The development of a finite elements based springback compensation tool for sheet metal products

Springback is a major problem in the deep drawing process. When the tools are released after the forming stage, the product springs back due to the action of internal stresses. In many cases the shape deviation is too large and springback compensation is needed: the tools of the deep drawing process are changed so, that the product becomes geometrically accurate after springback. In this paper, two different ways of geometric optimization are presented, the smooth displacement adjustment (SDA) method and the surface controlled overbending (SCO) method. Both methods use results from a finite elements deep drawing simulation for the optimization of the tool shape. The methods are demonstrated on an industrial product. The results are satisfactory, but it is shown that both methods still need to be improved and that the FE simulation needs to become more reliable to allow industrial application

Compensating springback in the automotive practice\ud using MASHAL

New materials are used in the automotive industry to reduce weight and to improve crash performance. These\ud materials feature a higher ratio of yield stress to elastic modulus leading to increased springback after tool release. The resulting\ud shape deviations and their efficient reduction is of major interest for the automotive industry nowadays. The usual strategies for\ud springback reduction can diminish springback to a certain amount only. In order to reduce the remaining shape deviation a\ud mathematical compensation algorithm is presented. The objective is to obtain the tool geometry such that the part springs back\ud into the right shape after releasing the tools.\ud In practice the process of compensation involves different tasks beginning with CAD construction of the part, planning the\ud drawing method and tool construction, FE-simulation, deep drawing at try-out stage and measurement of the manufactured part.\ud Thus the compensation can not be treated as an isolated task but as a process with various restrictions and requirements of\ud today’s automotive practice. For this reason a software prototype for compensation methods MASHAL – meaning program to\ud maintain accuracy (MASsHALtigkeit) – was developed. The basic idea of compensation with MASHAL is the transfer and\ud application of shape deviations between two different geometries on a third one. The developed algorithm allows for an effective\ud processing of these data, an approximation of springback and shape deviations and for a smooth extrapolation onto the tool\ud geometry.\ud Following topics are addressed: positioning of parts, global compensation and restriction of compensation to local areas,\ud damping of the compensation function in the blank holder domain, simulation and validation of springback and compensation of\ud CAD-data. The complete compensation procedure is illustrated on an industrial part

Springback Compensation: Fundamental Topics and Practical Application

Now that the simulation of deep drawing processes has become more reliable the virtual\ud compensation of the forming tools has become reality. In literature, the Displacement Adjustment (DA)\ud algorithm has proved to be most effective. In this article it is shown how the compensation factor, required for\ud (one-step) DA depends on material, process and geometrical parameters. For this an analytical bar stretchbending\ud model was used. A compensation factor is not required when DA is applied iteratively and the\ud products geometrical accuracy is improved further. This was demonstrated on an industrial part. The\ud compensation varies over the product, leading to a reduction in shape deviation of 90% and more, a result that\ud could not have been achieved with one-step compensatio

Time to Arrival Estimates, (Pedestrian) Gap Acceptance and the Size Arrival Effect

Various studies have found that road users’ acceptance of gaps to cross in front of another vehicle is dependent on the approaching vehicle’s size, with smaller accepted gaps in front of smaller vehicles. At the same time, the so called size arrival effect is well known from research on time to collision / time to arrival estimates, where larger objects / vehicles tend to be judged as arriving earlier than smaller objects / vehicles. However, so far there has been no attempt to connect these two approaches in a single experiment to investigate whether the size arrival effect that is prevalent in time to arrival estimates can explain the variations in gap acceptance. In this experiment, twenty-seven participants observed video clips of approaching virtual vehicles of varying size (truck, bus, van, two different cars and a motorcycle) from a pedestrian’s perspective, and were either required to indicate a crossing decision, or to estimate time to arrival. While, overall, the effect of vehicle size was clearly visible for both crossing decision and time to arrival estimates, there was also a clear exception in form of the motorcycle, which went with larger accepted gaps than some of the larger vehicles. This exception might be explained by the participants’ subjective rating of perceived threat, which was rather high for the motorcycle. As (with the exception of the motorcycle), vehicle size and perceived threat correlated substantially, it is unclear at this stage to what degree these two factors contribute to perceived time to arrival and crossing decisions

Northeast IPM Center Participation by the NYS IPM Program, 2006

NYS IPM Program staff were involved with several key activities of theNortheast IPM Center in 2006. Included were participation and leadership in the Center’s Working Groups and meeting with Natural Resource Conservation Service representatives

Curve Negotiation: Identifying Driver Behavior Around Curves with the Driver Performance Database

Approximately one quarter of all accidents outside city limits occur while driving around curves, where assistance systems could prevent the driver from negotiating curves with excessive speed. This study argues that the parameterizing of a Driving Assistant System could be realized with data from realistic, noncritical driving behavior offered by Naturalistic Driving Studies. The Driver Performance Database presented in this study provides a tool for observing normal, noncritical driving behavior. The Database contains results from road tests with an instrumented vehicle that were carried out on public road traffic on a predetermined route, which was precisely measured in advance. In addition to vehicle state parameters, we also collected data concerning the driving environment and physiological information. With the Driver Performance Database it is possible to generate different facets of human driving behavior in a descriptive and normative way, which is illustrated by driver behavior in curve negotiation

Curve Negotiation: Identifying Driver Behavior Around Curves with the Driver Performance Database

Approximately one quarter of all accidents outside city limits occur while driving around curves, where assistance systems could prevent the driver from negotiating curves with excessive speed. This study argues that the parameterizing of a Driving Assistant System could be realized with data from realistic, noncritical driving behavior offered by Naturalistic Driving Studies. The Driver Performance Database presented in this study provides a tool for observing normal, noncritical driving behavior. The Database contains results from road tests with an instrumented vehicle that were carried out on public road traffic on a predetermined route, which was precisely measured in advance. In addition to vehicle state parameters, we also collected data concerning the driving environment and physiological information. With the Driver Performance Database it is possible to generate different facets of human driving behavior in a descriptive and normative way, which is illustrated by driver behavior in curve negotiation