Experimental Study on Lateral Resolution of Phased Array Ultrasonic Testing of Irregular Structure Weld Defects

In the recent past, Non-Destructive Testing (NDT) has become the most popular technique due to its efficiency and accuracy without destroying the object and maintaining its original structure and gathering while examining external and internal welding defects. Generally, the NDT environment is harmful which is distinguished by huge volatile fields of electromagnetic, elevated radiation emission instability, and elevated heat. Therefore, a suitable NDT approach could be recognized and practiced. In this paper, a novel algorithm is proposed based on a Phased array ultrasonic test (PAUT) for NDT to attain the proper test attributes. In the proposed methodology, the carbon steel welding section is synthetically produced with various defects and tested using the PAUT method. The signals which are acquired from the PAUT device are having noise. The Adaptive Least Mean Square (ALMS) filter is proposed to filter PAUT signal to eliminate random noise and Gaussian noise. The ALMS filter is the combination of low pass filter (LPF), high pass filter (HPF), and bandpass filter (BPF). The time-domain PAUT signal is converted into a frequency-domain signal to extract more features by applying the Empirical Wavelet Transform (EWT) algorithm. In the frequency domain signal, first order and second order features extraction techniques are applied to extract various features for further classification. The Deep Learning methodology is proposed for the classification of PAUT signals. Based on the PAUT signal features, the Deep Convolution Neural Network (DCNN) is applied for further classification. The DCNN will classify the welding signal as to whether it is defective or non-defective. The Confusion Matrix (CM) is used for the estimation of measurement of performance of classification as calculating accuracy, sensitivity, and specificity. The experiments prove that the proposed methodology for PAUT testing for welding defect classification is obtained more accurately and efficiently across existing methodologies by providing numerical and graphical results.

ABSTRACTNondestructive evaluation (NDE) techniques of phased array ultrasonic testing (PAUT) and digital X-ray radiography were employed on friction stir (FS)-welded Aluminum Alloy (AA)-2219-T87 specimens. PAUT intricacies required for scanning of FS-welded specimens with a 10-MHz 32-element transducer are discussed. The time corrected gain (TCG) calibration is required for scanning with an increase in index offset to compensate for decrease in A-Scan signal peak amplitude. Calibration techniques to find small defects with appropriate size tolerances are also established. The NDE technique of digital X-ray radiography is compared to PAUT, where it was found that a calibrated PAUT system is able to discover defects less than 0.2 mm where X-ray radiography could not. Incomplete penetration (IP), wormhole (WH), surface cavity (SC), and internal void (IV) defects are analyzed. Furthermore, an online PAUT system for FSW has been developed and successfully tested. The work provided herein will provide a gateway for an ultimate goal of an automated PAUT online sensing system.

Integration of RSM with Grey based Taguchi Method for optimization of pulsed TIG welding process parameters

Optimization of batch process parameters for Congo red color removal by Neurospora crassa live fungal biomass with wheat bran dual adsorbent using response surface methodology

This study examined the application of pulverize clay brick wastes as alternative constituent for Portland cement replacement in green concrete production using the response surface methodology (RSM). The adopted response surface approach is central composite design (CCD). The statistical models were developed between the concrete constituents (clay brick powder and water cement ratio) and their response variables (slump, compressive and split tensile strength). Relationships were established and mathematical models in terms of actual factors from predicted responses were developed. The influence of the considered factors on the properties of response were visually observed from the contour and response surface plots. The statistical models of experimental values clearly depict that the obtained experimental values are in close agreement with the predicted values, which validates the response surface models with desirability value of 1. The results show that pulverized clay brick waste have significant influence on the properties of concrete than the water cement (w/c) ratio, however, a declining trend was seen for all analyzed concrete properties. In addition, this study showed that the pulverized waste brick can be used as alternative substitute for Portland cement up to 20% in the production of sustainable concrete.

Welding quality detection of metro train body based on ABC mask R-CNN

Cassava starch fermentations were conducted in batch cultures to optimize the effect of divalent cations on ethanol production with Saccharomyces pastorianus using the central composite rotatable response surface design. Divalent cations used were magnesium (Mg 2+ ), zinc (Zn 2+ ) and calcium (Ca 2+ ). Maximum ethanol concentration of 11.12% v/v was obtained with cationic concentration combination of 64, 0.48 and 30 mg/l for Mg 2+ , Zn 2+ and Ca 2+ , respectively, after 96 h of the fermentations. Minimum ethanol concentration of 7.53% (v/v) was obtained at a variable combination of 64, 0.48 and 76 mg/l for Mg 2+ , Zn 2+ and Ca 2+ , respectively. Thus response surface methodology was used in a central composite design to optimize the process variables of Mg 2+ , Zn 2+ , Ca 2+ in the fermentation medium, thereby increasing the ethanol production from 10.5% in the control to 11.12%. There were significant linear and quadratic effects of Zn 2+ as well as a significant (P 0.05) negative quadratic effect of Ca 2+ on ethanol production, which are confirmed in the response surface plots. Keywords: Ethanol, Saccharomyces pastorianus , cassava starch, hydrolyzates, central composite design, response surface methodology African Journal of Biotechnology Vol. 9(33), pp. 5423-5429, 16 August, 2010

Fabrication of silver nanoparticles-loaded chitosan-polylactic acid based biofilms was successfully employed for investigating the optimal of mechanical properties (i.e. tensile strength and elongation at break) of biofilms using central composite design (CCD), response surface methodology (RSM). In this study, only two factors that influences the biofilm mechanical properties were selected namely concentration of polyethelene glycol 400 (PEG) and percentage volume of polylactic acid (PLA)/chitosan. Analysis of results was performed by using response surface methodology (RSM) to avoid the traditional one-factor-at-a-time experiments. Common statistical tools such as analysis of variance (ANOVA) and response surface plot were used to determine the optimal tensile strength and elongation at break responses. Central composite design (CCD) builds a response surface for mechanical properties of biofilms optimization. From the results of statistical analysis, it could be concluded that the optimal conditions for mechanical properties of biofilms were 7.93% w/w concentration of polyethylene glycol (PEG) and 28.79%/71.21% percentage volume of polylactic acid (PLA)/chitosan. At this optimum stage, 7.99 MPa of tensile strength and 32.6 % elongation at break were obtained. Then, results of verification process have shown that the percentage errors are 2.08% for tensile strength and 3.89% elongation at break, respectively.

Although phased array ultrasonic testing (PAUT) technology has been widely employed in pipeline weld defect recognition, current defect recognition still relies on manual operation, which limits its efficiency. By combining deep learning with PAUT, this paper presents a new neural architecture based on YOLOv5 called DFW-YOLO that can call defect indications automatically. To address redundant defect feature maps, the FasterNet backbone is introduced to enhance the model’s feature extraction ability. Additionally, partial dilated convolution (PDConv) and partial convolution (PConv) with an atrous convolution branch are designed to extend the receptive field of the backbone network, mitigating the chance of feature loss. Furthermore, a weighted generalised feature pyramid network (WGFPN) structure is proposed as the neck network. This structure combines the reparameterized generalised feature pyramid network (RepGFPN) with the weighted aggregated concatenation (WAC) structure and eliminates inefficient branches to enhance the model’s feature fusion ability. The experimental results demonstrated that DFW-YOLO outperforms the original YOLOv5 in terms of detection performance. The precision, recall, mean average precision (mAP)@0.5, and mAP@0.5:0.95 are indicators for DFW-YOLO and reach 97.4%, 97.5%, 98.3%, and 68.2%, respectively, marking 4.8%, 5%, 2.7%, and 22.5% improvement compared to the original YOLOv5.

Aim of present work is to develop optimized sustained release dosage form of Bupropion hydrochloride using Formulation by Design (FbD) approach. Development and optimization of formulation batches was done by design experiment using Central Composite Design (CCD). Tablets were formulated by direct compression technique and evaluated. The impact of independent variables like concentration of Hydroxypropyl Methyl Cellulose (HPMC K4M) and Carbopol (CP 934P) were observed on dependent variable like hardness of tablet, drug release in 12 h (Q12h) and the time for fifty percent release of drug (T50%). Polynomial equations were generated using multiple linear regression analysis (MLRA), response surface plots and contour plots were drawn, optimum formulations were selected by brute force method. The hardness and Q12h was found in the range of 4.4- 4.7 Kg/cm2 and 88.19- 96.7% respectively, while T50% was found in the range of 3.5- 5.5h. Validation of optimization study performed using four confirmatory experimental runs which indicated very high degree of prognostic ability of FbD methodology with percentage error varied between -0.024% and 0.024 %. The overlaying of all these plots provided an overlay plot, which signified the region of optimization. Thus, central composite design (CCD) is a useful tool in the development of optimized dosage form along with the significance of independent variable as well as least investment of money, manpower and time. Keywords: Formulation by Design, Central Composite Design, Bupropion Hydrochloride.

Optimization of ultrasonic-assisted extraction of bioactive alkaloid compounds from rhizoma coptidis (Coptis chinensis Franch.) using response surface methodology

Optimization of manufacturing conditions for activated carbon from Turkish lignite by chemical activation using response surface methodology

Optimizing media and substrate for efficient anaerobic biological ammonia synthesis: A response surface methodology approach

This article examines the application of advanced cold metal transfer plus pulse composite arc technology for the thin‐walled additive manufacturing of high‐strength aluminum alloy AA2024. Response surface methodology is employed to design experiments and variance analysis is utilized to assess the model's reliability. Results are further optimized and validated using the non‐dominated sorting genetic algorithm II. The focus is on optimizing key process parameters—wire feeding speed, interlayer temperature, protective gas flow rate, and welding wire elongation—to minimize porosity and splash. A comprehensive analysis of parameter interactions is conducted using contour maps and response surface plots. The findings indicate that wire elongation most significantly affects porosity, while wire feeding speed has the greatest impact on splash. The optimized parameters effectively reduce grain size under low heat input conditions, decrease hydrogen‐induced porosity and solidification defects, achieving a porosity rate of 0.46% and a splash rate of 0.83%.

Laser cladding is a novel additive manufacturing and surface treatment process associated with many interactive process parameters. Using the response surface method with a central composite design, a structured design of experiments approach was chosen to examine the influence of selected process parameters on the bead geometry and hardness for single-track laser-cladded specimens using AISI 420 stainless steel powder on a AISI 1018 substrate. In the present study, robust predictive models for hardness, bead aspect ratio, and wetting angle with the substrate were determined using multiple regression analysis. The geometry and hardness relationships to the process inputs were evaluated using F-statistics from the analysis of variance and compared by utilizing perturbation plots, 3D surface mapping, and contour plots. The highly coupled non-linear relationships are evident from these analyses, but this study revealed that the laser speed has the most significant effect on the bead microhardness and the powder feed rate was found to be the most significant parameter for these bead geometry parameters. The perturbation plots confirmed this sensitivity of those process parameters. This research study gives a guideline for the selection of appropriate process parameters for the laser cladding process to achieve desired hardness and bead geometry.