Papers by Author: Rosanna Di Lorenzo

Abstract: In sheet metal forming processes there is still a lack of knowledge in the field of environmental sustainability mainly due to the need of a proper modeling of issues and factors to be taken into account. Such topic is, nowadays, a urgent and remarkable issue in manufacturing and the main concerns are related to more efficient use of materials and energy. What is more, the estimation of environmental burdens of forming technologies is very complex to be accomplished since it is essentially process-dependent. This means that when comparing, for instance, a traditional forming process with an innovative one, there are some peculiar aspects to be considered; actually, processes can be rather different each other in terms of tooling, operative parameters and so on. In this paper, a first modeling effort is presented in order to compare, from a sustainability point of view, a classical stamping process with an incremental forming one. In particular, a frustum of pyramid part is considered and a quantitative analysis of the process energy consumption was developed. The paper aims to provide some sustainability guidelines to promote discussion on limitations, advantages, savings, drawbacks offered by different technologies within sheet metal forming field.

Abstract: In sheet metal forming most of the problems are multi objective problems, generally characterized by conflicting objectives. The definition of proper parameters aimed to prevent both wrinkles and fracture is a typical example of an optimization problem in sheet metal forming characterized by conflicting goals. What is more, nowadays, a great interest would be focused on the availability of a cluster of possible optimal solutions instead of a single one, particularly in an industrial environment. Thus, the design parameters calibration, accomplishing all the objectives, is difficult and sometimes unsuccessful. In order to overcome this drawback a multi-objectives optimization procedure based on Pareto optimal solution search techniques seems a very attractive approach to deal with sheet metal forming processes design. In this paper, an integration between numerical simulations, response surface methodology and Pareto optimal solution search techniques was applied in order to design a rectangular deep drawing process. In particular, the initial blank shape and the blank holder force history were optimized as design variables in order to accomplish two different objectives: reduce excessive thinning and avoid wrinkling occurrence. The steps of the optimization procedure include: 1) application of Central Composite Design (CCD) for the identification of the necessary data over the domain of variation of the design variables; 2) numerical simulations of the samples identified by CCD; 3) development of a response surface model to interpret the final objectives as functions of the design variables; 4) Pareto optimal solution analysis to reach the most performing design variables. The final aim is to develop a predictive tool able to identify a sort of process window for the analyzed process also minimizing the computational effort in particular with respect to mono-objective optimization techniques or traditional trial and error methods. Many possible technological scenarios were investigated by the implemented procedure and a set of reliable solutions, i.e. able to satisfy different design requirements, were obtained.

Abstract: One of the main issues in sheet metal forming operations design is the determination of formability limits in order to prevent necking and fracture. In fact, the ability to predict fracture represents a powerful tool to improve the production quality in mechanical industry. Many researchers investigated the problem here addressed, mainly studying forming limit diagrams (FLD) or developing fracture criteria which are able to foresee fracture defects for different processes. In this paper, the author present some early results of a research project focused on the application of artificial intelligence (AI) for ductile fracture prediction in sheet metal forming operations. The main advantage of the application of AI tools and in particular, of artificial neural networks (ANN), is the possibility to obtain a predictive tool with a wide applicability. The prediction results obtained in this paper fully demonstrate the usefulness of the proposed approach.