Sajid Ullah Butt

Infrared thermography is a well-established non-contact technique for surface temperature monitoring across diverse manufacturing and materials processing applications, such as thermal process monitoring, quality inspection, and high-temperature material evaluation; however, its measurement accuracy is affected by emissivity variations in multi-material environments, which distort the relationship between apparent radiometric temperature and true surface temperature. This study presents a sequential thermal correction framework to improve surface temperature estimation in heterogeneous material systems under normal viewing conditions (90° incidence angle). The methodology operates in two stages: first, apparent emissivity is predicted from infrared temperature and material-specific features using regression-based models; second, the estimated emissivity is incorporated into the temperature-correction model to estimate the reference surface temperature, validated against K-type thermocouple measurements. Controlled experiments were conducted on A36 structural steel and alumina ceramic specimens, representing multi-material combinations found in manufacturing environments, over approximately 20 °C to 700 °C. Synchronized thermal imaging and thermocouple data were acquired throughout heating. Polynomial regression and machine learning approaches were evaluated using leave-one-session-out validation. The framework achieved an out-of-session mean absolute error of 9.45 °C, supporting reliable thermal monitoring in manufacturing applications.

The demand for large-scale high-performance components with tailored properties in the aerospace and automotive industries has increased interest in multi-material additive manufacturing (AM). Among AM techniques, the Wire Arc Additive Manufacturing (WAAM) process is preferred for bimetallic fabrication due to high deposition rates, low equipment costs, and efficient material utilization. However, differences in metallurgical and thermal properties between dissimilar alloys can cause heat accumulation, leading to thermal stresses, cracking, and weak interfacial bonds. To the best of the authors’ knowledge, no study has reported the fabrication and characterization of a 17-4PH SS/Inconel 625 joint using the large-scale CMT-WAAM Process. To fill this gap, this study characterizes the microstructure and elemental distribution of the joint using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray Microscopy (XRM) and energy dispersive spectroscopy (EDS). Microstructural analysis revealed a martensitic matrix with retained δ-ferrite in the 17-4PH region, a fully austenitic γ-phase in the Inconel 625 region, and a mixed BCC–FCC transition zone at the interface. EDS results demonstrated a Fe–Ni compositional gradient across the interface. Radiographic inspection confirmed a defect-free build, and XRM results showed a porosity of less than 0.003% only in the 17-4PH region. Tensile testing confirmed joint integrity, with fracture occurring in the Inconel 625 region, and average yield and ultimate tensile strengths of 391 ± 7 MPa and 676 ± 9 MPa, respectively. The simplified Johnson-Cook constitutive model successfully predicted the ultimate tensile strength (UTS), with a prediction error of 9.3% compared to the experimental result. Furthermore, a novel 3D-structured light scanner technique was developed and validated with an extensometer to provide insight into localized strain behavior.

In the era of Industry 4.0, digital twin (DT) has gained substantial attention across industries for its capability to create dynamic digital representations of physical systems. A DT facilitates continuous data exchange between a physical asset and its virtual counterpart, enabling real-time monitoring and predictive analysis. A critical precursor to developing a full DT is digital shadow (DS), which digitally mirrors the behavior of a physical system using simulation tools. The effectiveness of a DS largely depends on the accuracy and fidelity of the input data. This article proposes a step-by-step methodology for developing a DS using discrete event simulation (DES) in FlexSim. The approach begins with a product architecture analysis to identify key process variables, enabling effective product family classification. This is followed by the selection of suitable DES software, with a focus on accommodating high product variability typical in complex manufacturing systems. A dual-mode data acquisition strategy, combining manual time studies with sensor-generated data, is then applied to capture both observable and high-resolution operational data. A comparative case study is conducted using two DS models, one based on manually collected data and the other on high-fidelity sensor data, within a high-mix, high-volume manufacturing setting. Results demonstrate that DS models built with high-fidelity data significantly enhance simulation precision. The second model successfully identified a production line imbalance and provided actionable insights through a micro study of manual activities that were executed in the assembly process. Furthermore, the proposed future state, modeled through the DS, showed a potential 20 % increase in production output, contributing to operational efficiency. This study aims to underscore the value of DS as a reliable and scalable tool for analyzing complex systems, supporting informed decision-making and serving as a critical step toward the implementation of a complete DT.

With advances in Industry 4.0 and the growing availability of low-cost sensors, high-frequency data collection has become more accessible for manufacturing industries. These technologies enable the creation of digital shadows (DSs) to visualize and monitor the production processes. In this context, the DS captures the physical manufacturing process in a virtual environment, enhancing process monitoring and providing a dynamic, interactive, and unidirectional flow of data from the real world to the virtual model for performance analysis and maintenance planning. This article presents a modular approach by establishing foundational methods for transitioning towards DS by creating a digital model of a semiautomatic process of polyvinyl chloride (PVC) welding in a manufacturing environment. Utilizing Industrial Internet of Things–enabled sensing systems, the proposed framework follows a modular approach to develop a sensor architecture and demonstrates real-time data acquisition within the production line. The captured operational data, reflecting the operational status of the equipment, can be called the early-stage process DS. These data are then processed into the information-knowledge-modeling structure through live dashboards and analytics and further integrated into simulations, laying the groundwork for a future digital twin (DT). The implementation combines cloud-based visualization platforms with open-source Python web frameworks for data analytics, ensuring cost-effectiveness and ease of deployment. Real-time monitoring will support performance evaluation and machine metrics tracking, addressing the complexities of high-mix, high-volume production. This data-driven foundation establishes a clear pathway for the evolution from DS to DT in PVC welding manufacturing.

Stereolithography additive manufacturing (SLA-AM) can be used to produce ceramic structures by selectively curing a photosensitive resin that has ceramic powder in it. The photosensitive resin acts as a ceramic powder binder, which is burned, and the remaining ceramic part is sintered during post-processing using a temperature–time-controlled furnace. Due to this process, the ceramic part shrinks and becomes porous. Moreover, additive manufacturing leads to the orthotropic behavior of the manufactured parts. This article studies the effect of the manufacturing orientation of ceramic parts produced via SLA-AM on dimensional accuracy. Scaled CAD models were created by including the calculated shrinkage factor. The dimensions of the final sintered specimens were very close to the desired dimensions. As sintering induces porosity and reduces the mechanical strength, in this study, the effect of orientation on strength was investigated, and it was concluded that the on-edge specimen possessed by far the highest strength in terms of both compression and tension.

Due to the miniaturization of electronic devices, electromigration became one of the serious reliability issues in lead-free solder joints. The orientation of the β-Sn grain plays an important role in electromigration failures. Several studies have been carried out to investigate the effect of Sn grain orientation on electromigration. The efforts involve the influence of β-Sn grain orientation on the migration of Cu, Sn, and Ni atoms, on the morphology of the solder joint, and on the formation of Cu6Sn5 and (Cu, Ni)6Sn6 in the lead-free solder joint during electromigration. The current review provides a detailed review of past studies which were conducted to investigate the influence of β-Sn grain orientation on electromigration failures in lead-free solder joints.

With the growth of the manufacturing industry, the demand for alloy materials with high hardness, toughness, and impact strength has increased. Since products from such alloy materials are extremely difficult to manufacture with high accuracy and reduced surface roughness using traditional machining techniques, wire electric discharge machining can be used to machine such complex parts with more precision. In this case-study-based research, machining factors such as current, pulse-on time, and voltage are studied to determine their effects on the material removal rate for AISI 1045 medium carbon steel. The Taguchi L₉ orthogonal array is used in the design of experiments for optimization. Statistical techniques such as analysis of variance and signal-to-noise ratio are used to identify the control parameters that matter most in bringing about optimal results. Based on the results, the current is the most crucial control variable in this investigation. Moreover, the maximum material removal rate obtained was 0.7112 mm³/min with the obtained optimized values of current (I) = 16 A, voltage (V) = 50 V, and pulse-on time (Ton) = 100 µs.

This study consists of the development of a hair clipper comb finite element (FE) model, impact test analysis on the FE model, fabrication of the product using commercially available materials, followed by physical impact testing of the comb. Moreover, microscopic examination of the combs was performed to analyze the quality of the product and correlate the defects with the failure mechanism. The 3D model of comb for a Philips hair clipper was developed using ONSHAPE software, followed by a design study to understand the impact resistance of the product. The design study was performed using finite element analysis (FEA) explicit dynamic module, where two hair clipper comb designs, one with a solid body and the other with a shell were subjected to drop test simulation in two orientations: leg and head drop. Two readily available 3D printable plastic materials, Acrylonitrile Butadiene Styrene (ABS) and Polylactic acid (PLA) were selected for the FEA simulation while the comb was subjected to free fall from a height of 5 ft (1.67 m). The comb was dropped in two orientations: the head drop configuration and the leg drop configuration. For all combinations, the maximum stresses generated as a result of impact were noted and experiments performed to validate the simulation results. The four models were fabricated using fused deposition modeling (FDM) technique and were manually dropped from the same height. In line with the simulated results, models prepared from PLA material failed upon the impact while ABS samples having a comparatively better impact resistance sustained the impact without failure. Finally, fracture surface morphologies of the failed PLA component and the surface of ABS in as-printed condition were analyzed using Scanning Electron Microscopy (SEM). Based on the obtained results, the shell model made of ABS material turns out to be the most suitable choice out of all the designs considered.

Oxygen-rich SiAlON ceramics doped with various nanosized metal oxide (MO) stabilizers were synthesized with a view to examine their effect on thermal and mechanical characteristics. The nanosized starting powder precursors comprising Si₃N₄, AlN, Al₂O₃, and SiO₂ along with oxides of Ba, Y, Mg, La, Nd, Eu, Dy, Er, and Yb as the MO charge stabilizer were employed in developing different SiAlON samples. Ultrasonic probe sonication was utilized to develop a homogenous mixture of initial powder precursors followed by spark plasma sintering (SPS) of the samples at the low temperature of 1500 °C coupled with 30 min of isothermal treatment. Sample compositions (according to general formula of alpha SiAlON: M_m/v^v+Si_12−(m+n)Al_m+nO_nN_16−n) selected in the present study are represented by m value of 1.1 and n value of 1.6. The synthesized samples were evaluated for their physical behavior, microstructural and crystal structure evolution, and thermal and mechanical characteristics. More specifically, the sintered ceramics were examined by X-ray diffraction and electron microscopy to comprehend and relate the structural characteristics with the densification, thermal conductivity, hardness, and fracture toughness. The high reactivity of the nanopowders and the localized heating provided by SPS resulted in densified ceramics with relative densities in the range of 92–96%. Vickers hardness values were found to be in the range of 12.4–17.0 GPa and were seen to be profoundly influenced by the grain size of the alpha SiAlON (primary) phase. The fracture toughness of the samples was measured to be in the range of 4.1–6.2 MPa·m^1/2. SiAlON samples synthesized using Er and Yb charge stabilizers were found to have the highest fracture toughness of 5.7 and 6.2 MPa·m^1/2, primarily due to the relatively higher content of the elongated beta phase. While there was no obvious relationship between the thermal conductivity and the alpha SiAlON metal charge stabilizers, the values were seen to be influenced by the grain size of alpha phase where Dy-SiAlON had the lowest thermal conductivity of 5.79 W/m⋅K and Er-SiAlON showed the highest value of thermal conductivity (6.91 W/m⋅K). It was concluded that scientifically selected metal oxide charge stabilizers are beneficial in developing SiAlON ceramics with properties tailored according to specific applications.

Welding induced distortion causes dimensional inaccuracies in parts being produced and assembly fit-up problems during manufacturing. In this study, a framework is proposed to mitigate weld distortion at the design stage. A sequential approach is adopted to optimize the welding process. In the first phase, welding process parameters are optimized through the response surface method. The effect of these parameters on the overall distortion of the welded part is observed by a simulation of the welding process. In the second phase, the weld sequence is optimized using the optimum weld parameters. A reinforcement learning-based Q-learning technique is used to select the optimum welding path by sequential observation of weld distortion at each segment being welded. The optimum process parameters and weld path sequence have been selected for 3 mm steel plates having a lap joint configuration and a 2 mm vent panel with a butt joint configuration. It is concluded that the combination of the optimum welding parameters and welding sequence yields minimum distortion. By applying this framework, a reduction of 19% is observed in overall welding induced distortion.