Output Response Research Articles

Fabrication of bone tissue engineering scaffolds with a hierarchical structure using combination of 3D printing/gas foaming techniques

AbstractThe goal of this research was to study and optimize the structure and geometric features of scaffolds made using a combined method of 3D printing and gas foaming. This endeavor aimed to produce scaffolds with a hierarchical structure that closely resemble bone tissue. The study examined the effects of saturation pressure, foam temperature, and foam time on the scaffolds using response surface methodology (RSM). RSM is statistical technique used for optimizing and analyzing processes by modeling relationship between input variables and output responses. The results of multi‐objective optimization showed that highest pressure (55 MPa), the shortest time (40 s), and the temperature of 68°C were the optimal conditions. RSM was also used to develop mathematical models of structural properties, dimensional accuracy, and mechanical strength, focusing on different foam parameters, which could be used to predict desired properties. Subsequently, the designed scaffold underwent MTT assay testing to assess cell toxicity indicating its biocompatibility. The results demonstrate that by using the correct foam parameters in combination with 3D printing, it is possible to achieve polymer scaffolds with proportional dimensions, geometry, and mechanical strength suitable for cell growth to use inside the human body.

Power Quality Control of Micro Hydro Power Plant Based-ELC and VSI Using Fuzzy-PI Controller

Stability of frequency and voltage of micro hydro power plant (MHPP) are influenced by sufficiency of active power and reactive power respectively in handling load power. These powers are normally controlled by Electronic Load Controller (ELC) and Voltage Source Inverter (VSI), respectively. VSI is specifically added to compensate reactive power caused by inductive load. The objective of the study is to control active power and reactive power in MHPP system against the changes in load power through the improvement of ELC and VSI output responses by applying Fuzzy-PI controller. ELC applied Fuzzy-PI controller to obtain more precise TRIAC ignition angle to meet a suitable active power to balance the load power. While VSI implemented Fuzzy-PI controller to meet a precise reactive power needed to oppose inductive load power. Capability of Fuzzy-PI controller in improving ELC and VSI performance were assessed by using Matlab in complete MHPP model. Assessments on the proposed controller indicate that it was effective in improving the performance of both ELC and VSI. By applying Fuzzy-PI controller on ELC, a precise ignition angle of TRIAC stimulated a more precise ballast load to balance active power against the load power. While the addition of Fuzzy-PI controller on VSI produced more suitable reactive power to compensate for the inductive load presence. With a more stable frequency and voltage against changes in load power, the power quality of the MHPP also improve.

An experimental investigation of F53 super duplex stainless steel using wire cut EDM process

This research focuses on the machinability of F53 super duplex stainless steel using the Wire Electrical Discharge Machining (WEDM) process. F53 Super Duplex Stainless Steel is known for its exceptional corrosion resistance and mechanical properties. It is a popular choice for critical applications in various industries, such as offshore, chemical, and petrochemical. The wire EDM process is chosen for its ability to achieve precise and intricate cuts in hard-to-machine materials like super duplex stainless steel. This study considered important process variables like wire feed rate, servo voltage, pulse-on and pulse-off time, and material removal rate and surface roughness as output responses. Box Behnken method is used to analyse all possible interactions between input and output variables are taken into account, and three different levels of parameter conditions are adopted. It was found that MRR increased by 51.30 % with a gradual increase in pulse on time and applied wire feed and SR decreased by 62.86 % gradually with increasing servo voltage and pulse off time. The optimized values are found to be Ton = 11 µs, WF = 13 m/min, SV = 40 V and Toff = 8 µs. The microhardness study confirms the formation of the melted layer on the machined surface. This research contributes to a deeper understanding of the wire EDM process's applicability to the challenging material properties of F53 super duplex stainless steel, offering insights for the manufacturing and engineering industries aiming to improve the efficiency and precision of machining operations for this advanced material.

Optimization and characterization of hybrid bio-briquettes produced from the mixture of sawdust, sugarcane bagasse, and paddy straw.

Biomass briquetting is a viable densification technique that converts waste biomass materials into useful products and alternative energy. This work explores the characteristics and optimization of hybrid bio-briquette production by combining crop residues (paddy straw) and solid biomass materials (sawdust and sugarcane bagasse). A total number of 20 briquettes were fabricated with three input factors: sawdust (SD), sugarcane bagasse (SB), and paddy straw (PS) based on the faced-centered central composite design (FCCCD) approach in the laboratory to investigate the calorific value (CV) and ash content (AC). The bomb calorimeter technique was used to evaluate the briquette's calorific value and ash content. The proposed work focused on optimizing the briquette input parameters (SD, SB, and PS) and output responses (CV and AC) using analysis of variance (ANOVA) and response surface methodology (RSM) and hybrid artificial neural network-integrated with multi-objective genetic algorithms (ANN-MOGA). This study shows that the MOGA-ANN-based model results in the best value of CV (17.07 MJ/kg) and AC (1.95%) with optimal input parameters SD (39.99 g), SB (29.02 g), and PS (69.02 g). The optimal results observed from the MOGA-ANN model have also been validated experimentally. The Fourier transform infrared (FTIR) spectroscopy investigation reveals that biomass briquettes are the sustainable and environment-friendly option of fossil fuels for power generation and indoor cooking. The study suggests a strategy for minimizing agro-waste, which may be converted into future fuel in the form of briquettes.

Conjoint Enhancement of VSG Dynamic Output Responses by Disturbance-Oriented Adaptive Parameters

Virtual synchronous generator(VSG) is an encouraging concept to ensure provision of inertia, following large integration of renewable sources in system. However, apart from VSG contribution in improving system's frequency stability, it is also important to ensure long term robust and stable operation of VSG itself. In this regard, active power loop(APL) of VSG constituting variable control parameters is analyzed subsequent to contingency scenarios. It is observed that dynamics of APL outputs (virtual frequency and active power) is critically sensitive and prone to intrinsic oscillations. Further, during disturbance in system, utilization of constant control parameters of APL illustrates contradictory effects in dynamics of its outputs. As a solution to above issues, novel adaptive inertia and adaptive damping coefficient is proposed. The work provides distinct approach in following manner- 1) judicious selection of different operation range of natural undamped frequency( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ω_{n}$</tex-math></inline-formula> ) and damping ratio(ζ) of APL as compared to existing strategies, along with 2) reduced computational complexity and improved accuracy of controller, 3) thereby reduction in over(-under)shoots and intrinsic oscillations in virtual frequency and output active power of APL during transient period. This strengthens classical VSG design and contributes in its robust long term industry/real-time application. The efficacy of proposed strategy is showcased by real-time simulations along with validation at PHIL level.

Design of Fuzzy Fractional Order-based PID Controller for Higher Order Systems

The conventional Integer Order-based Proportional Integral Derivative (IO-PID) control is mostly used in process control and manufacturing industries. However, it has been analyzed after an in-depth review of the literature that traditional IO-PID control is not worthwhile for non-linear and higher-order applications. The Controlling of higher-order systems is a critical issue in many industrial plants. The foremost objective of Controllers in complex systems is the reduction of settling time, overshoot, etc for higher-order systems. A novel Fuzzy fractional order-based PID (FFOPID) controller is designed to resolve all the significant issues of control systems. The traditional controller and the novel controller are given the same unit step input and the simulation had been done using MATLAB. The comparison between the output response of the two controllers revealed that the designed novel controller works better than the traditional IO-PID controllers.

Multi-response optimization of input and output responses of multipass FSP of AA7050 with (SiC + TiB2) nanoparticles by response surface methodology

In this work, multi-response optimization of output and input responses of multipass friction stir processing (MPFSP) of AA7050 with (SiC + TiB2) nanoparticles by response surface methodology based on the center composite design and metallurgical characterization were analyzed, and the optimum parameters of the MPFSP were discussed. At high tool rotational speed (TRS) of 1100 rpm with 50% TiB2 and 100% SiC nanoparticles, maximum joint efficiency (137.80%) was observed due to uniformly dispersed SiC and TiB2 within the matrix, serving as practical obstacles to dislocation motion, hindering plastic deformation, and facilitating enhanced mechanical properties. MPFS and nanoparticles broke the coarse grain structure of the base metal and produced a fine and homogenous grain structure in the stir zone. Increasing the concentration of reinforcement particles and FSP passes (1 to 4) inhibited grain boundary migration and significantly reduced the high-angle grain boundary and grain size. The optimized value of input parameters TRS, TiB2 nanoparticles, and SiC nanoparticles was observed as 1068 rpm, 56%, and 97%, while the optimized value of output response tensile strength, % strain, and hardness value was found as 569.16 MPa, 20.79, and 148.32 HV respectively. The p-value for all three models remained below 0.05, indicating a confidence level exceeding 95% in the constructed models, rendering them suitable for design exploration. The hardness value range of MPFSP/(SiC + TiB2) lies between 130 HV and 190 HV. The minimum hardness value of 131.03 HV was observed at 0% TiB2 and 50% SiC reinforcement particles with TRS of 1100 rpm, while the highest microhardness (187.02 HV) was perceived at 1000 rpm.

Experimental study on electrical discharge machining of porous and pure 316L stainless steel

Porous metal materials are widely used in heat-absorbing, biomedical parts and other fields because of their excellent mechanical properties and biocompatibility. Further applications of porous metal materials are inseparable from secondary precision machining. However, traditional cutting methods tend to compromise the original porosity of the material surface. Electrical discharge machining (EDM) is a type of non-conventional machining method which is more preferable to overcome these defects. In the present study, the main target is to investigate the effect of four input electrical machining parameters (current, frequency, efficiency and gap voltage) on the five output responses (material removal rate (MRR), electrode wear, surface roughness (SR), noise and time consumption value of environmental particulate matter (PM2.5)) when electrical discharge machining porous 316L stainless steel and compared with that of pure 316L stainless steel. The Taguchi method was employed to determine the relationship between electrical discharge machining parameters and output responses. The analysis of variance (ANOVA) was used to evaluate the effect of input parameters on output responses. The effects of electrical machining parameters on above responses were analyzed by main effects, and the order of influence was obtained, respectively. Finally, the surface morphology was observed by scanning electron microscope (SEM), and the effect of electrical machining parameters on the characteristics and formation of machined surface of porous stainless steel was mainly analyzed.

Impact of systemic hypoxia and blood flow restriction on mechanical, cardiorespiratory, and neuromuscular responses to a multiple-set repeated sprint exercise.

Introduction: Repeated sprint cycling exercises (RSE) performed under systemic normobaric hypoxia (HYP) or with blood flow restriction (BFR) are of growing interest. To the best of our knowledge, there is no stringent consensus on the cardiorespiratory and neuromuscular responses between systemic HYP and BFR during RSE. Thus, this study assessed cardiorespiratory and neuromuscular responses to multiple sets of RSE under HYP or with BFR. Methods: According to a crossover design, fifteen men completed RSE (three sets of five 10-s sprints with 20s of recovery) in normoxia (NOR), HYP, and with bilaterally-cuffed BFR at 45% of resting arterial occlusive pressure during sets in NOR. Power output, cardiorespiratory and neuromuscular responses were assessed. Results: Average peak and mean powers were lower in BFR (dz = 0.87 and dz = 1.23, respectively) and HYP (dz = 0.65 and dz = 1.21, respectively) compared to NOR (p < 0.001). The percentage decrement of power output was greater in BFR (dz = 0.94) and HYP (dz = 0.64) compared to NOR (p < 0.001), as well as in BFR compared to NOR (p = 0.037, dz = 0.30). The percentage decrease of maximal voluntary contraction of the knee extensors after the session was greater in BFR compared to NOR and HYP (p = 0.011, dz = 0.78 and p = 0.027, dz = 0.75, respectively). Accumulated ventilation during exercise was higher in HYP and lower in BFR (p = 0.002, dz = 0.51, and p < 0.001, dz = 0.71, respectively). Peak oxygen consumption was reduced in HYP (p < 0.001, dz = 1.47). Heart rate was lower in BFR during exercise and recovery (p < 0.001, dz = 0.82 and p = 0.012, dz = 0.43, respectively). Finally, aerobic contribution was reduced in HYP compared to NOR (p = 0.002, dz = 0.46) and BFR (p = 0.005, dz = 0.33). Discussion: Thus, this study indicates that power output during RSE is impaired in HYP and BFR and that BFR amplifies neuromuscular fatigue. In contrast, HYP did not impair neuromuscular function but enhanced the ventilatory response along with reduced oxygen consumption.

Grey-Taguchi Approach for Optimizing FSW Parameters in Joining AA6262 and AA5083 Alloys

T his study concentrates on the friction stir welding (FSW) joining of dissimilar AA6262 and AA5083 plates. Four input parameters, namely tool rotational speed (TRS), welding speed (WSP), tool tilt angle (TTA), and pin depth (PDP) are chosen to analyze the output responses of tensile strength (UTS), hardness (HBN), and wear rate (WTR) in welding. The primary objective is to optimize the variables of the FSW process for AA6262 and AA5083 alloys using a combination of Taguchi and Grey Relational Analysis (GRA) methods, while also comprehending the correlations among process variables through the application of these two methods. Utilizing the Taguchi approach, L27 orthogonal array is constructed for conducting FSW experiments. After this, the multi-objective optimization challenge is streamlined into a single-objective optimization task by leveraging the grey relational degree. The significance and impact of the distinct variables are elucidated through the application of Analysis of Variance (ANOVA). Confirmation tests and predictive analysis are performed to validate the optimal variables, which are then compared against the acquired outcomes. The results highlight the welding speed as the paramount parameter, exerting substantial influence over the output responses of the welds in contrast to the other variables under examination. The application of the Grey-Taguchi methodology yields enhancements in Tensile Strength (245.23 MPa) and Hardness (99.2 Hv), while concurrently decreasing the Wear Rate (0.00456 m3/Nm). Furthermore, the Grey Relational Grade exhibits an advancement of 0.1312. These findings collectively signify an increased operational efficiency in the welding process, particularly for dissimilar alloys.

Extending application of explainable artificial intelligence for managers in financial organizations

AbstractAnomalies are a significant challenge for businesses in all industries. Artificial intelligence (AI) based machine learning (ML) detection models can help find aberrant customer transaction behaviour in financial datasets. However, the output responses provided by these AI-based ML models lack transparency and interpretability, making it challenging for financial managers to comprehend the reasoning underlying the AI detections. Suppose managers cannot comprehend how and why AI models develop responses based on the input information. In such cases, AI is unlikely to enhance data-driven decision-making and add value to organizations. This article’s primary objective is to illustrate the capacity of the SHapley Additive exPlanations (SHAP) technique to give finance managers an intuitive explanation of the anomaly detections AI-based ML models generate for a specific customer transaction dataset. Theoretically, we contribute to the literature on international finance by offering a conceptual review of AI algorithmic explainability. We discuss its implications for sustaining a competitive advantage using the concepts of action design research methodology following the research onion framework. We also suggest an explainable AI implementation methodology based on SHAP as a valuable guide for finance managers seeking to boost the transparency of AI-based ML models and to alleviate trust difficulties in data-driven decision-making.

Parametric optimization to establish eco-friendly nanofluid minimum quantity lubrication (NMQL) practice for turning superalloy Inconel 718

Purpose The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra). Design/methodology/approach The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments. Findings The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results. Practical implications The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity. Originality/value No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/

Experimental Investigation of the Influence of Various Wear Parameters on the Tribological Characteristics of AZ91 Hybrid Composites and Their Machine Learning Modeling

Abstract In the current work, the AZ91 hybrid composites are fabricated through the utilization of the stir casting technique, incorporating aluminum oxide (Al2O3) and graphene (Gr) as reinforcing elements. Wear behavior of the AZ91/Gr/Al2O3 composites was examined with the pin-on-disc setup under dry conditions. In this study, the factors such as reinforcement percentage (R), load (L), velocity (V), and sliding distance (D) have been chosen to investigate their impact on the wear-rate (WR) and coefficient of friction (COF). This study utilizes a full factorial design to conduct experiments. The experimental data was critically analyzed to examine the impact of each wear parameter (i.e., R, L, V, and D) on the WR and COF of composites. The wear mechanisms at the extreme conditions of maximum and minimum wear rates are also investigated by utilizing the scanning electron microscope (SEM) images of specimen's surface. The SEM study revealed the presence of delamination, abrasion, oxidation, and adhesion mechanisms on the surface experiencing wear. Machine learning (ML) models, such as decision tree (DT), random forest (RF), and gradient boosting regression (GBR), are employed to create a robust prediction model for predicting output responses based on input variables. The prediction model was trained and tested with 95% and 5% experimental data points, respectively. It was noticed that among all the models, the GBR model exhibited superior performance in predicting WR, with mean square error (MSE) = 0.0398, root-mean-square error (RMSE) = 0.1996, mean absolute error (MAE) = 0.1673, and R2 = 98.89, surpassing the accuracy of other models.

Pulmonary pressure-flow responses to exercise in heart failure treated with angiotensin receptor neprilysin inhibitor

BackgroundThe role of the angiotensin receptor neprilysin inhibitor (ARNI) in cardiac function, particularly its impact on pulmonary circulation, remains underexplored. Recent studies have described abnormal mean pulmonary artery pressure (mPAP)-cardiac output (CO) responses as having the potential to assess the disease state. The aim of this study was to assess the effects of ARNI on pulmonary circulation in heart failure. We measured echocardiographic parameters post 6-min walk (6 MW) and compared the changes with baseline and follow-up. Our hypothesis was that pulmonary pressure-flow relationship of the pulmonary circulation obtained by 6 MW stress echocardiography would be improved with treatment. MethodsWe prospectively enrolled 39 heart failure patients and conducted the 6 MW test indoors. Post-6 MW echocardiography measured echocardiographic variables, and CO was derived from electric cardiometry. Individualized ARNI doses were optimized, with follow-up echocardiographic evaluations after 1 year. ResultsLeft ventricular (LV) volume were significantly reduced (160.7 ± 49.6 mL vs 136.0 ± 54.3 mL, P < 0.001), and LV ejection fraction was significantly improved (37.6 ± 11.3% vs 44.9 ± 11.5%, P < 0.001). Among the 31 patients who underwent 6 MW stress echocardiographic study at baseline and 1 year later, 6 MW distance increased after treatment (380 m vs 430 m, P = 0.003). The ΔmPAP/ΔCO by 6 MW stress decreased with treatment (6.9 mmHg/L/min vs 2.8 mmHg/L/min, P = 0.002). The left atrial volume index was associated with the response group receiving ARNI treatment for pulmonary circulation. ConclusionsInitiation of ARNI was associated with improvement of left ventricular size and LVEF. Additionally, the 6 MW distance increased and the ΔmPAP/ΔCO was improved to within normal range with treatment.

Clinical phenogroup diversity and multiplicity: Impact on mechanisms of exercise intolerance in heart failure with preserved ejection fraction.

We aimed to clarify the extent to which cardiac and peripheral impairments to oxygen delivery and utilization contribute to exercise intolerance and risk for adverse events, and how this relates to diversity and multiplicity in pathophysiologic traits. Individuals with heart failure with preserved ejection fraction (HFpEF) and non-cardiac dyspnoea (controls) underwent invasive cardiopulmonary exercise testing and clinical follow-up. Haemodynamics and oxygen transport responses were compared. HFpEF patients were then categorized a priori into previously-proposed, non-exclusive descriptive clinical trait phenogroups, including cardiometabolic, pulmonary vascular disease, left atrial myopathy, and vascular stiffening phenogroups based on clinical and haemodynamic profiles to contrast pathophysiology and clinical risk. Overall, patients with HFpEF (n = 643) had impaired cardiac output reserve with exercise (2.3 vs. 2.8 L/min, p = 0.025) and greater reliance on peripheral oxygen extraction augmentation (4.5 vs. 3.8 ml/dl, p < 0.001) compared to dyspnoeic controls (n = 219). Most (94%) patients with HFpEF met criteria for at least one clinical phenogroup, and 67% fulfilled criteria for multiple overlapping phenogroups. There was greater impairment in peripheral limitations in the cardiometabolic group and greater cardiac output limitations and higher pulmonary vascular resistance during exertion in the other phenogroups. Increasing trait multiplicity within a given patient was associated with worse exercise haemodynamics, poorer exercise capacity, lower cardiac output reserve, and greater risk for heart failure hospitalization or death (hazard ratio 1.74, 95% confidence interval 1.08-2.79 for 0-1 vs. ≥2 phenogroup traits present). Though cardiac output response to exercise is limited in patients with HFpEF compared to those with non-cardiac dyspnoea, the relative contributions of cardiac and peripheral limitations vary with differing numbers and types of clinical phenotypic traits present. Patients fulfilling criteria for greater multiplicity and diversity of HFpEF phenogroup traits have poorer exercise capacity, worsening haemodynamic perturbations, and greater risk for adverse outcome.

Normalized Error-Based PI Controller and Its Application to the DC–DC Buck Converter

In this article, the structure of the normalized error-based proportional-integral (PI) controller is presented and its application to the DC–DC buck converter is discussed. The main motivation is to overcome the drawbacks of saturation and the limited range of controller gains offered by the traditional PI controller. Initially, the theoretical structure and advantages of an improved PI controller are shown. Next, the problem of regulation of the step-down DC–DC converter is addressed using the proposed controller. The objective is to keep the load voltage constant even when the converter parameters vary. The averaged state-space model of the converter is presented and a detailed stability analysis based on the Lypunov indirect method is carried out. The results show an improved range of controller parameters when the proposed controller is employed. Finally, some simulation results are shown to illustrate the effect of controller parameter variations on the output response. These results also verify the ability of the proposed controller to handle the changes in the load, input voltage, and reference voltage of the converter. Moreover, a comparative simulation study validates the superior transient response of the proposed normalized error-based PI controller over the traditional PI controller.

Parametric assessment of electrical discharge drilling on plasma sprayed functionally graded Al/Al2O3 coating on Al6061-T6 faceplates

Electrical discharge drilling is a non-conventional machining process that is effective and economical for cutting difficult-to-cut conductive materials. In this work, the parametric assessment of functionally graded Al/Al2O3 coating sprayed by plasma on Al6061-T6 faceplates is investigated. Initially, four different graded layers of Al/Al2O3 (50:50, 40:60, 30:70, and 20:80) were processed through plasma spray and deposited coating morphology, splat formation, and cross-sectional structure were confirmed through a scanning electron microscope. The parametric assessment has been investigated based on the effect of electrical discharge drilling parameters peak current (Pc), pulse-on time (Pon), and gap voltage (Vg) on the material removal rate (MRR), tool wear rate (TWR), machining time (MT), surface roughness (SR), taper angle (TA), and overcut (OC). The selected process parameters were Pc from 8 to 14 A, Pon from 60 to 90 μs, and Vg from 20 to 80 V respectively. The parametric assessment was performed based on the objectives of maximizing the MRR and minimizing the TWR, MT, SR, TA, and OC. The effect of process parameters on various output responses was summarized. From the experimentation, MRR is significantly affected by Vg, and maximum MRR is found at the high levels of Pc and Pon. Reduced TWR was observed at the 2nd level of Pc (0.058 mm3 min−1) and 4th level of Vg (0.076 mm3 min−1) and this same factor has more influence on MT. High level of Pc and Pon exhibits maximum TWR with erosion. The results show that Pon time relatively gives low SR (2.36 μm) and minimum OC (0.0015 mm) was achieved at a high level of Vg. Few un-drilled holes with cracks were witnessed due to high TWR. Many drilled holes exhibit < 2° of TA which indicates good dimensional accuracy.

Multi-response optimization of process parameters for sustainable machining of AISI 1018 steel with palm kernel oil-assisted minimum quantity lubrication technique

AbstractIn this study, palm kernel oil, an eco-friendly oil, was extracted from its seeds and then examined for thermal and physiochemical characterization. Subsequently, the performance of palm kernel oil was evaluated in comparison with standard mineral oil during the milling of AISI 1018 steel with a double milling tool using the MQL technique. The influence of cutting conditions such as feed rate, spindle speed, and DOC on response variables (cutting temperature and surface roughness) was studied using a Taguchi L9 orthogonal array. Using the TOPSIS approach (a compensatory method that provides a more realistic form of modeling than non-compensatory methods, and allows trade-offs between criteria, where a poor result in one criterion can be voided by a good result in another criterion) an integrated structure for modeling and optimizing the process was developed. The findings showed that palm kernel oil had a 54% oil yield. In terms of machining, palm kernel oil performed much better than mineral oil lubricants. From the results obtained, palm kernel oil reduced the surface roughness by about 15.6% over mineral oil. Effective cooling in palm kernel oil led to reduced cutting zone temperatures, which in turn extended tool life and improved cutting stability. Additionally, ANOVA was used to show the parameters' significant influence on the output responses. The findings showed that feed rate and depth of cut had the greatest impact on the responses for palm kernel oil and mineral oil, respectively.

Assessing Torque Transfer in Conduction System Pacing: Development and Evaluation of an Ex Vivo Model

BackgroundConduction system pacing (CSP) faces challenges in achieving reliable and safe deployments. Complex interactions between tissue and lead tip can result in endocardial entanglement, a drill effect that prevents penetration. No verified ex vivo model exists to quantitatively assess this relationship. ObjectivesThe purpose of this study was to quantitatively characterize CSP lead tip to tissue responses for 4 commonly used leads. MethodsCSP leads (from Medtronic, Biotronik, Boston Scientific, and Abbott) were examined for helix rotation efficiency in ex vivo ovine right ventricular septa. A custom jig was utilized for rotation measurements. Fifteen turns were executed, documenting tissue-interface changes every 90° using high-resolution photography. Response curves (input rotation vs helix rotation) were evaluated using piecewise linear regression, with a focus on output vs input response slopes and torque breakpoint events. ResultsWe analyzed 3,840 quarter-turn CSP insertions with 4 different lead types. Helix rotations were consistently less than input: Abbott Tendril = 0.21:1, Medtronic 3830 = 0.21:1, Biotronik Solia = 0.47:1, and Boston Scientific Ingevity = 0.56:1. Torque breakpoint events were observed on average 7.22 times per insertion (95% CI: 6.08-8.35; P = NS) across all leads. In 57.8% of insertions (37 of 64), uncontrolled torque breakpoint events occurred, signaling unexpected excess helix rotations. ConclusionsUsing a robust ex vivo model, we revealed a muted helix rotation response compared with input turns on the lead, and frequent torque change events during insertion. This is critical for CSP implanters, emphasizing the potential for unexpected torque breakpoint events, and suggesting the need for novel lead designs or deployment methods to enhance CSP efficiency and safety.

An Integrated Charge Excitation Alternative Current Dielectric Elastomer Generator for Joint Motion Energy Harvesting

AbstractJoint movement is a plentiful source of energy in living organisms; however, it is challenging to harvest due to its multidirectional nature, and effective technologies for harnessing this energy are lacking. In this study, a novel approach is presented to harvest joint motion energy by combining an alternating current dielectric elastomer generator (AC‐DEG) with an electret nanogenerator (ENG). The ACENG‐DEG, utilizing ion hydrogel electrodes and ENG, exhibits remarkable stretchability (860%) and efficiently converts mechanical energy into electrical energy through a stretch‐based mechanism. With only 37 cycles, the device reaches a saturation voltage of 1.18 kV and generates an output charge of 1.96 µC per cycle. Notably, the charge output response speed is approximately four times faster than that of traditional AC‐DEGs. The proposed strategy for harvesting human mechanical energy in this study represents a promising pathway for developing high‐performance energy harvesting devices and self‐powered systems.