ibm.com/ibm-q) with limited connectivity, and the other is a fully connected trapped-ion system. Even though the two systems have different native quantum interactions, both can be programed in a way that is blind to the underlying hardware, thus allowing a comparison of identical quantum algorithms between different physical systems. We show that quantum algorithms and circuits that use more connectivity clearly benefit from a better-connected system of qubits. Although the quantum systems here are not yet large enough to eclipse classical computers, this experiment exposes critical factors of scaling quantum computers, such as qubit connectivity and gate expressivity. In addition, the results suggest that codesigning particular quantum applications with the hardware itself will be paramount in successfully using quantum computers in the future. [ABSTRACT FROM AUTHOR]