International Journal of Artificial Intelligence in Mechanical Engineering

AI-Driven Recruitment Intelligence System Using SQL, Power BI and Machine Learning

M. Rajeswari, Bestha Revathi — Mon, 06 Jul 2026 00:00:00 +0000

Recruitment teams often handle candidate screening, hiring source review, and reporting through manual steps. This slows down decision-making and hides useful patterns in candidate data. This study presents an AI-driven recruitment intelligence system using SQL, Power BI, and machine learning. The study developed a structured academic prototype that stores candidate records in MySQL, analyzes recruitment patterns with SQL queries, visualizes hiring indicators in Power BI, and predicts candidate hiring status through a supervised Decision Tree model. The system classifies outcomes such as Hired, Rejected, and Screened. It used attributes such as application source, experience, role, location, skill score, and application date to support analysis and prediction. The dashboard presents KPI cards, slicers, hiring trends, source-wise analysis, and prediction-based visuals. The study demonstrates how a small recruitment dataset can become a decision support tool for HR teams. The model accuracy remained high because the dataset was controlled and limited in size. The study works as an academic prototype, yet it reflects a real HR analytics problem. The framework can be extended into a full-stack recruitment application by adding live data entry, recruiter login, candidate fit scoring, resume screening, and larger datasets.

AI-Driven Condition Monitoring and Predictive Maintenance of Fluid Power Systems: A Review of Diagnostic Models and Applications

S K Harisha, Nanjundaradhya N V — Tue, 18 Aug 2026 00:00:00 +0000

Hydraulic and pneumatic machinery, collectively referred to as fluid power systems, underpin a wide range of industrial automation, heavy-equipment, and aerospace actuation applications. Yet these systems remain susceptible to gradual degradation processes, including seal wear, cavitation, fluid contamination, and valve erosion, any of which can trigger unplanned downtime and inflate maintenance expenditure if not caught early. Classical model-based diagnostic techniques generally struggle to represent the nonlinear, stochastic character of fault progression in such machinery. This paper reviews a decade of research on artificial-intelligence-driven condition monitoring and predictive maintenance developed specifically for fluid power systems. It examines both supervised and unsupervised learning strategies, spanning convolutional neural networks (CNNs), long short-term memory (LSTM) networks, autoencoders, support vector machines (SVMs), and more recent transformer-based architectures, as applied to fault detection, fault classification, and remaining useful life (RUL) estimation. Published diagnostic models are assessed against a consistent set of benchmarks, including accuracy, false positive rate, real-time deployability, and sensor requirements, to enable a fair, cross-study comparison. The resulting analysis shows that hybrid deep-learning architectures consistently surpass single-method approaches, reaching fault-classification accuracies above 93% under controlled experimental conditions. The review further highlights persistent gaps in the field, most notably the limited ability of existing models to adapt across differing operating conditions and the continuing shortage of industry-validated, publicly available datasets. The paper closes with a discussion of promising future directions, including physics-informed neural networks and edge-deployable AI architectures.

A Physics-Informed Machine Learning Framework to Accurately Predict Tool Wear and Surface Integrity

Ogagavwodia Ejovi Okuma, Briggs Otekenari Tonye — Mon, 24 Aug 2026 00:00:00 +0000

Accurate prediction of tool wear and surface integrity assessment are key challenges in modern manufacturing, which directly affect manufacturing productivity, cost efficiency, and product quality. Current physics models are not flexible enough for different cutting conditions, and data-driven models need large amounts of labeled data and cannot be based on basic physical laws. This paper introduces a detailed mathematical model that combines Physics-Informed Machine Learning (PIML) techniques—such as Physics-Informed Neural Networks (PINNs), physics-informed Gaussian process regression and hybrid architectures—to simulate tool wear and surface integrity in machining processes. The framework integrates cutting mechanics, thermomechanical state variables and wear kinetics systematically into data-driven models to allow prediction with limited and noisy data from the industry. This discussion covers the latest advances in physics-informed machine learning (PIML) techniques, compares hybrid methods to pure data-driven and physics-based approaches, and highlights research gaps in real-time tool condition monitoring and generalization across cutting parameters. Key results show that a physics-informed model can attain prediction accuracy of R² ≥ 0.92–0.97, underlining the physical consistency, and with fewer training samples than conventional neural networks. Open challenges in uncertainty quantification and on edge devices are discussed, and practical implementations for Industry 4.0 smart manufacturing and remaining useful life estimation are discussed. This work provides a common theoretical and practical framework for physics-informed machine learning in manufacturing systems for various cutting processes and materials.