Prediction of the properties of products of the blanking process is nowadays still mainly an empirical effort. The lack of fundamental knowledge of the process may cause lengthy process development, and obstructs process innovation. Consequently, a validated numerical model of the process would be of great value. In this paper, an elasto–plastic finite-element model will be introduced, in which the extremely large deformations occurring, are handled by an operator split arbitrary Langrange Euler (ALE) method combined with remeshing. Special attention is given to the convective part of the ALE formulation. Ductile fracture is incorporated by a discrete crack approach. To demonstrate the possibilities of the procedure, some simulation results are presented.

Numerical modelling of the metal blanking process

Single point incremental forming (SPIF) is a truly die-less forming process which is quite suitable for the batch type and prototype production due to economical tooling cost, shorter lead time and ability to form nonsymmetrical geometries without using expensive dies for manufacturing complex components of sheet metal. This process mainly finds application in the medical sector, aerospace, and automotive industry. Moreover, lack of available information about formability of the process makes it limited for industrial applications. SPIF applicability can be ensured on the industrial scale when appropriate guidelines are highlighted regarding the relation between input parameters and the formability of the process. This paper insights the impact of forming tool shape, tool diameter, wall angle, step size, sheet thickness, and tool rotation on the formability of the AA2024-O aluminum alloy sheet material. Forming depth has been measured by scanning the components using a non-contact 3D scanner. Wall angle and step size have been proved more significant factors which affect the formability greatly.

Parametric effects on formability of AA2024-O aluminum alloy sheets in single point incremental forming

Sheet metal forming (SMF) is one of the most popular technologies for obtaining finished products in almost every sector of industrial production, especially in the aircraft, automotive, food and home appliance industries. Parallel to the development of new forming techniques, numerical and empirical approaches are being developed to improve existing and develop new methods of sheet metal forming. Many innovative numerical algorithms, experimental methods and theoretical contributions have recently been proposed for SMF by researchers and business research centers. These methods are mainly focused on the improvement of the formability of materials, production of complex-shaped parts with good surface quality, speeding up of the production cycle, reduction in the number of operations and the environmental performance of manufacturing. This study is intended to summarize recent development trends in both the numerical and experimental fields of conventional deep-drawing, spinning, flexible-die forming, electromagnetic forming and computer-controlled forming methods like incremental sheet forming. The review is limited to the considerable changes that have occurred in the SMF sector in the last decade, with special attention given to the 2015–2020 period. The progress observed in the last decade in the area of SMF mainly concerns the development nonconventional methods of forming difficult-to-form lightweight materials for automotive and aircraft applications. In evaluating the ecological convenience of SMF processes, the tribological aspects have also become the subject of great attention.

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Recent Developments and Trends in Sheet Metal Forming

Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and the construction industry. The various milling operations for these components have key performance indicators: accuracy, surface roughness (Ra), and machining time for removal of a unit volume min/cm3 (Tm). The specified surface roughness values for machining each component is achieved based on the prototype specifications. However, poor adherence to specifications can result in the rejection of the machined parts, implying extra production costs and raw material wastage. An algorithm using an artificial neural network (ANN) with the Edgeworth-Pareto method is presented in this paper to optimize the cutting parameter in CNC face-milling operations. The set of parameters are adjusted to improve surface roughness and minimal unit-volume material removal rates, thereby reducing production costs and improving accuracy. An ANN algorithm is designed in Matlab, based on a 3–10-1 Multi-Layer Perceptron (MLP), which predicts the Ra of the workpiece surface to an accuracy of ± 5.78% within the range of the experimental angular spindle speed, feed rate, and cutting depth. An unprecedented Pareto frontier for Ra and Tm was obtained for the finished grade-H steel workpiece using an ANN algorithm that was then used to determine optimized cutting conditions. Depending on the production objective, one or the other of two sets of optimum machining conditions can be used: the first one sets a minimum cutting power, while the other sets a maximum Tm with a slight increase (under 5%) in milling costs.

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Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

State of the Art Review on Process, System, and Operations Control in Modern Manufacturing

Single point incremental forming (SPIF) is a novel and potential process for sheet metal prototyping and low volume production applications. This article is focuses on the development of predictive models for surface roughness estimation in SPIF process. Surface roughness in SPIF has been modeled using three different techniques namely, Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Genetic Programming (GP). In the development of these predictive models, tool diameter, step depth, wall angle, feed rate and lubricant type have been considered as model variables. Arithmetic mean surface roughness (Ra) and maximum peak to valley height (Rz) are used as response variables to assess the surface roughness of incrementally formed parts. The data required to generate, compare and evaluate the proposed models have been obtained from SPIF experiments performed on Computer Numerical Control (CNC) milling machine using Box–Behnken design. The developed models are having satisfactory goodness of fit in predicting the surface roughness. Further, the GP model has been used for optimization of Ra and Rz using genetic algorithm. The optimum process parameters for minimum surface roughness in SPIF have been obtained and validated with the experiments and found highly satisfactory results within 10% error.

Modeling and optimization of surface roughness in single point incremental forming process

This paper focuses on the formability and thickness distribution in incremental sheet forming (ISF) of extra-deep drawing steel (EDD). In ISF, the formability of the material is primarily measured by the maximum formable wall angle and maximum allowable thinning. The maximum wall angle is generally obtained by forming frustum of cones and square pyramids having different wall angles till fracture, which requires a large number of experiments. Therefore in the present study, a continuously varying wall angle conical frustum (VWACF) was used to predict the maximum wall angle to minimize the number of experiments. VWACF is generated using circular, parabolic, elliptical and exponential generatrices. In order to get the maximum allowable thinning, the thickness of the formed geometry has been measured at various points along the depth. In addition, the thickness distribution has been computed theoretically based on the sine law and also using finite element code LS-DYNA. Theoretical and simulated thickness values have been compared with measured thickness values. It was found from the results that the finite element model was more accurate than theoretical model in predicting thickness distribution.

Experimental and numerical studies on formability of extra-deep drawing steel in incremental sheet metal forming

Evaluation of surface roughness in incremental forming using image processing based methods

Single-point incremental forming is the most economical process to make the sheet metal prototypes and low volume production without any dedicated dies and simple tooling. The surface finish of the parts produced in this process gets affected by various process parameters. To get the proper quality of parts for functional applications, it is important to understand the effect of various process parameters on part quality. Another drawback with this process is long processing time, which also gets affected by different process parameters. Thus, the first objective of this article is to study the effect of various process parameters on surface roughness and manufacturing time. Second objective is to carry out the multi-objective optimization to get optimum process parameters. For this, detailed experiments are conducted using Box–Behnken design. The effects of step depth, tool diameter, wall angle, feed rate and lubricant type on surface roughness and processing time have been investigated. Based on experim...

Parametric study and multi-objective optimization in single-point incremental forming of extra deep drawing steel sheets:

The demand for incremental sheet forming is increasing specifically in the automotive sector for forming complex shapes with high accuracy. The present work investigates the incremental forming beh...

Suresh Kurra

Papers

Modeling and optimization of surface roughness in single point incremental forming process

Experimental and numerical studies on formability of extra-deep drawing steel in incremental sheet metal forming

Evaluation of surface roughness in incremental forming using image processing based methods

Parametric study and multi-objective optimization in single-point incremental forming of extra deep drawing steel sheets:

Processing of DP590 steel using single point incremental forming for automotive applications