Profile monitoring can be effectively adopted to detect unnatural behaviors of machining processes, i.e., to signal when the functional relationship used to model the geometric feature monitored changes with time. Most of the literature concerned with profile monitoring deals with the issue of model identification for the functional relationship of interest, as well as with control charting of the model parameters. In this chapter, a different approach is presented for profile monitoring, with a focus on quality monitoring of geometric tolerances. This approach does not require an analytical model for the statistical description of profiles considered, and it does not involve a control charting method. An algorithm which allows a computer to automatically learn from data the relationship to represent profiles in space is described. The proposed algorithm is usually referred to as a neural network and the data set, from which the relationship is learned, consists just of profiles representative of the process in its in-control state. Throughout this chapter, a test case related to roundness profiles obtained by turning and described in Chapter 11 is used as a reference. A verification study on the efficacy of the neural network shows that this approach may outperform the usual control charting method.

In order to perform a successful sheet metal forming operation and to avoid shape deviations and tearing and wrinkling defects, process and material variables, such as tools geometry, blankholder force, friction, blank shape, sheet thickness and material properties, should be optimized. One of the main parameters which must be defined at the beginning of any sheet metal forming process design is the initial blank shape and its main dimensions [1]. In this paper, the authors’ attention is focused on a non-conventional sheet HDD process for which optimal blank shape and dimensions are not fully explored yet [2]. It will be demonstrated that, when the traditional One Step finite element method calculation, realised through the implicit code, is applied to HDD, the process shows various limits. In fact, while the One Step analysis is able to predict the optimal initial blank for the traditional Deep Drawing (DD) process, the same blank could not be the optimal one in a HDD process. The goal of this paper is to develop a methodology by which, with the aid of optimization algorithms, it will be possible for the user to define the best shape and dimensions for the initial blank in HDD processes, even when starting from the blank obtained by a One Step analysis.

Structural Optimization techniques are a well-known approach in order to improve Product Performances. Optimization procedures, many times, do not include manufacturing constraints arising from the Corporate technologies. This aspect becomes a disadvantage in the Design revue phase when the final Product release is a trade-off between the Optimization results and the Manufacturing constraints. This paper describes a specific new approach which considers product/process guidelines an input/output data in the optimization phase. The study case is represented by a high performances aeronautic seat structure having as mission profiles the SAE-AS Standards, in order to demonstrate occupant protection when a seat/occupant/restraint system is subjected to statically applied ultimate loads and to dynamic impact test conditions. The authors’ aim, in accordance to standards requirements, is to achieve a final design based on an optimized structural solution for the chosen process technologies taking into account the low volume production, typical attitude of the aeronautical industry. The presented study case offers the proper reference in order to extend this methodology to more complex structural applications.

The metal stamping is a forming process by plastic deformation of a metal surface carried by a punch in a die. During the process different deformation modes are possible. Thus, formability, the proneness of the material to be subjected to this kind of operation, it is very important for the process feasibility. If the blank is restrained between the die and the blank-holder, the metal stamping is said by stretching. In this case, the deformation status is of pure stretching. The Erichsen test is usually adopted to verify the material formability in this deformation conditions. In this test, the blank deformation is obtained thanks to a spherical punch. The friction in the contact of the sheet metal and the punch has influence in the deformations distribution. An alternative testing procedure for stretching conditions it is represented by the conventional bulging test. In this case, the deformation action is made by a pressurized fluid avoiding the effect of friction during the material deformation. This test condition better represents the deformation conditions when an unconventional process like direct hydroforming is adopted. In the present work authors have developed appropriate numerical models for the Erichsen test and, using the same tooling, except for the punch, a new bulging test has been developed. For the two different testing conditions the deformation status has been evaluated. The obtained results from the numerical analysis have confirmed a better formability in the case of the non conventional bulging test, with a more uniform distribution of the plastic deformation and of the thickness percentage reduction of the formed blank.

Springback is a really troublesome effect in sheet metal forming processes. In fact changes in geometry after springback are a big and costly problem in the automotive industry. In this paper the authors want to analyse the springback phenomenon experimentally in sheet metal hydroforming. Compared with conventional deep drawing, sheet hydroforming technology has many remarkable advantages, such as a higher drawing ratio, better surface quality, less springback, better dimensional freezing and capability to manufacture complicated shapes. The springback phenomenon has been extensively analysed in deep drawing processes but there are not many works in the literature about springback in sheet metal hydroforming. In order to study it, the authors have performed an accurate measuring phase on the chosen test cases through a coordinate measuring machine and the obtained measurements have been utilised for the determination of springback parameters, taking into account the method proposed by Makinouchi et al. The authors have focused their attention on the possibility of adopting a modified Makinouchi et al. approach in order to measure the springback of the large size considered test cases. Through the implemented methodology it has been possible to calculate the values of the springback parameters. The obtained results correspond to the observed experimental deformations. Analysing the springback parameter values of the different combinations investigated experimentally, the authors have also studied the pre-bulging influence on the springback amount.

The HDD process performance and the quality of the formed parts can be influenced by many process variables, such as: fluid pressure, blankholder force, pre-bulging pressure, friction and punch speed [1]. Many studies report how pre-bulging characterization may be done in accordance with the fluid pressure variation [2] or with the maximum bulge height [3][4] value. In this paper, the authors use the maximum bulge height to characterize pre-bulging levels influence on the process feasibility. The effects of three different pre-bulging levels as well as the traditional geometric and process parameters on a specific component characterized by an inverse drawn shape have been analyzed. In the experimental phase, for each considered process condition the Thickness Reduction (TR) has been detected on the formed component in sixteen different points of interest, to verify numerical and experimental correlation. The good accuracy shown by the numerical model allowed to develop an appropriate numerical campaign to obtain an ANOVA analysis to evaluate the pre-bulging heights influence on TR distribution. The obtained results have shown that the pre-bulging height influences the TR distribution significantly but in a less way than punch radius and blankholder forces profile.

Due to the widespread use of highly automated machine tools, manufacturing requires reliable models and methods for the prediction of output performance of machining processes. The prediction of optimal machining conditions for good surface finish and dimensional accuracy plays a very important role in process planning. The present work deals with the study and development of a surface roughness prediction model for machining Al7075-T6, using Response Surface Methodology (RSM). Machining operations of work pieces made by Al7075-T6 covering a wide range of machining conditions have been carried out with by flat end mill with four teeth made by High Speed Steel. A RS model, in terms of machining parameters, was developed for surface roughness prediction using the Radial Basis Functions (RBF) technique. This model gives the process response sensitivity to the individual process parameters. An attempt has also been made to optimize the surface roughness prediction model using Genetic Algorithms (GA).

Sheet hydroforming has gained increasing interest during the last years, especially as application in the manufacturing of some components for automotive, aerospace, and electrical appliances[1,2]. Many parameters influence the process of sheet hydroforming, one of them is the pre-bulging[3]. Different studies have been also done to determine the optimal forming parameters through FEA[4,5]. In the case of sheet hydromechanical forming process the blank is first placed on the lower die (a fluid chamber combined with draw ring) and then, after sealing the blank between blank holder and draw ring, punch progresses to deform the blank[6]. Pressure of the fluid chamber is also increased simultaneously with the punch progression[7]. In this paper, the pre-bulging effect on active hydromechanical deep drawing process has been investigated experimentally and numerically. Pre-bulging includes two parameters: pre-bulging height and pre-bulging pressure, which influence the forming process significantly[3]. Numerical simulations and experimental tests were carried out for a given shape to investigate the pre-bulging effect on the maximum hydroforming depth. During this activity, the authors have verified that the low numerical – experimental accuracy detected it was caused also by the simulation of the pre-bulging phase. The authors have analyzed the problem to define a correct procedure to simulate the pre-bulging phase. From this point of view, nine different levels of pre-bulging (taking into account the level equal to zero also) have been tested to experimentally calculate the Thickness Percentage Reduction (TPR) at the maximum pre-bulging height. For each level, the experiment has been conducted two times for a total number of eighteen experiments. The experimental TPR values have been compared with the numerical ones reaching a good accuracy only in the case of pre-bulging height greater than forty millimeters. The experimental activity has given a valid contribution to improve the simulation models reliability and to obtain useful information on the process itself. The effects of pre-bulging on the process performance are also discussed.

The increasing application of numerical simulation in metal forming field has helped engineers to solve problems one after another to manufacture a qualified formed product reducing the required time [1]. Accurate simulation results are fundamental for the tooling and the product designs. The wide application of numerical simulation is encouraging the development of highly accurate simulation procedures to meet industrial requirements. Many factors can influence the final simulation results and many studies have been carried out about materials [2], yield criteria [3] and plastic deformation [4,5], process parameters [6] and their optimization. In order to develop a reliable hydromechanical deep drawing (HDD) numerical model the authors have been worked out specific activities based on the evaluation of the effective stiffness of the blankholder structure [7]. In this paper after an appropriate tuning phase of the blankholder force distribution, the experimental activity has been taken into account to improve the accuracy of the numerical model. In the first phase, the effective capability of the blankholder structure to transfer the applied load given by hydraulic actuators to the blank has been explored. This phase ended with the definition of an appropriate subdivision of the blankholder active surface in order to take into account the effective pressure map obtained for the given loads configuration. In the second phase the numerical results obtained with the developed subdivision have been compared with the experimental data of the studied model. The numerical model has been then improved, finding the best solution for the blankholder force distribution.