Critical Engineering Applications Research Articles

Machine learning-inspired study of dynamical parameters of single vapor bubble under nucleate flow boiling regime

Heat transfer performance of nucleate boiling is interlinked with the departure dynamics of vapor bubbles, which also serves as the basis for heat transfer partitioning schemes and bubble growth models. Given the fast transients and multiple bubble ebullition cycles, boiling experiments lead to voluminous data that are to be analysed for determining the bubble dynamic parameters, and subsequently, the boiling heat transfer rates. Conventional approach of manual handling of such temporal and spatially-resolved experimental data is inherently time-consuming and error-prone. In the backdrop of these limitations, importance of machine learning algorithms gets highlighted in making the entire process automated and accurate as they minimize the need of any human intervention. The present work explores the potential of machine learning techniques towards quantifying the bubble departure characteristics and heat transfer partitioning during nucleate flow boiling. Water-based experiments have been conducted in a vertical channel under varying subcooling levels (ΔTsub = 2, 5, and 8 K) and flow rates (Re = 2400, and 3600). Mask R-CNN machine learning (ML) model is employed to evaluate the temporal variations of dynamical parameters of vapor bubbles and departure characteristics. The bubble departure characteristics as well as the corresponding evaporative and convective heat transfer rates, as retrieved through ML-generated masks, showed a strong dependence on the level of bulk fluid subcooling and flow rates. Heat transfer partitioning analysis revealed a competing interplay between the evaporative and condensation components of overall boiling heat transfer rates. These findings demonstrate the potential of ML techniques towards optimizing the thermal performance of boiling phenomena that have significant implications in a range of critical engineering applications.

Load magnitude and location estimation on additively manufactured circular structures using deep learning

Additively Manufactured (AM) metal parts have been used at critical engineering applications. Estimation of load and its location is important to avoid accidents. Since the excitation signals move through multiple paths, estimation of load and its location is difficult. In this study, surface response to excitation method (SuRE) was implemented with multiple steps by considering the challenges of the geometry. Two 3D printed stainless steel rocket nozzle type structures with two different sizes and a C shaped tube section were used. Two different PZT placements were used for the big nozzle while the PZTs were located at the opposite sides of the small nozzle. The large nozzle had 280 test cases for 9 categories, while the smaller nozzle had 60 test cases for 5 categories. Finally, the C shaped specimen had 48 test cases for three categories. Continuous wavelet transform (CWT) was used to obtain more representative presentation of the data to the deep learning algorithm. Convolutional Neural Networks (CNNs) were used for classification. Wavelet transformation – Deep learning combination yielded 100 % accuracy for the large nozzle and 97.14 % for the smaller nozzle. These findings demonstrate the effectiveness of this technique on curved surfaces representative of real-world parts. The proposed method estimated the load location with 100 % accuracy.

Non-Contact Laser Doppler Vibrometer for Characterization of Guided Waves in Timber Structures with Varying Moisture Content and Grain Orientations

Timber structures have been widely used in critical engineering applications throughout history. The need for Non-Destructive Testing of timbers has arisen, and ultrasonic-guided waves (GWs) have proved their efficacy in studying structural integrity. The effect of the grain direction in orthotropic timber structures on the propagation characteristics of GWs is not well understood, especially in the presence of moisture. This study aims to comprehend the effects of timber-grain orientations and the moisture content (MC) on dominant GW modes, specifically, on the anti-symmetric A0 mode. The study is based on samples extracted from a Western White Pine utility pole, in three different orientations including longitudinal, tangential, and radial. The MC is introduced using a desiccator with a saturated salt solution. The studied MC conditions included dry, ambient MC, and high MCs varying from 10% to 27%. The GWs were excited using piezoelectric actuators and sensed with a Scanning Laser Doppler Vibrometer with Free-Free boundary condition. Phase velocities were obtained using the frequency-wavenumber plots of the measured line scans and were compared to the analytical curves for mode analysis. The results showed that the experimental A0-mode phase velocities match well with those generated analytically. The difference was negligible for longitudinally oriented specimens with a percentage difference ranging from 0.28% to 5.34%, as the MC varied. The phase velocity decreased by about 7% as the MC increased from 0% to 27%. A noticeable difference in the phase velocity was measured with different orientations; at dry conditions, the A0 propagated at 958 m/s in the longitudinal specimens, while it decreased in the tangential specimens to about 760 m/s, and had the lowest phase velocity in the radial direction reaching 433 m/s. The results show a high sensitivity of the propagating GWs to the direction of the fibers and the MC present in the timber.

Rheological measurements and transition to turbulence for moderate Reynolds number inertial suspensions

Particulate flows at moderate particle Reynolds numbers are important in critical engineering and geological applications. This experimental study explores neutrally buoyant suspensions in an outer-rotating coaxial rheometer for solid fractions, $\phi$ , from 0.1 to 0.5, and particle Reynolds number, $Re$ , from 0.5 to 800, covering laminar, transitional and turbulent regimes; $Re$ is defined in terms of the square of the particle diameter and the shear rate. For $0.1 < \phi < 0.4$ and $0.5 < Re <10$ , the direct torque measurements normalised by the laminar flow torque, $M/M_{lam}$ , are independent of $Re$ , but depend on $\phi$ . For the same range of $\phi$ and for $10< Re<100$ , the normalised torques depend on both $\phi$ and $Re$ , and show an increasing dependence on $Re$ . As $Re$ increases, the flow transitions to turbulence. Small particles delay the turbulent transition for $\phi \leqslant 0.3$ , while large particles augment the transition. A modified Reynolds number, $Re^\prime$ , that depends linearly on the particle diameter and the maximum velocity, $U_{o}$ , is introduced for both laminar and turbulent flows and shows a better correlation of the results as compared with $Re$ . For $\phi = 50\,\%$ , the normalised torque minus the torque at zero rotational speed is nearly independent of $Re^\prime$ . Rheological models based on $Re^\prime$ and the Krieger–Dougherty relative viscosity are proposed in the laminar regime for $10< Re^\prime <500$ ; in the turbulent regime, a correlation is proposed in terms of $Re^\prime$ and $\phi$ for $1000< Re^\prime < 6000$ .

Accelerating Conjugate Heat Transfer Simulations in Squared Heated Cavities through Graphics Processing Unit (GPU) Computing

This research develops an innovative framework for accelerating Conjugate Heat Transfer (CHT) simulations within squared heated cavities through the application of Graphics Processing Units (GPUs). Although leveraging GPUs for computational speed improvements is well recognized, this study distinguishes itself by formulating a tailored optimization strategy utilizing the CUDA-C programming language. This approach is specifically designed to tackle the inherent challenges of modeling squared cavity configurations in thermal simulations. Comparative performance evaluations reveal that our GPU-accelerated framework reduces computation times by up to 99.7% relative to traditional mono-core CPU processing. More importantly, it demonstrates an increase in accuracy in heat transfer predictions compared to existing CPU-based models. These results highlight not only the technical feasibility but also the substantial enhancements in simulation efficiency and accuracy, which are crucial for critical engineering applications such as aerospace component design, electronic device cooling, and energy system optimization. By advancing GPU computational techniques, this work contributes significantly to the field of thermal management, offering a potential for broader application and paving the way for more efficient, sustainable engineering solutions.

Microstructural and mechanical analysis of heat-treated Zn-Al alloy and Zn-Al/ZrB2 composites

This study explores the impact of heat treatment on Zn-Al alloy and Zn-Al/ZrB2 composites. Specifically, heat treatment at 250 °C for 3 h resulted in a significant refinement of grain structure and notable hardness improvement, showcasing an optimal combination of microstructural and mechanical properties. However, higher temperatures led to increased grain size and reduced hardness for both alloy and composites. These findings offer promising insights for optimizing processing parameters, enhancing the performance of Zn-Al based composites in critical engineering applications.

Design and Implementation of a DC to AC Power Electronics-Based Inverter that Produces Pure Sine Wave Output for Critical Engineering Applications

Power inverters play a crucial role in the field of engineering, particularly in applications where power stability is imperative. In devices such as Uninterruptible Power Supplies (UPS), the conversion of raw power to DC, subsequent filtering, and inversion to AC are executed through pure sine wave inverters. These inverters exhibit remarkable stability, making them ideal for powering sensitive equipment like data switches and Remote Terminal Units (RTUs). This study delves into the intricate process of converting DC power into a pristine sine wave signal. The heart of this power conversion lies in the utilization of the KA3525A integrated circuit (IC) in conjunction with MOSFETs of the PN55 series, supported by capacitors and resistors for effective power filtration. The KA3525A, a monolithic IC, encompasses all essential control circuits for a pulse width modulating regulator. Within this IC, a voltage reference, error amplifier, pulse width modulator, oscillator, under-voltage lockout, soft start circuit, and output driver collaborate seamlessly. The MOSFETs function as switches, synchronized with the oscillating signal from the KA3525A IC. This coordination, combined with the filter and other signal conditioning units, enables the conversion process. The design achieves the conversion of raw power into a stable pure sine wave signal of 170V AC at the H-bridge terminals, demonstrating the success of the designed approach.

Deep learning enhanced metamodel design based on reduced mode shapes for delamination identification in composite structures

Structures requiring robust maintenance and accessibility pose challenges for structural integrity monitoring. This monitoring, crucial in various engineering applications, enables corrective actions to ensure machinery and equipment safety. Laminated composite materials, prevalent in aeronautical structures, often exhibit imperceptible delaminations or cracks, demanding the use of artificial intelligence for damage characterization. This study employs a numerical-experimental methodology, integrating artificial neural networks, to identify delaminations. Delamination, a structural damage form, was intentionally induced in epoxy-fiberglass composite structures. Modal tests captured characteristics in damaged and undamaged structures, revealing artificial intelligence effectiveness in pinpointing damage location and size. Damage significantly altered beam mode shapes, generating complex modes. Induced delaminations led to a measurable 15% reduction in natural frequencies, and damping ratios increased by approximately 10% in damaged structures. Artificial neural networks achieved an impressive 92% accuracy in predicting delamination location and size. Quantitative analysis of mode shape alterations validated the proposed methodology’s efficacy. This study provides valuable quantitative insights into damage detection in laminated composite structures, highlighting AI's potential for enhanced structural health monitoring. The numerical–experimental approach, supported by quantitative results, emphasizes the importance of advanced techniques for precise and efficient damage characterization in critical engineering applications.

Bioinspired Design Rules from Highly Mineralized Natural Composites for Two-Dimensional Composite Design.

Discoveries of two-dimensional (2D) materials, exemplified by the recent entry of MXene, have ushered in a new era of multifunctional materials for applications from electronics to biomedical sensors due to their superior combination of mechanical, chemical, and electrical properties. MXene, for example, can be designed for specialized applications using a plethora of element combinations and surface termination layers, making them attractive for highly optimized multifunctional composites. Although multiple critical engineering applications demand that such composites balance specialized functions with mechanical demands, the current knowledge of the mechanical performance and optimized traits necessary for such composite design is severely limited. In response to this pressing need, this paper critically reviews structure-function connections for highly mineralized 2D natural composites, such as nacre and exoskeletal of windowpane oysters, to extract fundamental bioinspired design principles that provide pathways for multifunctional 2D-based engineered systems. This paper highlights key bioinspired design features, including controlling flake geometry, enhancing interface interlocks, and utilizing polymer interphases, to address the limitations of the current design. Challenges in processing, such as flake size control and incorporating interlocking mechanisms of tablet stitching and nanotube forest, are discussed along with alternative potential solutions, such as roughened interfaces and surface waviness. Finally, this paper discusses future perspectives and opportunities, including bridging the gap between theory and practice with multiscale modeling and machine learning design approaches. Overall, this review underscores the potential of bioinspired design for engineered 2D composites while acknowledging the complexities involved and providing valuable insights for researchers and engineers in this rapidly evolving field.

Stress Intensity Factors for Pressurized Pipes with an Internal Crack: The Prediction Model Based on an Artificial Neural Network

During pipeline operation, internal cracks may occur. The severity around the crack tip can be quantified by the stress intensity factor (KI), which is a linear–elastic fracture mechanics parameter. For pressurized pipes featuring infinitely long internal surface cracks, KI can be interpolated from a function considering pressure, geometry, and crack size, as presented in API 579-1/ASME FFS-1. To enhance KI prediction accuracy, an artificial neural network (ANN) model was developed for such pressurized pipes. Predictions from the ANN model and API 579-1/ASME FFS-1 were compared with precise finite element analysis (FEA). The ANN model with an eight-neuron sub-layer outperformed others, displaying the lowest mean squared error (MSE) and minimal validation discrepancies. Nonlinear validation data improved both MSE and testing performance compared to uniform validation. The ANN model accurately predicted normalized KI, with differences of 2.2% or lower when compared to FEA results. Conversely, API 579-1/ASME FFS-1′s bilinear interpolation predicted inaccurately, exhibiting disparities of up to 4.3% within the linear zone and 24% within the nonlinearity zone. Additionally, the ANN model effectively forecasted the critical crack size (aC), differing by 0.59% from FEA, while API 579-1/ASME FFS-1′s bilinear interpolation underestimated aC by 4.13%. In summary, the developed ANN model offers accurate forecasts of normalized KI and critical crack size for pressurized pipes, providing valuable insights for structural assessments in critical engineering applications.

Numerical study of heat transfer and load-bearing performances of corrugated sandwich structure with open-cell metal foam

Critical engineering applications require lightweight components that can effectively dissipate heat and provide sufficient load-bearing capacity. Examples of such applications include the jet blast deflector (JBD) on an aircraft carrier and engine combustion chambers in supersonic vehicles. While lightweight corrugated sandwich structures (CSSs) exhibit excellent load-bearing capacity, their heat transfer capacity falls short. To overcome this limitation, a strategy of filling the CSS with an open-cell metal foam (MF) is proposed to create a sandwich structure with ultralight load bearing and heat transfer capabilities. Numerical simulations were conducted to study the heat transfer and load-bearing performance of this new structure. The findings indicate that the use of a MF significantly improves the heat transfer capabilities of the CSS. Compared to the CSS, the Nusselt number of MF and CSS-foam composite at a Reynolds number of 240 were enhanced by 51.3% and 102.3%, respectively. The overall thermal performance of CSS-foam composites was optimized under the same pumping power constraints. In turbulent conditions, the overall thermal performance of CSS-foam composites was 5.9% to 55.4% higher than that of CSS in turbulent conditions. Furthermore, the simulations showed that when the CSS was subjected to static and quasi-static compression, the maximum von Mises stress occurred at the connection between the panel and the web.

Synergistic strength and toughness through impact-induced nanostructural evolutions in metals

Synergistically improving strength and toughness is imperative to designing next generation materials for critical engineering applications. Incorporating structural heterogeneities at the nanoscale can synergistically improve the strength and toughness of metals through eliciting complementary deformation mechanisms. Here, we present a synergistic enhancement in strength and toughness in microscale samples with a gradient-nano-grained structure produced through impact-induced recrystallization of initially single-crystal defect-free silver microcubes. The impacted samples exhibit ultra-high strain hardening rates and up to 4.7x the yield strength and 5x the toughness as the single-crystal silver micropillars, and up to 7.4x the yield strength and 4x the toughness as bulk coarse-grained silver. Synergistic improvements in mechanical properties are achieved through the activation of intragranular dislocation slip and intergranular grain boundary rotation and migration enabled through inversely correlated gradients in grain size and dislocation density.

A novel macroscopic computational methodology to predict the locations and orientation of solidification-cracks: Application to pulsed laser welding

The prevention of solidification cracks in critical engineering applications demands generalised predictive approaches. The first part of the manuscript reports the computation of transient temperature and flow fields using high-fidelity CFD simulations of the mixed -mode pulsed laser welding process, using iterative calculations of the volumetric heat source. The temperature field is verified by comparing the experimental and numerical weld profiles with different power-deposited-per-unit-length ϕ,which varied from 76J/mm to 177J/mm. The second part of this manuscript presents a novel and generalised macro-scale computational methodology which allows in situ numerical estimation of the Crack Vulnerable Index (CVI). Unlike the previous attempts, here, only the thermal characteristics, i.e., the temperature, melt flow velocity, and fluid fraction, are used to predict the locations and orientation of the cracks dynamically. The predictions agree well with the experimentally obtained findings of pulsed laser welding. The study shows that the heat-flow direction correlates closely with the crack-orientations obtained from experiments. It is experimentally observed that the cracking tendency is substantially reduced as the power-deposited-per-unit-length is increased. The bulk weld-pool solidification velocity is inversely proportional to the number-of-cracks (a measure of cracking tendency) observed in the post-solidified weldments. Further, for the case with the lower cracking tendency, the peak values of the temperature gradient and the normalized bulk weld-pool solidification velocity are ∼33% and 11% higher than the respective values for the case with the higher cracking tendency.

Characteristic analysis of magnetorheological fluid porous fabric composite with a novel constitute model

As a novel material, magnetorheological fluid porous fabric composite (MRF-PF) has not been further analyzed and modeled. In this paper, mechanical properties of MRF-PF are analyzed quantitatively and qualitatively with a novel constitute model. Considering the wall slip, viscoelasticity and internal porous network of MRF-PF, a constitute model of MRF-PF is derived firstly which can describe the performance precisely. According to experimental results, mechanical properties of MRF-PF are different from conventional MRF. Shear stress τ decreases first, then increases slowly at off-state. Under low magnetic field, shear stress τ presents the opposite variation. It increases at first, and then decreases rapidly. With the further increase of magnetic field, Shear stress τ increases dramatically at high shear rate. This effect is determined by the internal porous network and absorption degree of MRF-PF. A possible flow mechanism of MRF-PF has been proposed. Comparing with different types of MRF-PFs, pre-immersed MRF-PF without relative rotation has a better performance. The maximum shear stress can reach as high as 62.35 kPa with an increase of 22.37% under the magnetic field of 0.7 T. The corresponding dynamic yield stress τ y increases to 55.18 kPa with an increase of 26.6%. What is more, internal porous network can support the particle chains to reinforce the shear-resistance property. The shear thinning effect of MRF-PF can be improved significantly. However, MR effect of MRF-PF decreases largely. These attractive mechanical properties make MRF-PF a potential choice for critical engineering applications.

Preparation of double-layer film modified polyacrylonitrile fiber modifier for designing and performance evaluation of PAN/SBS composite asphalt

• Innovative application of APTES, co-deposition of PEI and PDA in the modification of polyacrylonitrile fibers. • Double-layer membrane structure modified polyacrylonitrile fibers. • P-P/A-P/PAN and A-P/PAN with higher surface free energy, roughness and adhesion. • P-P/A-P/PAN/SBS@MA and A-P/PAN/SBS@MA had better viscoelastic properties. • P-P/A-P/PAN/SBS@MA and A-P/PAN/SBS@MA had better low temperature crack resistance and high temperature adhesion properties. To further enhance the properties of polyacrylonitrile (PAN) fiber incorporated modified asphalt, a new method of PAN fiber modification with a double membrane was developed. The influence of interface characteristics of modified fiber on the properties of fiber-reinforced asphalt was revealed from the free energy of the fiber surface and the adhesion between the modified fiber and matrix asphalt. Two types of PAN modified fibers with single-layer membrane structure (A-P/PAN) and double-layer membrane structure (P-P/A-P/PAN) were prepared, which were applied to synthesize three types of modified asphalts (Mas), i.e., PAN/SBS@MA, A-P/PAN/SBS@MA, and P-P/A-P/PAN/SBS@MA. Dynamic shear rheometer (DSR) experiment showed that at 46℃, the G*, G', and G' of P-P/A-P/PAN/SBS@MA were 24%, 19%, and 37% higher than those of PAN/SBS@MA. Similarly, at 175 ℃, the maximum yield strength of P-P/A-P/PAN/SBS@MA was 16.38% and 32.7% higher than those of A-P/PAN/SBS@MA and PAN/SBS@MA, respectively, which was attributed to the better surface free energy and adhesion work of the modified fibers. The surface contact angle test results showed that free energy of the surface and adhesion work of P-P/A-P/PAN fibers are 62% and 26.5% higher than those of PAN fibers, respectively. Scanning electron microscopy, atomic force microscopy, and UV-Vis tests indicated that the fiber's surface has successfully constructed a double-layer membrane structure. The fluorescent microscopy and cryo-SEM tests of the modified asphalt further confirmed that the modified PAN fiber had better interfacial interaction with the base asphalt. This study shows that the double-layer film modified PAN fiber composite SBS asphalt has good viscoelastic properties, high temperature adhesion, and low temperature fracture resistance, and hence carries critical practical engineering applications for highways, low temperature area pavements, airport pavements, and construction pavement.

A novel similarity-based status characterization methodology for gear surface wear propagation monitoring

The gearbox is a vital component for rotating machinery and has been used in many critical engineering applications. Surface wear is a common but inevitable phenomenon during the lifespan of the gearbox. Its propagation can result in some catastrophic failures and cause unexpected economic loss. Therefore, it is vital to evaluate the degradation process of the gear system caused by surface wear propagation in order to make reliable predictive maintenance-based decisions to ensure the safe operation of the gearbox transmission system. The vibration analysis technique is a prevailing tool for rotating machine condition monitoring. However, research on vibration-based gear wear monitoring is relatively rare as the dynamic interactions between gear surface wear and gear system dynamic characteristics would produce complex gear dynamic responses and vibration features. Therefore, this paper presents a novel similarity-based status characterization methodology for gear wear monitoring. In this proposed methodology, a novel gear wear monitoring indicator is developed to evaluate gear tooth contact characteristics at different wear severities, which could significantly benefit gear systems' health management. The effectiveness of the proposed method for gear wear propagation process monitoring is presented and proven through a series of run-to-failure tests with different lubrications and operational conditions.

Bayesian Spatio-Temporal grAph tRansformer network (B-STAR) for multi-aircraft trajectory prediction

Multi-Agent Trajectory Prediction is a critical and challenging component across different safety–critical engineering applications, e.g., autonomous driving and flight systems. Trajectory prediction tools are required for the next-generation air transportation system (NextGen). In practice, the prediction of aircraft trajectories needs to consider the impact of various sources, such as environmental conditions, pilot/controller behaviors, and potential conflicts with nearby aircraft. Huge uncertainties associated with these factors lead to the untrustworthiness of a deterministic trajectory prediction model. Moreover, the safety assurance in the near-terminal area is of specific interest due to the increased airspace complexity, where the instrument/visual flight rules are applied. In this work, we propose the Bayesian Spatio-Temporal grAph tRansformer (B-STAR) architecture to model the spatial and temporal relationship of multiple agents under uncertainties. It is shown that the proposed B-STAR achieves state-of-the-art performance on the ETH/UCY pedestrian dataset with UQ competence. Then, multi-aircraft near-terminal interactive trajectory prediction model is trained and validated with real-world flight recording data. The sensitivity study on the prediction/observation horizon and the graph neighboring distance threshold are performed. The code is available at https://github.com/ymlasu/para-atm-collection/tree/master/air-traffic-prediction/MultiAircraftTP.

Performance evaluation of machine learning for fault selection in power transmission lines

Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.

Effect of single-period overload parameters on fatigue crack retardation for high-density polyethylene

Nowadays, polymers have become indispensable and ubiquitous materials in many critical engineering applications. The majority of industrial polymeric components are subjected to unexpected overloads in service which may affect their lifespan. During the discontinuous slow crack growth (SCG), irreversible plastic zone is formed on the head of the crack tip. The applied overloads have a significant effect on the size of the formed plastic zone as well as the fatigue crack propagation (FCP) rate which consequently affects the failure lifetime of the component. The objective of this work is to investigate the effect of single-period overload with constant amplitude and sinusoidal waveform on the resultant FCP rate, crack band length, and the corresponding plastic zone size for High-Density Polyethylene (HDPE100). The effects of the overload ratio (OL ratio) for the first propagation crack-arrest band on the crack propagation process have been also investigated to achieve maximum retardation for crack growth and to obtain the maximum lifespan of the tested specimens. High retardation is observed at high OL ratios under constant OL position and OL number of cycles (duration). The morphology of fracture surface shows higher crack band length at high OL ratio. The crack band length represents about 80–90 % of the measured craze length.

Strenx 1100 Çeliğinin MMY Yardımıyla Sert Frezelenmesinde Enerji Tüketimi Üzerine Çalışma

Strenx 1100 is a structural steel utilized in the critical engineering applications such as marine, crane and transportation thanks to its high tensile and yield strength properties. Despite its advantages coming from mechanical properties, hard-to-cut structure of this material makes difficult the metal removing. Therefore, high amount of energy consumption reveals due to the requirement of high cutting forces. In order to overcome this problem, recently, minimum quantity lubrication (MQL) supported machining have been introduced from many authors. In this context, this study aims to measure and analyze the consumed energy in MQL assisted surface milling of Strenx 1100 steel. For this purpose, experimental results were evaluated by three methods: analysis on graphs, ANOVA based statistical evaluation and signal to noise (S/N) ratio based optimization. In the experiments, with the help of Taguchi, L9 orthogonal array design was adopted using three levels of cutting speed, feed rate and depth of cut. According to the obtained findings, cutting speed is the most effective parameter according to contribution rate (46.28%) and P value (0.048<0.05), meanwhile feed rate (23.6%) and depth of cut (27.8%) have important contributors on the consumed energy during milling. Seemingly, general trend for the consumed energy is about to increase with higher values of cutting speed and depth of cut and lower values of feed rate according to 3D plots. This situation is confirmed with optimal solutions as vC=75 m/min, f=0.25 mm, aP=0.225 mm/rev achieved by S/N ratios. Conducted experiments and further analysis provide an important guidance for the industrial applications in MQL reinforced machining of hard materials.