Data-Driven Thermal Optimization of Drill Geometry in Titanium Machining: FEM Modeling and Experimental Insights Ahmet Atak, Haider Khazal, Baydaa K. Khudhair, Raheem Al-Sabur, Hassanein I. Khalaf, et al. Journal of Manufacturing and Materials Processing, 2026 The current study offers a deeper understanding of the thermal behavior of AISI 420 stainless-steel drill bits during titanium alloy machining. It utilizes non-linear simulations with the finite element method (FEM) to analyze heat generation, accumulation, and dissipation. The FEM formulation displays the time-dependent temperatures for the tool and hole during the drilling process. The simulation was examined during drilling and subsequent stages, up to room temperature. The study explored a wide range of drill bit lengths (60–160 mm) and tool diameters (2–10 mm). Significant convergence of 4.1% was achieved when compared to infrared thermography data. Furthermore, increasing the tool length beyond 120 mm did not significantly increase the thermal effect. Moreover, increasing the tool diameter up to 10 mm also did not significantly increase the thermal efficiency compared to tool diameters between 2 and 5 mm based on a constant tool length. An exploratory data analysis (EDA) heatmap correlation matrix was used to examine the most efficient variables and the optimum tool geometry. The results obtained provide a clear understanding of the optimal geometry choice for steel drilling tools when used in drilling titanium alloys.
In-Depth Thermal Analysis of Different Pin Configurations in Friction Stir Spot Welding of Similar and Dissimilar Alloys Sajad N. Alasdi, Raheem Al-Sabur Journal of Manufacturing and Materials Processing, 2025 Over the past decade, friction stir spot welding (FSSW) has gained increasing attention, making it a competitor to conventional welding methods such as resistance welding, rivets, and screws. This type of welding is environmentally friendly because it does not require welding tools and is solid-state welding. This study attempts to demonstrate the importance of pin geometry on temperature distribution and joint quality by using threaded and non-threaded pins for similar and dissimilar alloys. To this end, thermal analysis of the welded joints was conducted using real-time monitoring from a thermal camera and an infrared thermometer, in addition to finite element method (FEM) simulations. The thermal analysis showed that the generated temperatures were higher in dissimilar alloys (Al-Cu) than in similar ones (Al-Al), reaching about 350 °C. In addition, dissimilar alloys show more pronounced FSSW stages through extended periods for each plunging, dwelling, and drawing-out time. The FEM simulation results are consistent with those obtained from thermal imaging cameras and infrared thermometers. The dwelling time was influential, as the higher it was, the more heat was generated, which could be close to the melting point, especially in aluminum alloys. This study provides an in-depth experimental and numerical investigation of temperature distribution throughout the welding cycle, utilizing different pin geometries for both similar and dissimilar non-ferrous alloy joints, offering valuable insights for advanced industrial welding applications.
Unveiling Surface Roughness Trends and Mechanical Properties in Friction Stir Welded Similar Alloys Joints Using Adaptive Thresholding and Grayscale Histogram Analysis Haider Khazal, Azzeddine Belaziz, Raheem Al-Sabur, Hassanein I. Khalaf, Zerrouki Abdelwahab Journal of Manufacturing and Materials Processing, 2025 Surface roughness plays a vital role in determining surface integrity and function. Surface irregularities or reduced quality near the surface can contribute to material failure. Surface roughness is considered a crucial factor in estimating the fatigue life of structures welded by FSW. This study attempts to provide a deeper understanding of the nature of the surface formation and roughness of aluminum joints during FSW processes. In order to form more efficient joints, the frictional temperature generated was monitored until reaching 450 °C, where the transverse movement of the tool and the joint welding began. Hardness and tensile tests showed that the formed joints were good, which paved the way for more reliable surface roughness measurements. The surface roughness of the weld joint was measured along the weld line at three symmetrical levels using welding parameters that included a rotational speed of 1250 rpm, a welding speed of 71 mm/min, and a tilt angle of 1.5°. The average hardness in the stir zone was measured at 64 HV, compared to 50 HV in the base material, indicating a strengthening effect induced by the welding process. In terms of tensile strength, the FSW joint exhibited a maximum force of 2.759 kN. Average roughness (Rz), arithmetic center roughness (Ra), and maximum peak-to-valley height (Rt) were measured. The results showed that along the weld line and at all levels, the roughness coefficients (Rz, Ra, and Rt) gradually increased from the beginning of the weld line to its end. The roughness Rz varies from start to finish, ranging between 9.84 μm and 16.87 μm on the RS and 8.77 μm and 13.98 μm on the AS, leveling off slightly toward the end as the heat input stabilizes. The obtained surface roughness and mechanical properties can give an in-depth understanding of the joint surface forming and increase the ability to overcome cracks and defects. Consequently, this approach, using adaptive thresholding image processing coupled with grayscale histogram analysis, yielded significant understanding of the FSW joint’s surface texture.
Optimizing heat exchanger efficiency: Predictive modeling to minimize fouling in crude oil refining Kouidri Ikram, Kaid-Ameur Djilali, Nadjem Bailek, Jihad A. Younis, Dahmani Abdennasser, et al. Advances in Mechanical Engineering, 2025 Fouling in heat exchangers significantly compromises energy efficiency in crude oil refining, leading to increased operational costs and environmental impacts. This study presents a predictive model aimed at enhancing heat exchanger performance by minimizing fouling resistance. Model fitting was conducted using approaches of varying complexity, with measures taken to avoid overfitting. The models were subsequently refined to incorporate key variables, such as inlet and outlet temperatures and mass flow rates, ensuring robustness and generalizability. The final simplified model comprises only 19 terms, yet achieved high predictive performance ( R 2 = 0.961; predicted R 2 = 0.956) and effectively addressed multicollinearity. The selected model identified significant linear, quadratic, and interaction effects among thermal and flow parameters, with the mass flow rates of the hot fluid (MFH) and cold fluid (MFC) emerging as particularly influential. Notably, the model demonstrated that fouling resistance decreases substantially with increasing hot fluid flow rate. Optimization using a desirability function identified 37 parameter combinations that achieved a fouling resistance (RFC) of 0.001 m 2 °C/W with a maximum desirability score of 1.00, consistently favoring high MFH values (~93.00 kg/s) and hot fluid outlet temperatures (THO) near 43.00°C. These findings confirm the model’s robustness and practical applicability, providing actionable insights for operational strategies aimed at minimizing fouling while maintaining thermal efficiency.
Mastering friction stir welding (FSW) with machine learning (ML): A comprehensive guide to algorithms and applications Raheem Al-Sabur, Akshansh Mishra, Hassanein I. Khalaf Using Computational Intelligence for Sustainable Manufacturing of Advanced Materials, 2025 This chapter introduces machine learning (ML) in friction stir welding (FSW), a solid-state welding process that has gained significant attention in research and application. The chapter discusses five primary ML methods: artificial neural networks (ANNs), support vector machines (SVM), random forests (RF), particle swarm optimisation (PSO), and convolutional neural networks (CNNs). The chapter emphasizes the successful application of ANNs in optimizing FSW process parameters and predicting tool wear, tensile failure, and fracture positions. CNNs are shown to be effective for microstructure studies and image detection, while SVM is a good tool for FSW process monitoring and temperature control. RF is demonstrated to have good abilities in investigating welding defects and tool monitoring, while PSO is frequently used in FSW welding bead studies. The chapter provides a straightforward methodology for those interested in utilising ML in welding studies, particularly for FSW.
Design and Modeling of an Intelligent Robotic Gripper Using a Cam Mechanism with Position and Force Control Using an Adaptive Neuro-Fuzzy Computing Technique Imad A. Kheioon, Raheem Al-Sabur, Abdel-Nasser Sharkawy Automation, 2025 Manufacturers increasingly turn to robotic gripper designs to improve the efficiency of gripping and moving objects and provide greater flexibility to these objects. Neuro-fuzzy techniques are the most widespread in developing gripper designs. In this study, the traditional gripper design is modified by adding a suitable cam that makes it compatible with the basic design, and an adaptive neuro-fuzzy inference system (ANFIS) is used in a MATLAB Simulink environment. The developed gripper investigates the follower path concerning the cam surface curve, and the gripper position is controlled using the developed ANFIS-PID. Three methods are examined in the developed ANFIS-PID controller: grid partitioning (genfis1), subtractive clustering (genfis2), and fuzzy C-means clustering (genfis3). The results show that the added cam can improve the gripping strength and that the ANFIS-PID model effectively handles the rise time and supported settling time. The developed ANFIS-PID controller demonstrates more efficient performance than Fuzzy-PID and traditional tuned-PID controllers. This proposed controller does not achieve any overshoot, and the rise time is improved by approximately 50–51%, and the steady-state error is improved by 75–95%, compared with Fuzzy-PID and tuned PID controllers. Moreover, the developed ANFIS-PID controller provides more stability for a wide range of set point displacements—0.05 cm, 0.5 cm, and 1.5 cm—during the testing period. The developed ANFIS-PID controller is not affected by disturbance, making it well suited for robotic gripper designs. Grip force control is also investigated using the proposed ANFIS-PID controller and compared with the Fuzzy-PID in three scenarios. The result from this force control proves objects’ higher actual gripping performance by using the proposed ANFIS-PID.
Machine Learning-Based Welding Defect Recognition Using GLCM Features and k-fold Cross-Validation: KNN and SVM Techniques SJ Kadhim, AB AlSalait, R Al-Sabur, AN Sharkawy Engineering Transactions , 2026 2026
Data-Driven Thermal Optimization of Drill Geometry in Titanium Machining: FEM Modeling and Experimental Insights A Atak, H Khazal, BK Khudhair, R Al-Sabur, HI Khalaf, M Alhafadhi Journal of Manufacturing and Materials Processing 10 (3), 109 , 2026 2026
Machine learning and exploratory data analysis for predicting tensile and thermal responses in friction stir spot welding SN Alasadi, R Al-Sabur International Journal of AI for Materials and Design 2 (4), 37-51 , 2025 2025
Intelligent neuro-fuzzy adaptive MIMO control for a self-balancing two wheeled autonomous robot via recursive resolution of the matrix diophantine equation B Bekhiti, R Al-Sabur, M Roudane, JA Younis, AN Sharkawy Discover Robotics 1 (1), 1-28 , 2025 2025 Citations: 4
Experimental investigation of through-wall hole geometry on pressure resistance in steel pipelines repaired with composite materials TR Flaifel, R Mosalmani, R Al-Sabur, M Shishesaz Discover Materials 5 (1), 212 , 2025 2025 Citations: 1
INTEGRITY AND FSSW PERFORMANCE OF Al AND Mg SHEETS FOR NEXT-GENERATION AEROSPACE AND AUTOMOTIVE APPLICATIONS KARAKTERISTIKE TAČKASTOG ZAVARIVANJA TRENJEM SA MEŠANJEM (FSSW) I … HI Khalaf, A Atak, R Al-Sabur, H Khazal, A Kubit Society for Structural Integrity and Life 25 (2), 237-243 , 2025 2025
Temperature-Controlled Process for Recycled Waste Tire Polymer-Polymer Composites: An Innovative and Sustainable Solution for Marine Fender Applications AH Zaibel, SAS Almtori, R Al-Sabur, AN Sharkawy Buletin Ilmiah Sarjana Teknik Elektro 7 (3), 468-480 , 2025 2025
A New Adaptive Flux-Oriented Control Framework for Induction Motors with Online Neural Network Training B Bekhiti, R Al-Sabur, AN Sharkawy Buletin Ilmiah Sarjana Teknik Elektro 7 (3), 296-311 , 2025 2025 Citations: 2
In-depth thermal analysis of different pin configurations in friction stir spot welding of similar and dissimilar alloys SN Alasdi, R Al-Sabur Journal of Manufacturing and Materials Processing 9 (6), 184 , 2025 2025 Citations: 2
Unveiling Surface Roughness Trends and Mechanical Properties in Friction Stir Welded Similar Alloys Joints Using Adaptive Thresholding and Grayscale Histogram Analysis H Khazal, A Belaziz, R Al-Sabur, HI Khalaf, Z Abdelwahab Journal of Manufacturing and Materials Processing 9 (5), 159 , 2025 2025 Citations: 5
Optimizing heat exchanger efficiency: Predictive modeling to minimize fouling in crude oil refining K Ikram, KA Djilali, N Bailek, JA Younis, D Abdennasser, R Al-Sabur, ... Advances in Mechanical Engineering 17 (5), 16878132251344071 , 2025 2025 Citations: 7
Experimental Investigation of Defect Geometry and Composite Type on the Pressure Resistance of Repaired Steel Pipelines TR Flaifel, R Mosalmani, R Al-Sabur, M Shishesaz 2025
Toward Eco-Friendly Solar Still: Enhancement of Solar Still Productivity Using Ground Tire Rubber MNF Fares, M Al-Saad, HHJ Almutter, MA Al-Mayyahi, MM Alfaize, ... Journal of Sustainable Development of Energy, Water and Environment Systems … , 2025 2025 Citations: 3
Trends and impact of the Viola-Jones algorithm: A bibliometric analysis of face detection research (2001-2024) SA Wijaya, TS Famuji, MA Mu'min, Y Safitri, N Tristanti, A Dahmani, ... Scientific Journal of Engineering Research 1 (1), 33-42 , 2025 2025 Citations: 7
Design and modeling of an intelligent robotic gripper using a cam mechanism with position and force control using an adaptive neuro-fuzzy computing technique IA Kheioon, R Al-Sabur, AN Sharkawy Automation 6 (1), 4 , 2025 2025 Citations: 14
Mastering Friction Stir Welding (FSW) With Machine Learning (ML): A Comprehensive Guide to Algorithms and Applications R Al-Sabur, A Mishra, HI Khalaf Using Computational Intelligence for Sustainable Manufacturing of Advanced … , 2025 2025 Citations: 1
Investigation of Critical Stress Intensity Factors for AISI 4340 and ASTM A533 Alloy Steels at Different Murakami Area Parameters AD Hassan, YM Ameen, M Mohammed, R Al-Sabur International Journal of Mechanical Engineering and Robotics Research 14 (2) , 2025 2025 Citations: 2
Modeling the Structural Dynamics of Carbon Fiber Composites for Robotic Systems Under Sinusoidal Load. R Al-Sabur, YM Ameen, HI Khalaf, A Mishra, AN Sharkawy International Journal of Robotics & Control Systems 5 (1) , 2025 2025 Citations: 2
Experimental investigation of mechanical behavior and load-bearing capacity of fiberglass and metal reinforced polypropylene sanitary manhole covers R Al-Sabur, HI Khalaf, A Kubit, R Perłowski, W Jurczak Advances in Science and Technology. Research Journal 19 (4) , 2025 2025 Citations: 1
Parametric analysis of climate factors for monthly weather prediction in Ghardaïa district using machine learning-based approach: ANN-MLPs A Dahmani, Y Ammi, K Ikram, S Kherrour, S Hanini, R Al-Sabur, M Laidi, ... International Journal of Robotics and Control Systems 5 (1), 179-196 , 2024 2024 Citations: 8
MOST CITED SCHOLAR PUBLICATIONS
Experimental investigation of friction stir welding on 6061-t6 aluminum alloy using taguchi-based gra A Asmare, R Al-Sabur, E Messele Metals 10 (11), 1480 , 2020 2020 Citations: 83
Effects of Underwater Friction Stir Welding Heat Generation on Residual Stress of AA6068-T6 Aluminum Alloy HI Khalaf, R Al-Sabur, ME Abdullah, A Kubit, HA Derazkola s Note: MDPI stays neu-tral with regard to jurisdictional claims in … , 2022 2022 Citations: 69
Tensile strength prediction of aluminium alloys welded by FSW using response surface methodology–Comparative review R Al-Sabur Materials Today: Proceedings 45, 4504-4510 , 2021 2021 Citations: 64
The effects of pin profile on HDPE thermomechanical phenomena during FSW HI Khalaf, R Al-Sabur, M Demiral, J Tomków, J Łabanowski, ME Abdullah, ... Polymers 14 (21), 4632 , 2022 2022 Citations: 44
Effect of number of tool shoulders on the quality of steel to magnesium alloy dissimilar friction stir welds HI Khalaf, R Al-Sabur, HA Derazkola Archives of Civil and Mechanical Engineering 23 (2), 125 , 2023 2023 Citations: 41
Machine learning algorithms for prediction of penetration depth and geometrical analysis of weld in friction stir spot welding process AS Bahedh, A Mishra, R Al-Sabur, AK Jassim Metallurgical Research & Technology 119 (3), 305 , 2022 2022 Citations: 34
Investigating residual stresses in metal-plastic composites stiffening ribs formed using the single point incremental forming method A Kubit, R Al-Sabur, A Gradzik, K Ochał, J Slota, M Korzeniowski Materials 15 (22), 8252 , 2022 2022 Citations: 30
Real-time monitoring applied to optimize friction stir spot welding joint for AA1230 Al-alloys R Al-Sabur, AK Jassim, E Messele Materials Today: Proceedings 42, 2018-2024 , 2021 2021 Citations: 30
Friction Stir Spot Welding Applied to Weld Dissimilar Metals of AA1100 Al-alloy and C11000 Copper RK Al-Sabur, AK Jassim IOP Conference Series: Materials Science and Engineering 455 (1), 1-10 , 2018 2018 Citations: 29
Enhancement of corrosion resistance and mechanical properties of API 5L X60 steel by heat treatments in different environments HM Lieth, R Al-Sabur, RJ Jassim, A Alsahlani Journal of Engineering Research 9 (4), 428-442 , 2021 2021 Citations: 24
Analysis and construction of a pneumatic-powered portable friction stir welding tool for polymer joining R Al-Sabur, M Serier, AN Siddiquee Advances in Materials and Processing Technologies 10 (2), 1052-1066 , 2024 2024 Citations: 22
Thermal modeling of tool-work interface during friction stir welding process A Chikh, M Serier, R Al-Sabur, AN Siddiquee, N Gangil Russian Journal of Non-Ferrous Metals 63 (6), 690-700 , 2022 2022 Citations: 21
Effects of noncontact shoulder tool velocities on friction stir joining of polyamide 6 (PA6) R Al-Sabur, HI Khalaf, A Świerczyńska, G Rogalski, HA Derazkola Materials 15 (12), 4214 , 2022 2022 Citations: 20
Comparative analysis of fouling resistance prediction in shell and tube heat exchangers using advanced machine learning techniques K Ikram, K Djilali, D Abdennasser, R Al-Sabur, B Ahmed, AN Sharkawy, ... Research on Engineering Structures and Materials 10 (1), 253-270 , 2024 2024 Citations: 19
Laminar flowmeter for mechanical ventilator: Manufacturing challenge of Covid-19 pandemic J Alsalaet, BS Munahi, R Al-Sabur, M Al-Saad, AK Ali, HA Fadhil, ... Flow Measurement and Instrumentation 82, 102058 , 2021 2021 Citations: 18
Contact resistance prediction of zirconium joints welded by small scale resistance spot welding using ANN and RSM models R Al-Sabur, M Slobodyan, S Chhalotre, M Verma Materials Today: Proceedings 47 (17), 5907-5911 , 2021 2021 Citations: 17
Parametric analysis for torque prediction in friction stir welding using machine learning and shapley additive explanations SE Belalia, M Serier, R Al-Sabur Journal of Computational Applied Mechanics 55 (1), 113-124 , 2024 2024 Citations: 15
Analysis of surface texture and roughness in composites stiffening ribs formed by SPIF process R Al-Sabur, A Kubit, HI Khalaf, W Jurczak, A Dzierwa, M Korzeniowski Materials 16 (7), 2901 , 2023 2023 Citations: 15
Design and modeling of an intelligent robotic gripper using a cam mechanism with position and force control using an adaptive neuro-fuzzy computing technique IA Kheioon, R Al-Sabur, AN Sharkawy Automation 6 (1), 4 , 2025 2025 Citations: 14
Effect of temperature on the performance of naphtha and kerosene as viscosity reduction agents for improving flow ability of Basrah-Iraq heavy crude oil AN Khalaf, AA Abdullah, RK Al-Sabur Journal of Advanced Research in Fluid Mechanics and Thermal Sciences 84 (1 … , 2021 2021 Citations: 14
