Abdulcelil Bayar

Functionally graded lattice structures (FGLSs) have emerged as a promising class of architected materials that enable spatial control of mechanical, thermal, and biological properties through variations in geometry, topology, material composition, and density. Advances in additive manufacturing (AM) have significantly expanded the design and fabrication capabilities of FGLSs for applications in aerospace, biomedical, energy, and transportation sectors. This review presents a comprehensive assessment of FGLSs through a process–structure–property (P–S–P) perspective, highlighting the influence of AM processes, manufacturing defects, and microstructural evolution on structural performance. A unified six-axis taxonomy (topology family, graded variable, gradient-field description, scale level, process and material route, target metric) is proposed for cross-study comparison. Five architecture families are examined: strut-based, triply periodic minimal surface (TPMS), stochastic, plate-based, and mechanism-driven or hierarchical lattices, together with density, material, topology, and hybrid grading approaches. Recent advances in computational modeling, topology optimization, artificial intelligence (AI), and machine learning are discussed, emphasizing their role in accelerating design, property prediction, and inverse engineering. Finally, key challenges and future opportunities related to digital twins, autonomous manufacturing, multi-material systems, and qualification of FGLSs are identified. This review provides a structured framework for the development of next-generation AI-enabled FGLSs with enhanced multifunctional performance and manufacturability.

This review paper presents a comprehensive and system-oriented analysis of advanced non-destructive testing (NDT) technologies for metal additive manufacturing (AM), including X-ray computed tomography (XCT), ultrasonic testing (UT), infrared thermography, acoustic emission (AE), and electromagnetic techniques. While the existing literature often focuses on the physical principles of individual NDT methods, this work addresses a critical knowledge gap by analyzing NDT as a digitally integrated “quality intelligence layer” rather than a standalone post-process inspection tool. The primary motivation is to bridge the disconnect between raw inspection data and cyber–physical production systems. Particular focus is given to NDT data analytics and digitalization, where machine learning (ML) and digital twin (DT) integration are discussed as fundamental enablers of intelligent manufacturing. The review systematically examines image and signal processing pipelines required for quantitative defect characterization, highlighting challenges related to voxel resolution, signal-to-noise ratio, anisotropic microstructures, and operator dependency. It further analyzes supervised learning, deep learning, and multi-sensor data fusion approaches for automated defect classification and predictive quality assessment. Furthermore, the role of digital twins in coupling in situ monitoring data, ex situ NDT results, and physics-based models is discussed as a transformative pathway toward closed-loop process control and evidence-based certification. By synthesizing NDT science with digital manufacturing architectures, this review contributes a unique framework for transitioning from traditional inspection-centric quality control to a predictive, adaptive, and digital twin-enabled quality assurance paradigm. The work concludes by identifying key research gaps in data standardization and computational scalability, providing a strategic roadmap for the future of smart AM production.

In this study, Ti-6Al-4V alloys were produced using a 3D metal printing process (EOS M290), and their weight change/oxidation (thermogravimetric analysis - TGA) properties were investigated at different processing speeds. After production by layered manufacturing, the printed samples were subjected to stress relief treatment at 700°C for 2 hours. Thermogravimetric analysis (TGA) results of both the as-printed and stress-relieved samples showed an increase in mass values in all samples, starting at approximately 400°C and becoming more pronounced between 600 and 800°C. When the thermodynamic enthalpy and activation energy of these temperature-dependent phase transformations were calculated, it was observed that the stress relief process caused chemical and physical changes in the materials. After stress relief heat treatment, martensite transforms into the more stable α+β phase. Since the defect density decreases, the diffusion rate of oxygen slows down, and the mass increase was found to be 10% less than in the as-printed case. The activation energy, calculated using the Kissinger equation, was found to be 50% lower compared to the stressrelieving heat treatment of the as-printed sample. This indicates that the energy required to initiate the change is easier. Due to the martensite in its microstructure and high dislocation density, the low activation energy also proved, via SEM images, that it is much more unstable to thermal effects. The test results provide an important information for understanding how stress-relieving heat treatment affects the temperature, oxidation resistance, and phase transformation behavior of Ti-6Al-4V alloys produced via doping methods.

This study investigates the combined effects of drilling parameters and tool features on thrust force during drilling of hybrid AA7050/CFRP stacks intended for aerospace applications. Experiments were designed using a Taguchi L16 (4²2²) orthogonal array by considering feed rate, cutting speed, drill diameter, and coating condition. Unlike most previous studies on Al/CFRP stack drilling, this work provides a layer-specific evaluation by separately analyzing the thrust-force responses of the AA7050 and CFRP layers, while also correlating force behavior with aluminum-side damage and CFRP-side delamination within a unified framework. For the aluminum layer, feed rate was the dominant factor affecting thrust force, with a contribution of 71.22%, followed by drill diameter (26.22%). For the CFRP layer, drill diameter was the most influential parameter (52.58%), followed by feed rate (43.77%). Thrust force ranged from 155.24 to 414.45 N for AA7050 and from 77.73 to 175.39 N for CFRP. Uncoated tools and higher feed rates increased thrust force, leading to poorer machining performance. Delamination in the CFRP layer was primarily governed by feed rate (46.71%), followed by drill diameter (35%). Overall, increasing feed rate and drill diameter significantly deteriorated the machinability of the hybrid structure.

In this study, a boron-alloyed microalloyed steel was subjected to austenitizing and tempering heat treatments, followed by surface coating using the flame spray technique. Ceramic powders containing 87% Al₂O₃ and 13% TiO₂ with an average particle size of approximately 50 μm were used to produce coatings at three different thicknesses. Prior to coating, surface preparation was performed by sandblasting treatment to ensure optimal adhesion. After the coating process, the density of the coatings was measured using the Archimedes principle to indirectly assess porosity, interfacial bonding quality, and overall coating integrity. The phase structure, crystallinity, and structural transformations of the coated surfaces were analyzed by X-ray diffraction (XRD). Additionally, dislocation density values were calculated from the XRD data to evaluate the crystallographic characteristics of the coatings. The mechanical performance was assessed through wear tests and hardness measurements conducted on both coated and uncoated samples. The results demonstrated that the flame spray coating significantly enhanced the surface properties of the boron-alloyed steel, providing up to a 45% improvement in wear resistance. Furthermore, salt spray corrosion testing revealed that the coated specimens exhibited superior corrosion resistance without any visible surface degradation, highlighting the effective environmental protection offered by the Al₂O₃–TiO₂ ceramic coating.

By greatly increasing automation, accuracy, and flexibility at every step of the additive manufacturing process, from design and production to quality assurance, artificial intelligence (AI) is revolutionizing the 3D printing industry. The integration of AI algorithms into 3D printing systems enables real-time optimization of print parameters, accurate prediction of material behavior, and early defect detection using computer vision and sensor data. Machine learning (ML) techniques further streamline the design-to-production pipeline by generating complex geometries, automating slicing processes, and enabling adaptive, self-correcting control during printing—functions that align directly with the principles of Industry 4.0/5.0, where cyber-physical integration, autonomous decision-making, and human–machine collaboration drive intelligent manufacturing systems. Along with improving operational effectiveness and product uniformity, this potent combination of AI and 3D printing also propels the creation of intelligent manufacturing systems that are capable of self-learning. This confluence has the potential to completely transform sectors including consumer products, healthcare, construction, and aerospace as it develops. This comprehensive review explores how AI enhances the capabilities of 3D printing, with a focus on process optimization, defect detection, and intelligent control mechanisms. Moreover, unresolved challenges are highlighted—including data scarcity, limited generalizability across printers and materials, certification barriers in safety-critical domains, computational costs, and the need for explainable AI.