Study and optimization of process parameters for deformation machining stretching mode
Downloads
Published
DOI:
https://doi.org/10.58414/SCIENTIFICTEMPER.2024.15.2.31Keywords:
Deformation machining, Surface roughness, Hardness, Grey Relation Analysis, Analysis of Variance (ANOVA)Dimensions Badge
Issue
Section
License
Copyright (c) 2024 The Scientific Temper

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Monolithic thin-structure parts with intricate geometric designs are employed in a variety of aeronautical, medical, marine, and automotive applications, which include the moldlines of the fuselage, turbine blades, impellers, avionic shelves, irregular fins, prostheses, bone and joint support, and skull plates. The deformation machining process is the solution to this challenging and difficult-to-manufacture high-quality components with intricate narrow geometries at competitive prices. The aim of the present study is to assess the effect of process parameters of the deformation machining process wherein a thin, floor-like structure is created by milling and is then formed using a single-point incremental forming tool. Investigation involves the design and development of tooling required for the process followed by feasibility checking of the process. To examine the impact of different process parameters on the process response, the experiments were carried out using the design of experiments. The findings of this study indicate that different process parameters, including spindle speed, tool diameter, incremental step depth, and feed rate, have a substantial impact on the process response, like thickness, surface finish, and hardness. Uneven and non-uniform surface patterns during SEM indicate that it is needed to examine the impact of process parameters. This research involves the feasibility study of a new hybrid technique of deformation machining. Conventionally, a metallic structure is produced by joining various components through welding or by fastening. These methods require additional expenditure on equipment, storage, floor space, human resources, etc., with higher lead time. Joining increases weight and reduces fatigue strength. The creation of monolithic structures can eliminate all these disadvantages.Abstract
How to Cite
Downloads
Similar Articles
- Venkatesh R, A study on women empowerment by enhancing saving capabilities – through self-help groups , The Scientific Temper: Vol. 15 No. 04 (2024): The Scientific Temper
- M. Deepika, I Antonitte Vinoline, Optimization of an Advanced Integrated Inventory Model Considering Shortages and Deterioration across Varying Demand Functions , The Scientific Temper: Vol. 16 No. 09 (2025): The Scientific Temper
- Hardik Talsania, Kirit Modi, Attention-Enhanced Multi-Modal Machine Learning for Cardiovascular Disease Diagnosis , The Scientific Temper: Vol. 17 No. 01 (2026): The Scientific Temper
- Shruti Bhonsle, Vikrantkumar Dasani, M N Parmar, Digital Campaigns and Behaviour Change Communication for Organ Donation , The Scientific Temper: Vol. 17 No. 03 (2026): The Scientific Temper
- MRINAL CHANDRA, DEVELOPMENT OF METHOD FOREXTRACTIVE SPECTROPHOTOMETRIC DETERMINATION OF COPPER(II) WITH N-BENZOYL THIOUREATHIOSEMICARBONZONE(MAAPHE) AS AN ANALYTICAL REAGENT , The Scientific Temper: Vol. 8 No. 1&2 (2017): The Scientific Temper
- Rakesh Thakur, Surender Singh, The Pangwala People of Pangi Region: Ethnography of Rituals and Ceremonies , The Scientific Temper: Vol. 13 No. 02 (2022): The Scientific Temper
- Isaac Asampana, Henry M. Akwetey, Ben Ocra, Jones Y. Nyame, Albert A. Akanferi, Hannah A. Tanye, Factors motivating the adoption of virtual learning environments in higher education. Is gender relevant? , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Milindkumar N. Dandale, Amar P. Yadav, P. S. K. Reddy, Seema G. Kadu, Madhusudana T, Manthan S. Manavadaria, Deep learning enhanced drug discovery for novel biomaterials in regenerative medicine utilizing graph neural network approach for predicting cellular responses , The Scientific Temper: Vol. 15 No. 01 (2024): The Scientific Temper
- Tewoderos Legesse, Bekelech Sharew, Evaluation of white seeded sesame (Sesamum indicium L.) genotypes on growth and yield performance in Menit Goldya Woreda of West Omo Zone, SWE , The Scientific Temper: Vol. 15 No. 02 (2024): The Scientific Temper
- Annalakshmi D., C. Jayanthi, An asymmetric key encryption and decryption model incorporating optimization techniques for enhanced security and efficiency , The Scientific Temper: Vol. 15 No. 03 (2024): The Scientific Temper
<< < 23 24 25 26 27 28 29 30 31 32 > >>
You may also start an advanced similarity search for this article.

