scholarly journals Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

2020 ◽  
Vol 92 (6) ◽  
pp. 7-16
Author(s):  
AKSHANSH MISHRA
Keyword(s):  
Machine Learning ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Regression Model ◽  
Decision Trees ◽  
Root Mean Square ◽  
Welded Joints ◽  
Mean Square ◽  
Friction Stir ◽  
Mean Square Errors

In modern computational science, the interplay existing between machine learning and optimization process marks the most vital developments. Optimization plays an important role in mechanical industries because it leads to reduce in material cost, time consumption and increase in production rate. The recent work focuses on performing the optimization task on Friction Stir Welding process for obtaining the maximum Ultimate Tensile Strength (UTS) of the friction stir welded joints. Two machine learning algorithms i.e. Artificial Neural Network (ANN) and Decision Trees regression model are selected for the purpose. The input variables are Tool Rotational Speed (RPM), Tool Traverse Speed (mm/min) and Axial Force (KN) while the output variable is Ultimate Tensile Strength (MPa). It is observed that in case of the Artificial Neural Networks the Root Mean Square Errors for training and testing sets are 0.842 and 0.808 respectively while in case of Decision Trees regression model, the training and testing sets result Root Mean Square Errors of 11.72 and 14.61. So, it can be concluded that ANN algorithm gives better and accurate result than Decision Tree regression algorithm.

scholarly journals Effect of Friction Stir Processing on Gas Tungsten Arc-Welded and Friction Stir-Welded 5083-H111 Aluminium Alloy Joints

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sipokazi Mabuwa ◽  
Velaphi Msomi
Keyword(s):  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Aluminium Alloy ◽  
Friction Stir Processing ◽  
Welded Joints ◽  
Ultrafine Grain ◽  
Dimple Size ◽  
Friction Stir ◽  
Gas Tungsten Arc ◽  
Gas Tungsten

This paper presents the analysis of the friction stir-processed aluminium alloy 5083-H111 gas tungsten arc-welded and friction stir-welded joints. The comparative analysis was performed on the processed and unprocessed gas tungsten arc-welded and friction stir-welded joints of similar aluminium alloy 5083-H111. The results showed a clear distinction between the friction stir processed joints and unprocessed joints. There is a good correlation observed between the microstructural results and the tensile results. Ultrafine grain sizes of 4.62 μm and 7.177 μm were observed on the microstructure of the friction stir-processed friction stir-welded and gas tungsten arc-welded joints. The ultimate tensile strength for friction stir-welded and gas tungsten arc-welded before friction stir processing was 153.75 and 262.083 MPa, respectively. The ultimate tensile strength for friction stir processed friction stir-welded joint was 303.153 MPa and gas tungsten arc-welded joints one was 249.917 MPa. The microhardness values for the unprocessed friction stir-welded and gas tungsten arc-welded joints were both approximately 87 HV, while those of the friction stir-processed ones were 86.5 and 86 HV, respectively. The application of friction stir processing transformed the gas tungsten arc morphology from brittle to ductile dimples and reduced the ductile dimple size of the unprocessed friction stir-welded joints from the range of 4.90–38.33 μm to 3.35–15.59 μm.

scholarly journals Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7970
Author(s):  
Abdel-Rahman Hedar ◽  
Majid Almaraashi ◽  
Alaa E. Abdel-Hakim ◽  
Mahmoud Abdulrahim
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Root Mean Square ◽  
Prediction Models ◽  
Weather Prediction ◽  
Hybrid Models ◽  
Mean Square ◽  
Learning Models ◽  
Mean Square Errors ◽  
Machine Learning Models

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. The effect of the attribute reduction process is investigated using thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia. The feature-reduction has achieved higher classification rates up to 8.5% for the best classifiers and up to 15% for other classifiers, for the different data collection regions. Additionally, in the regression, it achieved improvements of average root mean square error up to 5.6% and in mean absolute error values up to 8.3%. The hybrid models could reduce the root mean square errors by 70.2% and 4.3% than the numerical and machine learning models, respectively, when these models are applied to some dataset. For some reduced feature data, the hybrid models could reduce the root mean square errors by 47.3% and 14.4% than the numerical and machine learning models, respectively.

Investigations on microstructural and mechanical properties of friction stir welded AA 2024-T351

2020 ◽  
Vol 62 (8) ◽  
pp. 793-802
Author(s):  
Şefika Kasman ◽  
Sertan Ozan
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Welded Joints ◽  
Rotational Speed ◽  
Stir Zone ◽  
Tool Rotational Speed ◽  
Friction Stir ◽  
Aa 2024

Abstract In the present study, AA 2024-T351 plates with a thickness of 6 mm were joined using the friction stir welding technique with three different tool rotational speeds and two different pin profiles. Microstructural features and mechanical properties of welded joints were investigated. The grains in recrystallized regions along the stir zone were observed to be almost with invariable sizes. The grain size was revealed to increase with the increase in tool rotational speed. The average grain size was observed to dramatically increase from 2.3 μm to 5.6 μm for welded joints produced with pentagonal shaped pin. All the welded joints were observed to contain defects; the presence of defects exhibited a negative effect on the tensile properties of the welded joint. Most of the defects were observed to localize at the root region of joints. The joint, welded with the tool rotational speed of 250 rpm using pentagonal shaped pin, exhibited ultimate tensile strength with a value of 365 MPa. The ultimate tensile strength of welded joints was found to be higher with the decrease in the tool rotational speed. The welding efficiency of joints was compared with the ultimate tensile strength of base metal; notably, welding efficiency values between 46 % and 80 % were achieved. Microstructural characterizations revealed that Al2Cu (θ phase), Al2CuMg (S phase), and AlCuFeMnSi, Al7Cu2Fe intermetallic particles were dispersed in the stir zone.

scholarly journals Evaluations of Machine Learning-Based CYGNSS Soil Moisture Estimates against SMAP Observations

2020 ◽  
Vol 12 (21) ◽  
pp. 3503 ◽  
Author(s):  
Volkan Senyurek ◽  
Fangni Lei ◽  
Dylan Boyd ◽  
Ali Cafer Gurbuz ◽  
Mehmet Kurum ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Correlation Coefficient ◽  
Root Mean Square ◽  
Temporal Resolution ◽  
Mean Square ◽  
In Situ Observations ◽  
Spatio Temporal ◽  
Mean Square Errors

This paper presents a machine learning (ML) framework to derive a quasi-global soil moisture (SM) product by direct use of the Cyclone Global Navigation Satellite System (CYGNSS)’s high spatio-temporal resolution observations over the tropics (within ±38° latitudes) at L-band. The learning model is trained by using in-situ SM data from the International Soil Moisture Network (ISMN) sites and various space-borne ancillary data. The approach produces daily SM retrievals that are gridded to 3 km and 9 km within the CYGNSS spatial coverage. The performance of the model is independently evaluated at various temporal scales (daily, 3-day, weekly, and monthly) against Soil Moisture Active Passive (SMAP) mission’s enhanced SM products at a resolution of 9 km × 9 km. The mean unbiased root-mean-square difference (ubRMSD) between concurrent (same calendar day) CYGNSS and SMAP SM retrievals for about three years (from 2017 to 2019) is 0.044 cm3 cm−3 with a correlation coefficient of 0.66 over SMAP recommended grids. The performance gradually improves with temporal averaging and degrades over regions regularly flagged by SMAP such as dense forest, high topography, and coastlines. Furthermore, CYGNSS and SMAP retrievals are evaluated against 170 ISMN in-situ observations that result in mean unbiased root-mean-square errors (ubRMSE) of 0.055 cm3 cm−3 and 0.054 cm3 cm−3, respectively, and a higher correlation coefficient with CYGNSS retrievals. It is important to note that the proposed approach is trained over limited in-situ observations and is independent of SMAP observations in its training. The retrieval performance indicates current applicability and future growth potential of GNSS-R-based, directly measured spaceborne SM products that can provide improved spatio-temporal resolution than currently available datasets.

An appraisal of characteristic mechanical properties and microstructure of friction stir welding for Aluminium 6061 alloy – Silicon Carbide (SiCp) metal matrix composite

2019 ◽  
Vol 13 (4) ◽  
pp. 5804-5817
Author(s):  
Ibrahim Sabry
Keyword(s):  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Metal Matrix Composite ◽  
Matrix Composite ◽  
Metal Matrix ◽  
Welded Joint ◽  
Rotation Speed ◽  
Fusion Welding ◽  
Friction Stir ◽  
Al 6061

It is expected that the demand for Metal Matrix Composite (MMCs) will increase in these applications in the aerospace and automotive industries sectors, strengthened AMC has different advantages over monolithic aluminium alloy as it has characteristics between matrix metal and reinforcement particles.  However, adequate joining technique, which is important for structural materials, has not been established for (MMCs) yet. Conventional fusion welding is difficult because of the irregular redistribution or reinforcement particles.  Also, the reaction between reinforcement particles and aluminium matrix as weld defects such as porosity in the fusion zone make fusion welding more difficult. The aim of this work was to show friction stir welding (FSW) feasibility for entering Al 6061/5 to Al 6061/18 wt. % SiCp composites has been produced by using stir casting technique. SiCp is added as reinforcement in to Aluminium alloy (Al 6061) for preparing metal matrix composite. This method is less expensive and very effective. Different rotational speeds,1000 and 1800 rpm and traverse speed 10 mm \ min was examined. Specimen composite plates having thick 10 mm were FS welded successfully. A high-speed steel (HSS) cylindrical instrument with conical pin form was used for FSW. The outcome revealed that the ultimate tensile strength of the welded joint (Al 6061/18 wt. %) was 195 MPa at rotation speed 1800 rpm, the outcome revealed that the ultimate tensile strength of the welded joint (Al 6061/18 wt.%) was 165 MPa at rotation speed 1000 rpm, that was very near to the composite matrix as-cast strength. The research of microstructure showed the reason for increased joint strength and microhardness. The microstructural study showed the reason (4 %) for higher joint strength and microhardness.  due to Significant   of SiCp close to the boundary of the dynamically recrystallized and thermo mechanically affected zone (TMAZ) was observed through rotation speed 1800 rpm. The friction stir welded ultimate tensile strength Decreases as the volume fraction increases of SiCp (18 wt.%).

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

Vibratory weld conditioning during gas tungsten arc welding of al 5052 alloy on the mechanical and micro-structural behavior

2020 ◽  
Vol 17 (6) ◽  
pp. 831-836
Author(s):  
M. Vykunta Rao ◽  
Srinivasa Rao P. ◽  
B. Surendra Babu
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Tensile Strength ◽  
Ultimate Tensile Strength ◽  
Welded Joints ◽  
Great Influence ◽  
Welding Process ◽  
Microstructural Characterization ◽  
Content Type ◽  
Average Grain Size

Purpose Vibratory weld conditioning parameters have a great influence on the improvement of mechanical properties of weld connections. The purpose of this paper is to understand the influence of vibratory weld conditioning on the mechanical and microstructural characterization of aluminum 5052 alloy weldments. An attempt is made to understand the effect of the vibratory tungsten inert gas (TIG) welding process parameters on the hardness, ultimate tensile strength and microstructure of Al 5052-H32 alloy weldments. Design/methodology/approach Aluminum 5052 H32 specimens are welded at different combinations of vibromotor voltage inputs and time of vibrations. Voltage input is varied from 50 to 230 V at an interval of 10 V. At each voltage input to the vibromotor, there are three levels of time of vibration, i.e. 80, 90 and 100 s. The vibratory TIG-welded specimens are tested for their mechanical and microstructural properties. Findings The results indicate that the mechanical properties of aluminum alloy weld connections improved by increasing voltage input up to 160 V. Also, it has been observed that by increasing vibromotor voltage input beyond 160 V, mechanical properties were reduced significantly. It is also found that vibration time has less influence on the mechanical properties of weld connections. Improvement in hardness and ultimate tensile strength of vibratory welded joints is 16 and 14%, respectively, when compared without vibration, i.e. normal weld conditions. Average grain size is measured as per ASTM E 112–96. Average grain size is in the case of 0, 120, 160 and 230 is 20.709, 17.99, 16.57 and 20.8086 µm, respectively. Originality/value Novel vibratory TIG welded joints are prepared. Mechanical and micro-structural properties are tested.

scholarly journals Evidence That Supertriangles Exist in Nature from the Vertical Projections of Koelreuteria paniculata Fruit

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 23
Author(s):  
Yuping Li ◽  
Brady K. Quinn ◽  
Johan Gielis ◽  
Yirong Li ◽  
Peijian Shi
Keyword(s):  
Root Mean Square ◽  
Goodness Of Fit ◽  
Mean Square ◽  
Sea Stars ◽  
Different Types ◽  
Koelreuteria Paniculata ◽  
Two Parameters ◽  
Mean Square Errors

Many natural radial symmetrical shapes (e.g., sea stars) follow the Gielis equation (GE) or its twin equation (TGE). A supertriangle (three triangles arranged around a central polygon) represents such a shape, but no study has tested whether natural shapes can be represented as/are supertriangles or whether the GE or TGE can describe their shape. We collected 100 pieces of Koelreuteria paniculata fruit, which have a supertriangular shape, extracted the boundary coordinates for their vertical projections, and then fitted them with the GE and TGE. The adjusted root mean square errors (RMSEadj) of the two equations were always less than 0.08, and >70% were less than 0.05. For 57/100 fruit projections, the GE had a lower RMSEadj than the TGE, although overall differences in the goodness of fit were non-significant. However, the TGE produces more symmetrical shapes than the GE as the two parameters controlling the extent of symmetry in it are approximately equal. This work demonstrates that natural supertriangles exist, validates the use of the GE and TGE to model their shapes, and suggests that different complex radially symmetrical shapes can be generated by the same equation, implying that different types of biological symmetry may result from the same biophysical mechanisms.

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