Optimization of Zinc Coating Thickness by Unreplicated Factorial Design of Experiments in Hot-Dip Galvanization Process

Verma, Neha; Sharma, Vinay; Badar, M. Affan; Choubey, Niharika; Parihar, Rityuj Singh

doi:10.1007/s12541-022-00695-2

Cited by 9 publications

(2 citation statements)

References 33 publications

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“…Process and quality improvement tools especially utilizing lean and Six Sigma concepts are widely used in both manufacturing and service sectors (Freimut et al, 2005;Soković et al, 2009;Verma et al, 2021;Alblooshi et al, 2022;Bader et al, 2020;Badiru, 2005;Bhandari et al, 2021;Soltani and Bhandari 2023;Bartlett et al, 2023;Verma et al, 2022). Design of Experiments (DOE) is a powerful technique that is often used as an applied statistical tool that deals with planning, conducting, analyzing, and interpreting controlled experiments in a laboratory to explore and evaluate the factors that control the value of a factor or various factors (Bower, 2023).…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Welding Parameters Affecting the Tensile Strength of Mild Steel Plates

Joshi,

Bhandari

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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This study is aimed at using various statistical analyses within the Design of Experiments (DOE) technique to investigate and analyze the effect of welding process parameters on the tensile strength of A36-grade mild steel. Three process parameters and two corresponding levels were considered and the MIG welding process was performed. The ideal conditions to achieve the highest strength were determined and the results were obtained by running the design on a statistical software Minitab. This experimental study found that the only factor having a significant contribution to the tensile stress of the plates in the selected scenario is the weld speed. In addition, this study provides valuable insights for enhancing welding procedures and techniques. The paper concludes with a reasonable interpretation of the test results and justifies the research questions that were proposed.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Welding Parameters Affecting the Tensile Strength of Mild Steel Plates

Joshi,

Bhandari

2023

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…Mounayri et al [28] developed and used an ANN model to predict cutting force and surface roughness in end milling. Verma et al [29] used an un-replicated factorial design to optimize zinc coating thickness in hot-dip galvanization process. Jiang et al [30] conducted experiments to investigate the characteristics of gas lm in micro-electrochemical discharge machining.…”

Section: Introductionmentioning

confidence: 99%

An analysis to enhance the machining performance of micro-EDM for drilling of blind micro-hole using ANN

Pratap,

Upadhyay,

Sharma

et al. 2023

Int J Adv Manuf Technol

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The high demand for compact and multitasking devices in the market has been a driving force behind the growing interest in microfabrication techniques. These techniques have wide-ranging applications in many industries, including aerospace, automobile, electronics, and defense. Micro electrical discharge machining (µEDM) techniques have the unique ability to produce highly precise and intricate features on small components, which has further fueled the demand for such products. However, with the increasing demand for micro-featured products, there is a pressing need to enhance the process capability of µEDM process. This work aims to address this need by focusing on enhancing the performance of µEDM by varied process parameters and materials such as copper, brass, and tungsten carbide for the drilling of blind micro holes. Surface roughness (SR) and material removal rate (MRR) are the main performance factors taken into account in this investigation. Notably, the minimum SR was achieved on tungsten carbide, while the maximum MRR was achieved using copper electrodes. For SR and MRR, arti cial neural network (ANN) models have been constructed that predict with more than 90% accuracy. These ndings have signi cant implications for the future of microfabrication using µEDM.

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Effect of Dipping Time on Microstructure and Properties of Zn-1Al-2Mg Coating by Hot-Dipping Galvanizing

Zhang,

Ding,

et al. 2024

J. of Materi Eng and Perform

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Optimization of Zinc Coating Thickness by Unreplicated Factorial Design of Experiments in Hot-Dip Galvanization Process

Cited by 9 publications

References 33 publications

Statistical Analysis of Welding Parameters Affecting the Tensile Strength of Mild Steel Plates

Statistical Analysis of Welding Parameters Affecting the Tensile Strength of Mild Steel Plates

An analysis to enhance the machining performance of micro-EDM for drilling of blind micro-hole using ANN

Effect of Dipping Time on Microstructure and Properties of Zn-1Al-2Mg Coating by Hot-Dipping Galvanizing

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