Additive Manufacturing (AM) processes enable their deployment in broad applications from aerospace to art, design, and architecture. Part quality and performance are the main concerns during AM ...processes execution that the achievement of adequate characteristics can be guaranteed, considering a wide range of influencing factors, such as process parameters, material, environment, measurement, and operators training. Investigating the effects of not only the influential AM processes variables but also their interactions and coupled impacts are essential to process optimization which requires huge efforts to be made. Therefore, numerical simulation can be an effective tool that facilities the evaluation of the AM processes principles. Selective Laser Melting (SLM) is a widespread Powder Bed Fusion (PBF) AM process that due to its superior advantages, such as capability to print complex and highly customized components, which leads to an increasing attention paid by industries and academia. Temperature distribution and melt pool dynamics have paramount importance to be well simulated and correlated by part quality in terms of surface finish, induced residual stress and microstructure evolution during SLM. Summarizing numerical simulations of SLM in this survey is pointed out as one important research perspective as well as exploring the contribution of adopted approaches and practices. This review survey has been organized to give an overview of AM processes such as extrusion, photopolymerization, material jetting, laminated object manufacturing, and powder bed fusion. And in particular is targeted to discuss the conducted numerical simulation of SLM to illustrate a uniform picture of existing nonproprietary approaches to predict the heat transfer, melt pool behavior, microstructure and residual stresses analysis.
Maintenance scheduling is a fundamental element in industry, where excessive downtime can lead to considerable economic losses. Active monitoring systems of various components are ever more used, and ...rolling bearings can be identified as one of the primary causes of failure on production lines. Vibration signals extracted from bearings are affected by noise, which can make their nature unclear and the extraction and classification of features difficult. In recent years, the use of the discrete wavelet transform for denoising has been increasing, but studies in the literature that optimise all the parameters used in this process are lacking. In the current article, the authors present an algorithm to optimise the parameters required for denoising based on the discrete wavelet transform and thresholding. One-hundred sixty different configurations of the mother wavelet, threshold evaluation method, and threshold function are compared on the Case Western Reserve University database to obtain the best combination for bearing damage identification with an iterative method and are evaluated with tradeoff and kurtosis. The analysis results show that the best combination of parameters for denoising is dmey, rigrSURE, and the hard threshold. The signals were then distributed in a 2D plane for classification through an algorithm based on principal component analysis, which uses a preselection of features extracted in the time domain.
Vibration of electrodes in operation strongly affects the efficiency of the electric arc furnace (EAF) fed by ac current during the steel smelting process. Therefore, an effective control of the ...structural dynamics through an active system is a current goal of the "intelligent manufacturing" approach. A vertical position control applied to each electrode allows keeping the arc length almost constant and reduces the effects of some electromechanical actions due to the mutual magnetic induction among the three electric phases. Nevertheless, control action needs for a detailed model of the whole system dynamic behavior. A new method for modeling the equipment behavior and somehow the process was implemented. A key issue was including into the model all the electromechanical coupling effects occurring in this system and suitably linking to the structural dynamics. Modeling activity was performed by resorting to the multibody dynamics and the finite-element method, while some analytical formulations were used to describe both the electric arc behavior and the control. A preliminary validation on a real plant was performed as far as the huge size of the system allowed and an assessment of the mechanical design of the EAF was completed.
Today’s deep learning strategies require ever-increasing computational efforts and demand for very large amounts of labelled data. Providing such expensive resources for machine diagnosis is highly ...challenging. Transfer learning recently emerged as a valuable approach to address these issues. Thus, the knowledge learned by deep architectures in different scenarios can be reused for the purpose of machine diagnosis, minimizing data collecting efforts. Existing research provides evidence that networks pre-trained for image recognition can classify machine vibrations in the time-frequency domain by means of transfer learning. So far, however, there has been little discussion about the potentials included in networks pre-trained for sound recognition, which are inherently suited for time-frequency tasks. This work argues that deep architectures trained for music recognition and sound detection can perform machine diagnosis. The YAMNet convolutional network was designed to serve extremely efficient mobile applications for sound detection, and it was originally trained on millions of data extracted from YouTube clips. That framework is employed to detect bearing faults for the CWRU dataset. It is shown that transferring knowledge from sound and music recognition to bearing fault detection is successful. The maximum accuracy is achieved using a few hundred data for fine-tuning the fault diagnosis model.
Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models ...behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models’ outcomes.
The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a ...novel approach for generating synthetic data using a Generative Adversarial Network (GAN) with cycle consistency loss function known as cycleGAN. The proposed method aims to generate synthetic data that could effectively replace real experimental data. The generative model is trained to transform wavelet images of simulated vibrational signals into authentic data obtained from machinery with damaged bearings. The utilization of Maximum Mean Discrepancy (MMD) and Fréchet Inception Distance (FID) demonstrates a noteworthy resemblance between synthetic and real experimental data. Also, the generative model enables the synthesis of data that may have been entirely lacking from the experimental observation, indicating generative zero-shot learning capabilities. The efficacy of synthetic data in training diagnosis algorithms by means of Transfer Learning (TL) on Convolutional Neural Networks (CNNs) has been demonstrated to be comparable to that of real data. The study has been validated by means of the test rig for medium-sized industrial bearings accessible at the Politecnico di Torino.
The aerospace industry is one of the leading figures in the development and improvement of techniques for the design of new products. One of the most promising developments of the last decades is the ...exploitation of digital models that make it possible to evaluate design solutions and simulate the behavior of the individual systems and their interactions. The goal is to be able to predict and analyze all aspects an aircraft much in advance of its industrialization in order to heavily reduce the time and costs of product development and to guarantee flexibility to test a multitude of solutions. The main issue in this context is the complexity of creating models that are capable of accurately sizing and simulating multiple interacting systems, thus considering the constraints imposed by the need for their mutual compatibility. The present contribution introduces two interconnected models regarding an aircraft system, in particular, the landing gear, that make it possible to size its main components and subsystems and to use the found parameters to populate a dynamic model that simulates the behavior of the aircraft during landing. These models provide a preliminary digitalization of the system itself and of the design process as well, thereby making it possible to define a potential configuration and to test it in a dynamic virtual environment, thus taking into account the interaction between the individual subsystems. The model was tested through three use cases, differentiated by class and scope, which made it possible to compare and validate the obtained results with actual values.
Mechanical vibrational energy, which is provided by continuous or discontinuous motion, is an infinite source of energy that may be found anywhere. This source may be utilized to generate electricity ...to replenish batteries or directly power electrical equipment thanks to energy harvesters. The new gadgets are based on the utilization of piezoelectric materials, which can transform vibrating mechanical energy into useable electrical energy owing to their intrinsic qualities. The purpose of this article is to highlight developments in three independent but closely connected multidisciplinary domains, starting with the piezoelectric materials and related manufacturing technologies related to the structure and specific application; the paper presents the state of the art of materials that possess the piezoelectric property, from classic inorganics such as PZT to lead-free materials, including biodegradable and biocompatible materials. The second domain is the choice of harvester structure, which allows the piezoelectric material to flex or deform while retaining mechanical dependability. Finally, developments in the design of electrical interface circuits for readout and storage of electrical energy given by piezoelectric to improve charge management efficiency are discussed.
A two-step optimization for crankshaft counterweights Brusa, Eugenio; Dagna, Alberto; Delprete, Cristiana ...
Engineering science and technology, an international journal,
March 2024, 2024-03-00, 2024-03-01, Letnik:
51
Journal Article
Recenzirano
Odprti dostop
The optimization of industrial products and processes has been, since the beginning of the third industrial revolution, a fundamental aspect of the design phase as it allows, together with the ...testing and validation phases, the improvement of the performance or efficiency of what has been designed. Optimization is also applied to the counterweights of the crankshafts that ensure the balancing of the forces and moments generated by internal combustion engines during their regular use. Traditionally, this process is carried out, at the preliminary stage, in an analytical way, using specific formulas for each engine configuration. This approach, however, allows the identification of only the macro-parameters of the counterweight, i.e. mass and position of the center of gravity, leaving the designer the translation into technical drawing of the result of the optimization. Thanks to the increase in computational power obtained in recent decades and the interconnection of systems, typical of Industry 4.0, this work intends to propose a new methodology for optimizing counterweights, based on a two step approach, able to identify the best solution not only in terms of macro-parameters, but also of the specific dimensional parameters of the counterweight.
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