Parameter optimization and texture evolution in single point incremental sheet forming process

Basak, Shamik; Prasad, K. Sajun; Mehto, Amarjeet; Bagchi, Joy; Ganesh, Y Shiva; Mohanty, Saurav; Sidpara, Ajay; Panda, Sushanta Kumar

doi:10.1177/0954405419846001

Cited by 37 publications

(18 citation statements)

References 46 publications

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“…Figure 4a,b demonstrates that a lower step size and feed rate lead to reduce the maximum forming angle of the bimetal parts with an increase in the tool diameter. The same observations are reported in the previous study of Basak et al [16]. However, the effect of step size is more obvious than the feed rate effect, which is consistent with the feature importance observations in the previous section.…”

Section: Experimental Parameters Effectsupporting

confidence: 92%

“…Maji and Kumar [11] developed forward and inverse models to predict the forming angle, sheet thickness, and surface roughness using an adaptive neuro-fuzzy model and response surface methodology (RSM). Basak et al [16] predicted the maximum forming angle and the quality of the incremental formed parts using RSM (Box-Behnken design). Yao et al [17] established the relation between the process variables (step size, tool diameter, wall angle, and sheet thickness) and surface quality using the response surface methodology.…”

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confidence: 99%

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Experimental Investigation and Optimal Prediction of Maximum Forming Angle and Surface Roughness of an Al/SUS Bimetal Sheet in an Incremental Forming Process Using Machine Learning

et al. 2019

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Bimetal sheets have superior properties as they combine different materials with different characteristics. Producing bimetal parts using a single-point incremental forming process (SPIF) has increased recently with the development of industrial requirements. Such types of sheets have multiple functions that are not applicable in the case of monolithic sheets. In this study, the correlation between the operating variables, the maximum forming angle, and the surface roughness is established based on the ensemble learning using gradient boosting regression tree (GBRT). In order to obtain the dataset for the machine learning, a series of experiments with continuous variable angle pyramid shape were carried out based on D-Optimal design. This design is created based on numerical variables (i.e., tool diameter, step size, and feed rate) and categorical variable (i.e., layer arrangement). The grid search cross-validation (CV) method was used to determine the optimum GBRT parameters prior to model training. After the parameter tuning and model selection, the model with a better generalization performance is obtained. The reliability of the predictive models is confirmed by the testing samples. Furthermore, the microstructure of the aluminum/stainless steel (Al/SUS) bimetal sheet is analyzed under different levels of operating parameters and layer arrangements. The microstructure results reveal that severe cracks are attained in the case of a small tool diameter while a clear refinement is observed when a high tool diameter value with small step down is used for both Al and SUS layers. different materials that have different characteristics, this strengthens the conductivity, corrosion resistance, and the yield strength of the combined sheet. The application of such processes to produce the desired composite parts combines the characteristics of both the forming process and the parent materials to enhance the quality and the formability of the low formable materials and reduce the cost and the weight of manufactured parts.The SPIF of composite sheets has generated considerable recent research interest. In the study of Al-Ghamdi and Hussain [1], the formability improvement of a Cu/steel bimetal sheet during SPIF was better than that in the stamping process due to the limitation of the delamination in the former process. In their further work [2], the relation between the maximum forming angle and the process parameters of the tri-layer Cu/steel/Cu was studied experimentally. The results of the study revealed that a complex relation was observed between the operating parameters of the tri-layer sheet. Gheysarian and Honarpisheh [3] investigated the effect of the layer arrangement, tool path, and tool diameter on formability (i.e., fracture depth) and surface quality of Al/Cu bimetal sheets. The results showed the improvement in the formability corresponding to a large tool diameter, spiral tool path, and copper layer as a contacted sheet. Later, Honarpisheh et al. [4] focused on the improvement of the maximum forming angl...

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Section: Experimental Parameters Effectsupporting

confidence: 92%

mentioning

confidence: 99%

Experimental Investigation and Optimal Prediction of Maximum Forming Angle and Surface Roughness of an Al/SUS Bimetal Sheet in an Incremental Forming Process Using Machine Learning

et al. 2019

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“…The machining runs are conducted according to the experimental matrix generated by the Box-Behnken method (BBM). 32 BBM is chosen due to an acceptable balance between predictive accuracy and economy. The BBM is an effective method, which combines a two-level factorial design.…”

Section: Methodsmentioning

confidence: 99%

Green machining for the dry milling process of stainless steel 304

Nguyen

Mia

Dang

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This article presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed ( V), depth of cut ( a), feed rate ( f), and nose radius ( r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the Grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85% and 57.65%, respectively, and the power factor improves around 28.83%, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices.

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“…Increase in formability is found with an increase in step-depth while working with PLA and PVC plastic materials. 159 However, formability of metals found to be decreased with the increase in step-depth 20,175 or marginal effect 147 of step-depth on formability.…”

Section: Elements Of Isf Processmentioning

confidence: 96%

Review of the effect of process parameters on performance measures in the incremental sheet forming process

Gohil

Modi

2020

Proceedings of the Institution of Mechanical Engineers, Part B:

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Incremental sheet forming process has developed the interest of researchers in the field of sheet metal forming due to high formability and capability to produce prototypes of new products at low cost and minimum lead time. Research work is going on in various front to enhance the process capabilities so that it can be explored for commercial production. In this article, progress and recent development in the field of incremental forming has been reviewed and presented for the benefit of practicing engineers and industry. The effect of various process parameters on the performance of the process have been summarized in this paper. Moreover, the issues which need attention are discussed towards the conclusion of this paper.

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Parameter optimization and texture evolution in single point incremental sheet forming process

Cited by 37 publications

References 46 publications

Experimental Investigation and Optimal Prediction of Maximum Forming Angle and Surface Roughness of an Al/SUS Bimetal Sheet in an Incremental Forming Process Using Machine Learning

Experimental Investigation and Optimal Prediction of Maximum Forming Angle and Surface Roughness of an Al/SUS Bimetal Sheet in an Incremental Forming Process Using Machine Learning

Green machining for the dry milling process of stainless steel 304

Review of the effect of process parameters on performance measures in the incremental sheet forming process

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