R. Di Lorenzo

Influence of mechanical properties of the sheet material on formability in single point incremental forming

Abstract New trends in sheet metal forming are rapidly developing and several new forming processes have been proposed to accomplish the goals of flexibility and cost reduction. Among them single point incremental forming operations, in which the final shape of the component is obtained by the relative movement of a simple and small punch with respect to the blank, appear quite promising. In the paper, material formability issues in incremental forming were studied. Some relevant correlations among material formability and other mechanical properties of the material were analysed. The FLD 0 value, i.e. the major strain at fracture in plane strain conditions, was determined for different materials and the influence of the main material parameters on formability was accurately investigated through a statistical analysis.

Optimal Blankholder Force Path in Sheet Metal Forming Processes: An Al Based Procedure

Abstract Blankholder force plays a fundamental role in the deep drawing process mechanics since it controls, by friction, the material flow into the die cavity. The availability of computer controlled hydraulic presses in the industries promoted a new research field focused on the definition of optimal BHF histories, function of the punch displacement; such studies were aimed to the determination of the so called “process window”, i.e. the BHF path which permits to obtain the maximum height sound component avoiding both wrinkling and tearing. In the paper a design procedure is proposed in order to determine the optimal BHF path in an axisymmetric deep drawing process: in particular, a closed-loop control system based on the fuzzy reasoning has been set up and interfaced with a FEM code. The determined BHF path has been experimentally verified assessing the effectiveness of the proposed approach.

Optimal design of tube hydroforming processes: a fuzzy-logic-based approach

Abstract In recent years, tube hydroforming has become an economic and industrially suitable alternative to various traditional stamping processes, in particular for small batch production. In the present paper, an artificial intelligence system based on fuzzy logic was implemented for tube hydroforming process design. The aim was to achieve a process design procedure able to prevent forming defects and guarantee the achievement of the desired final shape of the component. In particular, the process design concerns the internal pressure history and the axial feeding. The fuzzy system is able to provide optimal trajectories for both the controlled parameters, producing a defect-free final part.

Intelligent computation techniques for process planning of cold forging

In this paper, two artificial intelligence (AI) techniques were applied to the problem of process planning in multiple-blow cold forging. Given the reduction in area of the product to be forged and the degree of formability of the material, in the first application a fuzzy logic (FL) technique was used to discriminate whether or not a cold forged product was feasible in a single blow. In the second application, a neural network (NN) architecture was used to identify the correct number of blows necessary to complete the cold forging process.

Process parameters calibration in 3D tube hydroforming processes

In tube hydroforming the concurrent actions of pressurized fluid and mechanical feeding allow to obtain tube shapes characterized by complex geometries such as different diameters sections and/or bulged zones. What is crucial in such processes is the proper design of operative parameters aimed to avoid defects (for instance shape defects or ductile fractures). The main process parameters are material feeding history (i.e. the punches velocity history) and internal pressure path during the process. In more complex three dimensional processes, also the action of a counterpunch is generally useful to reduce thinning in particular in expansion zones of the tube (i.e. T or Y shaped tubes). The good calibration of these parameters allows the optimal design of the process; in fact many researches have proposed different approaches to the optimization of these parameters. Generally, the main goals in the optimization approaches concern the control of thinning and the reaching of the desired final shape. In this p...

An integrated approach to the design of tube hydroforming processes: artificial intelligence, numerical analysis and experimental investigation

In the last years, the growing role of process flexibility in modern mechanical industries has driven a rising interest in optimisation of process/product design through innovative techniques. Moreover, the development of niche productions, which are characterised by low production volumes and small batches leads to the need of more flexible and rapid forming technologies. In this way, a great research effort is performed towards the study of new stamping processes: among them hydro forming finds a large interest in automotive industry since it allows to significantly reduce tooling costs and also to avoid some secondary operations. Different studies are available in the technical literature concerning the fundamentals of tube hydro forming processes as well as the industrial application of such operations. As process design issues are concerned, in the paper, the authors propose the integration of different tools, namely artificial intelligence techniques, numerical simulations and experimental knowledge...

Scheduling a cellular manufacturing system with GA

The flexible manufacturing cell scheduling problem is considered with a multi-objective approach, pursuing together makespan minimisation and the in process job wait minimisation. The formulation of the scheduling problem is discussed, analysing how to generate well suited sequences, like generalised permutation sequences, and the proper construction of a JIT timing of activities. An evolutionary sequencing algorithm based on both classic genetic operators and hybrid operators is then proposed. The hybrid operators have been introduced to construct highly fit initial population, to perform periodically a local search on the population and to maintain enough genetical diversity in the actual population. Simulation runs on a large number of randomly generated problems, showed the high performance of the proposed evolutionary hybrid algorithm, in front of a modified NEH algorithm, in the determination of schedules minimising makespan and in process job wait together.

An Inverse Approach for the Design of the Optimal Preform Shape in Cold Forging

Abstract In closed die forging the preform design plays a critical role for the success of the process: in fact it must ensure that in the finishing step the desired product is obtained without shape defects such as underfilling or folding and with a minimum material loss into the flash. In the paper an inverse approach is applied to the preform shape optimization problem: the method permits to evaluate a response function which links the set of parameters defining the preform shape with the fulfillment of the product design specifications. The proposed approach has been applied to a closed die forging process aimed to the production of a C-shape component, and has allowed to determine the optimal preform geometry which ensures the complete filling of the die cavity.

Comparison of analytical methods and AI tools for material characterisation in hot forming

Abstract Hot forming processes probably represent the most ancient of forming operations and what is more they are still today commonly used in modern mechanical industry in order to obtain sound parts, achieving large deformations with a limited required power. Hot metal forming operations are characterised by a large number of physical and thermal phenomena which have to be taken into account in order to model and design the processes themselves. Actually several thermally activated phenomena occur during the forming processes such as recovery, recrystallisation, grain growth, precipitation, allotropic transformations, etc. In this paper the comparison between an analytical method based on the Gauss–Newton algorithm and the genetic algorithms (GAs) is proposed with the aim of characterising material behaviour in hot forming operations. Such approaches were utilised in order to determine the coefficients of one of the most effective equation utilised for material characterisation, namely the equation proposed by Beynon.

Design of a fuzzy controller for the deep drawing process by using GAs

Presents a genetic algorithm based approach to design a fuzzy control system for the deep drawing process. A fuzzy controller has been built up based on additive fuzzy set theory. Such a system has proved its ability to cope with the uncertainties characterising process conditions in the deep drawing operation. The knowledge base necessary to train the fuzzy controller has been obtained by finite element (FE) simulations of the process. Finally, the designed controller response has been tested proving its effectiveness.