A general framework for crankshaft balancing and counterweight design

In the automotive field, the requirements in terms of carbon emissions and improved efficiency are shifting the focus of designers towards reduced engine size. As a result, the dynamic balancing of an engine with strict limitations on the number of cylinders, the weight and the available space becomes a challenging task. The present contribution aims at providing the designer with a tool capable of selecting fundamental parameters needed to correctly balance an internal combustion engine, including the masses and geometry of the elements to be added directly onto the crankshaft and onto the balancing shafts. The relevant elements that distinguish the tool from others already proposed are two. The first is the comprehensive matrix formulation which makes the tool fit for a wide variety of engine configurations. The second is an optimisation procedure that selects not only the position of the mass and centre of gravity of the counterweight but also its complete geometric configuration, thus instantaneously identifying the overall dimensions and weight of the crankshaft

Modelling Strategy and Parametric Study of Metal Gaskets for Automotive Applications

This paper is focused on finite element simulation of cylinder head gaskets. Finite element codes support several methodologies, each of which has its own strengths and weaknesses. One of the key points lies in the influence of the gasket geometry on its final behaviour. Such a contribution can come from the detailed modelling of the gasket or by defining a global non-linear behaviour in which material and geometry non-linearities are summarised. Two approaches were used to simulate the gasket behaviour. The first one consists in using a 2D approach, which allows to model through-thickness non-linear behaviour of gasket. The second one consists in using conventional 3D finite element modelling. The numerical methods have been discussed and compared in relation to the accordance with experimental data, amount of information supplied and computational time required. Finally, a parametric study shows how some geometric parameters influence the compressive load and the elastic recovery of a single-layer steel gasket

Deep Cryogenic treatment: a bibliographic review

The use of cryogenic treatment (CT) to improve mechanical properties of materials has been developed from the end of the Sixties. At the present time, the initial mistrust about CT has been cleared up and many papers about different materials reporting laboratory tests results, microstructural investigations and hypothesis on CT strengthening mechanisms have been published. The removal of retained austenite combined with fine dispersed η-carbides precipitation have been widely observed and their effects on mechanical properties have been measured. In addition, some recent studies have pointed out a different mechanism for fatigue strengthening of stainless steels, which involves nano-martensite formation during the CT. The present paper summarizes the state of art about CT, focusing on methods, parameters, results and assumed microstructural mechanisms, in order to get a starting point for new researches to come

Bearing Health Monitoring Based on the Orthogonal Empirical Mode Decomposition

Bearing is a crucial component of industrial equipment, since any fault occurring in this system usually affects the functionality of the whole machine. To manage this problem, some currently available technologies enable the remote prognosis and diagnosis of bearings, before that faults compromise the system function and safety, respectively. A system for the in-service monitoring of bearing, to detect any inner fault or damage of components and material, allows preventing undesired machine stops. Moreover, it even helps in performing an out-monitoring action, aimed at revealing any anomalous behaviour of the system hosting bearings, through their dynamic response. The in-monitoring can be based on the vibration signal measurement and exploited to detect the presence of defects in material. In this paper, the orthogonal empirical mode decomposition is analysed and tested to investigate how it could be effectively exploited in a lean in-service monitoring operation and remote diagnosis. The proposed approach is validated on a test rig, where an elementary power transmission line was set up. The activity highlights some main properties and practical issues of the technological implementation, as well as the precision of the Orthogonal Empirical Mode Decomposition, as a compact approach for an effective detection of bearing faults in operation

Fatigue Damage Estimation from Random Vibration Testing: Application to a notched specimen

Vibrations are random in a wide range of applications and they are the main cause of mechanical failure. To prevent such failure, it is necessary to evaluate the fatigue life using test or analysis techniques. For computing the severity of the damage many methods are available in literature, but the estimation damage is just an approximation. The objective of this study is to propose a numerical model, together with experimental validation, in order to estimate fatigue damage caused by random vibrations in metallic materials undergoing uniaxial fatigue testing

Deep transfer learning for machine diagnosis: From sound and music recognition to bearing fault detection

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

Envelope analysis applied to non-Hertzian contact simulations in damaged roller bearings

In the latest years many researcher focused on the possibility to foresee the failure of a mechanical system in the early stages in order to allow quick response times. Monitoring and diagnostics are at the base of those methodologies of predictive maintenance, which represents the standard for companies. Data acquired by monitoring systems are sometimes not sufficient to perform an effective diagnosis and to detect failures. In the present work the possibility of a defining a relation between the response of a system and the dimension of a defect causing the vibration is explored. Through a non-Hertzian contact model a roller bearing is studied and a correlation is sought between the size of the defect and the frequency content of the contact pressure time history. Resorting to a non-Hertzian approach enables the determination with good accuracy of the overpressures due to edge effects caused by the sudden change in curvature in presence of a defect. The estimation of the pressure variation can be used to estimate the amplitude of the harmonic content exciting the bearing during operation. Using algorithms for the signal analysis already assessed in the literature, in particular the envelope method, an in-depth analysis of the harmonic content of the signal to consider is possible. The possibility of building a correlation between the load and the size of the defect which might help to identify the dimension of a damage from the estimated frequency content. It is then possible to identify the presence and nature of the defect, allowing an early diagnosis of the failure

Health indicators construction for damage level assessment in bearing diagnostics: A proposal of an energetic approach based on envelope analysis

Predictive maintenance strategies are established in the industrial context on account of their benefits in terms of costs abatement and machine failures reduction. Among the available techniques, vibration-based condition monitoring (VBCM) has notably been applied in many bearing fault detection problems. The health indicators construction is a central issue for VBCM, since these features provide the necessary information to assess the current machine condition. However, the relation between vibration data and its sources intimately related to bearing damage is not effortlessly definable from a diagnostic perspective. This study discloses a diagnostic investigation performed both on the vibration signal and on the contact pressure signal that is supposed to be one of main forcing terms in the dynamic equilibrium of the damaged bearing. Envelope analysis and spectral kurtosis (SK) are applied to extract and compare diagnostic features from both signals, referring to the Case Western Reserve University (CWRU) case-study. Namely, health indicators are constructed by means of physical considerations based on the effect of faults on the signal power contents. These indicators show to be promising not only for damage detection but, also, for damage severity assessment. Moreover, they provide an invaluable reading key of the link occurring between the contact pressure path and the vibration response

Physical characterization of colorectal cancer spheroids and evaluation of NK cell Infiltration through a flow-based analysis

To improve pathogenetic studies in cancer development and reliable preclinical testing of anti-cancer treatments, three-dimensional (3D) cultures, including spheroids, have been widely recognized as more physiologically relevant in vitro models of in vivo tumor behavior. Currently, the generation of uniformly sized spheroids is still challenging: different 3D cell culture methods produce heterogeneous populations in dimensions and morphology, that may strongly influence readouts reliability correlated to tumor growth rate or antitumor natural killer (NK) cell-mediated cytotoxicity. In this context, an increasing consensus claims the integration of microfluidic technologies within 3D cell culture, as the physical characterization of tumor spheroids is unavoidably demanded to standardize protocols and assays for in vitro testing. In this paper, we employed a flow-based method specifically conceived to measure weight, size and focused onto mass density values of tumor spheroids. These measurements are combined with confocal and digital imaging of such samples. We tested the spheroids of four colorectal cancer (CRC) cell lines that exhibit statistically relevant differences in their physical characteristics, even though starting from the same cell seeding density. These variations are seemingly cell line-dependent and associated with the number of growing cells and the degree of spheroid compaction as well, supported by different adenosine-triphosphate contents. We also showed that this technology can estimate the NK cell killing efficacy by measuring the weight loss and diameter shrinkage of tumor spheroids, alongside with the commonly used cell viability in vitro test. As the activity of NK cells relies on their infiltration rate, the in vitro sensitivity of CRC spheroids proved to be exposure time- and cell line-dependent with direct correlation to the cell viability reduction. All these functional aspects can be measured by the system and are documented by digital image analysis. In conclusion, this flow-based method potentially paves the way towards standardization of 3D cell cultures and its early adoption in cancer research to test antitumor immune response and set up new immunotherapy strategies