Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al<sub>2</sub>O<sub>3</sub>)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Sharma, Milap; Janardhan, Gorti; Sharma, Vipin Kumar; Kumar, Vinod; Joshi, Ravinder Singh

doi:10.1177/09544089211049012

Cited by 6 publications

(3 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to the test parameter combination and the Box-Behnken [20][21][22] model center principle, a four-factor, three-stage response surface test was conducted using the RSM theoretical design, as illustrated in the response surface basic factor table (Table 1). The quantities of standardized factors for the test and the statistics of test results are presented in Table 2.…”

Section: Resultsmentioning

confidence: 99%

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

Liu,

Lei,

et al. 2024

Agriculture

View full text Add to dashboard Cite

A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among the basic factors, the total irrigation flow accounted for only 17.20% of the relatively small self-cleaning flow. The contribution of initial sand content was found to be the smallest, with a coefficient of only 0.0166. Furthermore, the contribution rate of the flow term was significantly higher than that of the initial sand content, with a value of 159.73%. In terms of quadratic interaction, the difference between the interaction term of flushing flow and filter cartridge speed, and the interaction term of filter cartridge speed and self-cleaning flow was 38.42%. On the other hand, the difference within this level for the interaction term between initial sand content and filter cartridge speed, as well as the interaction term between irrigation flow and self-cleaning flow, was 2.82%. Finally, through joint optimization of the response surface and model, the optimal values for the irrigation flow rate, filter cartridge speed, self-cleaning flow rate, and initial sand content were determined to be 121.687 m3·h−1, 1.331 r·min−1, 19.980 m3·h−1, and 0.261 g·L−1; the measured minimum head loss was found to be 21.671 kPa. These research findings can serve as a reference for enhancing the design of farmland filters and optimizing irrigation systems.

show abstract

Section: Resultsmentioning

confidence: 99%

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

Liu,

Lei,

et al. 2024

Agriculture

View full text Add to dashboard Cite

show abstract

“…The next advantage is easy to implement and can be done efficiently using modern computing hardware. Feedforward neural networks can easily handle high-dimensional input data, making them suitable for a wide range of applications like supervised learning problems, including classification, regression, and sequence prediction [60][61][62][63].…”

Section: 𝑣 𝑗mentioning

confidence: 99%

Ensembled Surrogate Assisted Material Extrusion Based 3D Printing Process Parameter Optimization for Enhanced Mechanical Properties of PEEK

Padhy,

Simhambhatla,

Bhattacharjee

2023

Preprint

View full text Add to dashboard Cite

In the material extrusion based 3D printing-fused deposition modelling (FDM), each material may require its own unique set of processing parameters and these parameters can be difficult to optimise and control. This can lead to variability in the final product and can make it challenging to produce parts with consistent quality. Indeed, it is difficult to consider specific FDM parameters to obtain optimum mechanical properties mainly in case of high grade polymers (HGPs) e.g. PEEK, PEK, PPS etc. With the high thermal gradient and heat distribution during their printing, possibilities of residual stresses and deformations are unavoidable, which directly affects its quality and mechanical properties. In this article, an ensembled Surrogate Assisted Evolutionary Algorithm (SAEA) based method is used to optimise the process parameters (layer height, print speed, print direction and nozzle temperature) to enhance the mechanical properties, considered as print quality, of PEEK considering print time into account. The solution obtained through the SAEA approach is further compared with the solution computed through Gray Relational Analysis (GRA) Taguchi, which is used as the benchmark method, to establish the superiority of the proposed one. The comparison indicates the SAEA based solution has 28.86% of higher Ultimate stress value, 66.95% of lower percentage of elongation and 7.14% of lower print time in comparison to the benchmark result. It has also been found that print direction has a greater role in deciding the optimum value of mechanical properties for FDM 3d-printed PEEK material.

show abstract

“…A maximum value of ẟH was obtained as 35.05 HV0.5 for speed of poles (400 rpm), abrasive grit size (90.5 mm), gap (2 mm) and quantity of abrasives (11g). Sharma et al [34] aimed to develop a Box-Behnken design (BBD) model with three-level factorial design for various parameters to predict the ΔRa of Al-6061/SiC/Al2O3/rare earth oxides (REOs) hybrid composites. Another neural model trained using "Levenberg-Marquardt Algorithm" for predicting the surface roughness was developed and then compared with the BBD model.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis for Prediction of Box–Behnken Design Based Surface Roughness for MAFM Finished Aluminium-6063/Silicon Carbide/Boron Carbide (Al-6063/SiC/B4C) Composites Using Nature Inspired Optimization Techniques

Chawla

Mittal

Sharma

2023

Preprint

View full text Add to dashboard Cite

Advanced manufacturing materials like silicon carbide, silicon nitride, and boron carbide are challenging to finish using traditional polishing and grinding procedures with accuracy, super finish and few surface flaws like micro-cracks. In recent years, magnetic field assisted manufacturing technologies have emerged as efficient methods for cleaning, deburring and polishing parts that are made of metal and high-tech engineering materials. The present research work considers the MAFM setup for experimental readings by finishing the hybrid Al/SiC/B4C-MMCs. By making use of the magnetic field to envelope the work-piece in abrasive flow machining, it can result in improving or increasing the materials removal rate along with the surface finish. At times, it becomes necessary to predict the generated surface with a high degree of precision and accuracy. This aptly requires the use of regression models for obtaining such exactness. In the present work, the MAFM process is investigated by the use of hybrid ANN approach. Here ANN is employed to model the input–output relations between the various parameters. For this, purpose, a generalized framework is designed using six inputs and two outputs. The input parameters are extrusion pressure (Ep), mesh number (M), concentration of abrasives (C), work-piece material (Wp), number of cycles (N) and magnetic flux density (Mf). In contrast, the output parameters are material removal rate (MRR) and change in surface roughness (ΔRa). The optimizations implemented through the hybrid PSO-GA-SA-PS algorithms have been used to optimize the MAFM process. Material removal has been accomplished using loosely bonded magnetic abrasive medium composed of silicone carbide (SiC) and the experiments have been carried out on a MAFM experimental setup. The main experimentation is designed by Design Expert® (12.0 version) software and a total of 54 runs are planned and implemented based upon a design matrix suggested by the Box-Behnken design (BBD) of response surface methodology (RSM). Incidentally, the comparison shows that trained artificial neural network models have superior prediction abilities as compared to results obtained from BBD model. It is therefore concluded that the composition of the loosely bound SiC-based magnetic abrasive medium plays a significant role in how well Al/SiC/B4C finishes.

show abstract

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Cited by 6 publications

References 29 publications

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

Ensembled Surrogate Assisted Material Extrusion Based 3D Printing Process Parameter Optimization for Enhanced Mechanical Properties of PEEK

Comparative Analysis for Prediction of Box–Behnken Design Based Surface Roughness for MAFM Finished Aluminium-6063/Silicon Carbide/Boron Carbide (Al-6063/SiC/B4C) Composites Using Nature Inspired Optimization Techniques

Contact Info

Product

Resources

About

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al2O3)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design

Cited by 6 publications

References 29 publications

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors

Ensembled Surrogate Assisted Material Extrusion Based 3D Printing Process Parameter Optimization for Enhanced Mechanical Properties of PEEK

Comparative Analysis for Prediction of Box–Behnken Design Based Surface Roughness for MAFM Finished Aluminium-6063/Silicon Carbide/Boron Carbide (Al-6063/SiC/B4C) Composites Using Nature Inspired Optimization Techniques

Contact Info

Product

Resources

About

Comparative prediction of surface roughness for MAFM finished aluminium/silicon carbide/aluminium trioxide/rare earth oxides (Al/SiC/Al₂O₃)/REOs) composites using a Levenberg–Marquardt Algorithm and a Box–Behnken Design