Additive manufacturing (AM) processes enable the production of functional parts with complex geometries, multi-materials, as well as individualized mass production. Another significant benefit of AM ...is the ability to produce optimized geometries with near perfect strength to weight ratios. Weight plays a crucial role in many functional parts such as parts used in automotive and aeronautic industries. Current topology optimization techniques, a powerful tool for weight reduction and product optimization in general, do not work well for such kind of process since AM methods necessitate applying own dedicated design rules. This paper reports a product/process optimization study of a simple test case geometry (C-Clip), where structural optimization has been applied using an innovative approach based on the design of lattice structure feasible thanks to additive process adoption. Moreover, it has been conducted a study to evaluate the possible advantages offered by the integration of the two previous approaches in order to verify the required design specifications. The aim of the work has been to evaluate the potentiality offered by the integration of the two structural optimization approaches (topological and lattice structures design) to obtain innovative and highly performing structures. This activity represents a necessary step for the definition and the subsequent development of a methodology aimed to the creation of structures obtained with this combined design approach. In order to define an objective evaluation of the component performances, appropriate Key Performance Indicators (KPI) have been developed. An engineering intelligence tool has been used to post process the generated optimization results for the three different approaches. Finally, the first three “best” structural solutions have been manufactured by 3D printing machine, with scaled dimensions, in order to evaluate the printing time considering the geometry complexity for the chosen structural layout in order to have useful feedbacks on Product/Process choices interaction.
Aluminum alloys foams with homogeneous and regular open cells have been frequently proposed and used as support structures for catalytic applications. In this kind of application, the quality of ...produced metal foam assumes primary importance. This paper presents an application of a classifier algorithm to predict quality in the manufacturing process of aluminum alloy foams with homogeneous and regular open cells. A data analysis methodology of experimental data, which is based on Binary Gaussian Process Classification, is presented. The proposed method is a Bayesian classification method, which gets away from any assumptions about the relationship between process inputs (the geometric design parameters of the regular unit cells) and process output (probability to obtain defective foam). We demonstrate that the proposed methodology can provide an effective tool to derive a model for the prediction of quality. An investment casting process, via 3D printing of wax patterns, is considered throughout the paper. Despite this specific case study, the methodology can be exploited in different processes in which the assumptions of traditional statistical approaches could not be easily verified, e.g., additive manufacturing.
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•Fundamental knowledge was collected concerning wet coating layer deposition on POCS.•POCS were effectively activated depositing a Pd/CeO2 catalyst by slurry coating.•High activities ...and stable performances in CO oxidation were achieved.•A gas-solid mass transfer correlation for cubic-cell POCS was developed.
Aluminum periodic open cellular structures (POCS) of cylindrical shape (9 mm diameter and 4−25 mm length) manufactured by investment casting were investigated as potential enhanced catalyst carriers. Samples with an ideal cubic cell geometry and different cell sizes - namely 1.5, 1.75 and 2.25 mm - were tested. First, coating experiments were performed aiming at understanding the wet coating deposition process: the influence operative spin coating parameters (i.e. spinning speed, time, acceleration) on final coating thickness was studied using glycerol solutions of different viscosity. Based on this preliminary investigation, POCS were activated by depositing a Pd/CeO2 catalyst by slurry coating techniques and tested under mass-transfer limited conditions in CO oxidation, used as a test reaction for gas/solid limited heterogeneous processes. A correlation for mass transfer performances of these structures in function of the Reynolds number is herein presented and used to compare these novel materials against conventional catalyst supports.
A metal foam represents a promising material since it keeps the high mechanical properties of the metal while reducing the weight up to 90%. Among several manufacturing processes, the investment ...casting is a foundry process flexible enough to be suitable both for stochastic and for regular foams. This paper presents an experimental determination of the manufacturing process of metal regular foams by investment casting. The goal is to derive experimentally an actual formability map. The use of logistic regression and response surface design is proposed as an effective tool for determining a statistical model of the metal foam casting process.
In this paper we propose a robust approach for solving the scheduling problem of parallel machines with sequence-dependent set-up costs. In the literature, several mathematical models and solution ...methods have been proposed to solve such scheduling problems, but most of which are based on the strong assumption that input data are known in a deterministic way. In this paper, a fuzzy mathematical programming model is formulated by taking into account the uncertainty in processing times to provide the optimal solution as a trade-off between total set-up cost and robustness in demand satisfaction. The proposed approach requires the solution of a non-linear mixed integer programming (NLMIP), that can be formulated as an equivalent mixed integer linear programming (MILP) model. The resulting MILP model in real applications could be intractable due to its NP-hardness. Therefore, we propose a solution method technique, based on the solution of an approximated model, whose dimension is remarkably reduced with respect to the original counterpart. Numerical experiments conducted on the basis of data taken from a real application show that the average deviation of the reduced model solution over the optimum is less than 1.5%.
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.
In order to produce products with constant quality, manufacturing systems need to be monitored for any unnatural deviations in the state of the process. Control charts have an important role in ...solving quality control problems; nevertheless, their effectiveness is strictly dependent on statistical assumptions that in real industrial applications are frequently violated. In contrast, neural networks can elaborate huge amounts of noisy data in real time, requiring no hypothesis on statistical distribution of monitored measurements. This important feature makes neural networks potential tools that can be used to improve data analysis in manufacturing quality control applications. In this paper, a neural network system, which is based on an unsupervised training phase, is presented for quality control. In particular, the adaptive resonance theory (ART) has been investigated in order to implement a model-free quality control system, which can be exploited for recognising changes in the state of a manufacturing process. The aim of this research is to analyse the performances of ART neural network under the assumption that predictable unnatural patterns are not available. To such aim, a simplified Fuzzy ART neural algorithm is firstly discussed, and then studied by means of extensive Monte Carlo simulation.
This paper presents a solution to the problem of manipulation control: target identification and grasping. The proposed controller is designed for a real platform in combination with a monocular ...vision system. The objective of the controller is to learn an optimal policy to reach and to grasp a spherical object of known size, randomly placed in the environment. In order to accomplish this, the task has been treated as a reinforcement problem, in which the controller learns by a trial and error approach the situation-action mapping. The optimal policy is found by using the Q-Learning algorithm, a model free reinforcement learning technique, that rewards actions that move the arm closer to the target.The vision system uses geometrical computation to simplify the segmentation of the moving target (a spherical object) and determines an estimate of the target parameters. To speed-up the learning time, the simulated knowledge has been ported on the real platform, an industrial robot manipulator PUMA 560. Experimental results demonstrate the effectiveness of the adaptive controller that does not require an explicit global target position using direct perception of the environment.
Consideration about the possibility to integrate vague uncertainty notions into numerical simulation modeling tools may be a very interesting research field. In this way, it will be possible to ...exploit more efficient and robust modeling evaluation tools in the study of high productivity and flexibility production systems. In literature, few works investigated on the possibility to cope with the lack of numerical models able to deal with ill-defined uncertainty. In particular, if it is possible to describe uncertainty by statistical distribution, the methods of classical discrete event simulation theory are able to model the considered system thoroughly and may be regarded as an exhaustive tool. Otherwise, if uncertainty cannot be described by statistical distribution, no robust methods tools are available to model and analyze discrete complex dynamic systems. In this work, the integration of Fuzzy Sets in discrete event simulation theory is analyzed. Firstly, uncertainty is considered from different point of views and all the necessary issues to introduce fuzziness in discrete event simulation models are illustrated. Then, a possibility-based approach is considered and fuzzy set theory concepts have been introduced in such a context. The soundness of simulation mechanisms has been formally established by considering the new questions arising from the description of system variables as fuzzy sets. Finally, the application of the proposed methodology to a simplified test case is showed and the obtained results are presented.
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.
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