A novel process for transforming sheet metal blanks: Ridged die forming

Up to 20% of all sheet metal produced is scrapped as blanking skeletons. A novel process is therefore designed and examined, aiming to transform tessellating 'pre-blanks' in-plane into the real blanks required for stamping. Prior to blanking, the sheet is formed with a set of ridged dies, from which pre-blanks are cut and then flattened into true blanks. Several different approaches to designing ridged dies are evaluated by simulation and experiment, and the best results demonstrate a potential reduction in blanking yield losses for can-making from 9.3% to 6.9%. © 2013 CIRP.

Sheet Metal Department

This is a view of a classroom in the Sheet Metal Department at the New York Trade School. Students are shown doing a variety of different tasks in the large room. Black and white photograph.

Nat Gold, Sheet Metal Graduate from the New York Trade School

Nat Gold graduated from the Sheet Metal program at the New York Trade School in 1942. He is represented here in the sheet metal shop he owns. Notice the blueprints for the White Plains Senior High School hanging on the wall behind him. Original caption reads, "Shop Owner, Brook Sheet Metal Inc. Nat Gold - Sheet Metal 1942, represents one of many Sheet Metal graduates who became owners of their own business." Black and white photograph.

Design and validation of a sheet metal shearing experimental procedure

Throughout the industrial processes of sheet metal manufacturing and refining, shear cutting is widely used for its speed and cost advantages over competing cutting methods. Industrial shears may include some force measurement possibilities, but the force is most likely influenced by friction losses between shear tool and the point of measurement, and are in general not showing the actual force applied to the sheet. Well defined shears and accurate measurements of force and shear tool position are important for understanding the influence of shear parameters. Accurate experimental data are also necessary for calibration of numerical shear models. Here, a dedicated laboratory set-up with well defined geometry and movement in the shear, and high measurability in terms of force and geometry is designed, built and verified. Parameters important to the shear process are studied with perturbation analysis techniques and requirements on input parameter accuracy are formulated to meet experimental output demands. Input parameters in shearing are mostly geometric parameters, but also material properties and contact conditions. Based on the accuracy requirements, a symmetric experiment with internal balancing of forces is constructed to avoid guides and corresponding friction losses. Finally, the experimental procedure is validated through shearing of a medium grade steel. With the obtained experimental set-up performance, force changes as result of changes in studied input parameters are distinguishable down to a level of 1%.

Using geometric shape variations to create an inverse model for a sheet metal process

The output of the sheet metal forming process is subject to much variation. This paper develops a method to measure shape variation in channel forming and relate this back to the corresponding process parameter levels of the manufacturing set-up to create an inverse model. The shape variation in the channels is measured using a modified form of the point distribution model (also known as the active shape model). This means that channels can be represented by a weighting vector of minimal linear dimension that contains all the shape variation information from the average formed channel.

The inverse models were created using classifiers that related the weighting vectors to the process parameter levels for the blank holder force (BHF), die radii (DR) and tool gap (TG) of the parameters. Several classifiers were tested: linear, quadratic Gaussian and artificial neural networks. The quadratic Gaussian classifiers were the most accurate and the most consistent type of classifier over all the parameters.

A shape error metric for sheet metal forming and its application to springback

There are many variations within sheet metal forming, some of which are manifest in the final geometry of the formed component. It is important that this geometric variation be quantified and measured for use in a process or quality control system. The contribution of this paper is to propose a novel way of measuring the geometric difference between the desired shape and an actual formed "U" channel. The metric is based upon measuring errors in terms of the significant manufacturing variations. The metric accords with the manually measured errors of the channel set. The shape error metric is then extended to develop a simple empirical, whole-component, springback error measure. The springback error measure combines into one value all the angle springback and side wall curl geometric errors for a single channel. Two trends were observed: combined springback decreases when the blank holder force is increased; and the combined springback marginally decreases when the die radii is increased.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

Contact pressure and wear in sheet metal forming - an FEM analysis

Wear is the principal cause of tool failure in most sheet metal forming processes. It is well known that the contact pressure between the blank and the tool has a large influence on the wear of the tool, and hence the tool life. This investigation utilises the finite element method to analyse the contact pressure distribution over the die radius for a particular deep drawing process. Furthermore, the evolution of the predicted contact pressure distribution throughout the entire stroke of the punch is also examined. It was found that the majority of the process shows a steady state pressure distribution, with two characteristic peaks over the die radius, at the beginning and end of the sheet contact area. Interestingly, the initial transient contact pressure response showed extremely high localised peak pressures; more than twice that of the steady state peaks. Results are compared to wear reported in the literature, during similar experimental deep drawing processes. Finally, the significance and effect of the results on wear and wear-testing techniques are discussed.

Modeling of contact pressure in sheet metal forming

For a given sheet metal forming process, an accurate determination of the contact pressure distribution experienced is an essential step towards the estimation of tool life. This investigation utilizes finite element (FE) analysis to determine the evolution and distribution of contact pressure over the die radius, throughout the duration of a channel forming process. It was found that a typical two-peak steady-state contact pressure response exists for the majority of the process. However, this was preceded by a transient  response, which produced extremely large and localized contact pressures. Notably, it was found that the peak transient contact pressure was more than double the steady-state peak. These contact pressure results may have a significant influence on the tool wear response and therefore impact current wear testing and prediction techniques. Hence, an investigation into the validity of the predicted contact pressure was conducted.

The influence of temperature on the forming behavior of metal/polymer laminates in sheet metal forming

The influence of temperature on the forming behavior of an aluminum/polypropylene/aluminum (APA) sandwich sheet was studied. Shear and tensile tests were performed to determine the mechanical properties of the laminate and the component materials as a function of process temperature. The forming limit diagram (FLD) of the laminate was established for two different temperatures, and its springback behavior was examined in four-point bend and channel bend tests. Cup forming tests were performed at various test temperatures to determine the limiting drawing ratio (LDR) and the tendency for wrinkling at these temperatures. Although there was only a minor influence of temperature on the mechanical properties and the FLD values of the laminate, the bend test results reveal that springback can be reduced by forming at higher temperature. The decreasing strength of the core material with rising process temperature led to an increased tendency of the laminate to wrinkle in the heated cup drawing tests.

The effect of clamping sequence on dimensional variability in sheet metal assembly

Finite element (FE) modelling techniques have become a popular tool for exploring welding and clamping sequence dependence in sheet metal assemblies. In the current paper, the dimensional variability associated with different assembly clamping sequences is investigated with a FE contact modelling approach implemented in the commercial code Abaqus. A simplified channel section assembly consisting of a top hat and bottom plate is the case study investigated. Expected variation modes of bow and twist were used to simulate key variability sources in the main structural component under investigation; the top hat of the channel section. It was found that final assembly variability can change considerably depending on clamp sequence selection. It was also found that different clamp sequences can control particular modes of variation better than others, and that there is not one particular clamping sequence that is the best for containing all variation modes. An adaptable assembly process is therefore suggested, where given the shape of input components the best available clamping sequence is selected. Comparison of the performance of the proposed adaptable clamping sequence to traditional fixed clamping sequences shows improvements for the dimensional control of variability in non-rigid components. While introducing such a method in production would require inspection of each component being assembled and investigation of the alternative clamping sequences, given access to fast and detailed dimensional inspection technology such as optical coordinate measuring machines (OCMM's), the approach shows promise for future application.

Knowledge based optimisation techniques for sheet metal forming die design

A knowledge based optimism technique has been developed to predict solutions for quality issues found in an initial draw die design. Post processing of the initial design yields all the features applying forces, and major quality issues. Using the geometric relationship between the two, a knowledge-base is interrogated to determine the possible corrective actions. These actions are then passed through a fast semi-analytical model to determine the level of change required. Results from a 2D forming are presented to highlight the advantage of the new algorithms over current optimisation techniques.