Machines — Open Access Journal of Engineering

The advent of Industry 4.0 has brought to extremely powerful data collection possibilities. Despite this, the potential contained in databases is often partially exploited, especially focusing on the manufacturing field. There are several root causes of this paradox, but the crucial one is the absence of a well-established and standardized Industrial Big Data Analytics procedure, in particular for the application within the assembly systems. This work aims to develop a customized Knowledge Discovery in Databases (KDD) procedure for its application within the assembly department of Bosch VHIT S.p.A., active in the automotive industry. The work is focused on the data mining phase of the KDD process, where ARIMA method is used. Various applications to different lines of the assembly systems show the effectiveness of the customized KDD for the exploitation of production databases for the company, and for the spread of such a methodology to other companies too. Full article

After briefly recalling the main problems that arise in the study of globe valves for alternative pumps, a methodology has been set up in order to refine the design. The obtained method has the advantages of simplicity and independence from empirical diagrams. In summary, from the obtained equation, the suitable values of the parameters can be deduced, based on the assigned data (capacity Q₀ and number of rounds n) of all the dimensions of the valve or of the valves. Depending on the parameter values, it is possible to identify the most suitable kind of valve: a single dish-shaped valve, a ring valve, a valve with several rings or a group of valves. Full article

In this study, an assessment of degradation and failure modes in the gas-path components of twin-shaft industrial gas turbines (IGTs) has been carried out through a model-based analysis. Measurements from twin-shaft IGTs operated in the field and denoting reduction in engine performance attributed to compressor fouling conditions, hot-end blade turbine damage, and failure in the variable stator guide vane (VSGV) mechanism of the compressor have been considered for the analysis. The measurements were compared with simulated data from a thermodynamic model constructed in a Simulink environment, which predicts the physical parameters (pressure and temperature) across the different stations of the IGT. The model predicts engine health parameters, e.g., component efficiencies and flow capacities, which are not available in the engine field data. The results show that it is possible to simulate the change in physical parameters across the IGT during degradation and failure in the components by varying component efficiencies and flow capacities during IGT simulation. The results also demonstrate that the model can predict the measured field data attributed to failure in the gas-path components of twin-shaft IGTs. The estimated health parameters during degradation or failure in the gas-path components can assist the development of health-index prognostic methods for operational engine performance prediction. Full article

This paper applies micro textures to the rake face of PCBN (Polycrystalline Cubic Boron Nitride) tools, including three types of micro textures that are microgroove textures vertical to the cutting edge, microgroove textures parallel to the cutting edge, and microhole textures. In this paper, the effects of different cutting speeds on the surface quality of hardened bearing steel GCr15 by dry turning with non-texture PCBN tools and micro-texture PCBN tools are studied, and the surface roughness values obtained by different micro textures were compared and analyzed. The results showed that, compared to that of non-texture tools, the influence degree of the micro-texture tools on the machined surface roughness was different. The microhole texture and vertical microgroove texture were able to effectively reduce the surface roughness of the workpiece, and microhole texture had the best effective influence on surface roughness, but the parallel microgroove texture increased surface roughness. The influence of cutting speeds on surface roughness was different due to different types of micro textures. The influence of micro textures on surface roughness has huge potential for tool applications. Full article

The aim of the study is to review the topic of competences that will be present at smart factories. The study used bibliometric and topic analysis to achieve insight into new trends in Industry 4.0. Bibliometric analysis and topic mining was done on 43 peer-reviewed journal articles and conference papers, published before July 2018 in the Thomson Reuters’ Web of Science and Scopus databases, using the software tool Statistica Data Miner. Results are segmented into four sections: (1) personnel development in learning organizations, (2) training techniques for personnel, (3) future engineering profiles and engineering education, and (4) relational capabilities. Each section is thoroughly discussed in this paper. The study contributes to the pool of knowledge on Industry 4.0 phenomena by compiling competences needed at smart factories in the future. Full article

Laser machining processes are a new entrant and a rapidly evolving type of non-conventional machining process which allows the machining of complex geometries with high precision, surface quality and productivity in a wide range of materials. Thus, the need for creating a method has emerged that will help the laser machine operator to select the optimal process parameters. In this study an experimental investigation of the effect of the process parameters on the effectiveness of the laser engraving process was held. The examined process parameters were namely the average output power, the repetition rate, and the scanning speed. For this purpose 126 experimental samples, with various combinations of process parameters using a nanosecond Nd:YAG DMG MORI Lasertec 40 laser machine on a SAE 304 stainless steel plate were made. The measured criteria which evaluated the effectiveness of the process were the removed material layer thickness and the material removal rate. Full article

The employment of tools and techniques for monitoring and supervising the performance of industrial systems has become essential for enterprises that seek to be more competitive in today’s market. The main reason is the need for validating tasks that are executed by systems, such as industrial machines, which are involved in production processes. The early detection of malfunctions and/or improvable system values permits the anticipation to critical issues that may delay or even disallow productivity. Advances on Information and Communication Technologies (ICT)-based technologies allows the collection of data on system runtime. In fact, the data is not only collected but formatted and integrated in computer nodes. Then, the formatted data can be further processed and analyzed. This article focuses on the utilization of standard Key Performance Indicators (KPIs), which are a set of parameters that permit the evaluation of the performance of systems. More precisely, the presented research work demonstrates the implementation and visualization of a set of KPIs defined in the ISO 22400 standard-Automation systems and integration, for manufacturing operations management. The approach is validated within a discrete manufacturing web-based interface that is currently used for monitoring and controlling an assembly line at runtime. The selected ISO 22400 KPIs are described within an ontology, which the description is done according to the data models included in the KPI Markup Language (KPIML), which is an XML implementation developed by the Manufacturing Enterprise Solutions Association (MESA) international organization. Full article

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it. Full article

This study examined the control of a planar two-link robot arm. The control approach design was based on the dynamic model of the robot. The mathematical model of the system was nonlinear, and thus a feedback linearization control was first proposed to obtain a linear system for which a model predictive control (MPC) was developed. The MPC control parameters were obtained analytically by minimizing a cost function. In addition, a simulation study was done comparing the proposed MPC control approach, the linear quadratic (LQ) control based on the same feedback linearization, and a control approach proposed in the literature for the same problem. The results showed the efficiency of the proposed method. Full article

In this article, the information value is used in numeric analysis as both a method for data approximation and a measure of data equality among a set of values. To this end, a surface segmentation, based on a study for constructing a hierarchy for vectors clustering using certain similarity criteria, is presented. The technique is based on the analysis of vectors representing regions associated with given types of critical points. An approach based on the Max Entropy in Metric Space (MEMS) is introduced in the paper, in order to extract a cluster of local features and to obtain an analysis of mechanical systems in the 2D and/or 3D spaces. The approach proposed in the paper can be effectively used in virtual prototyping and optimal designing of mechanical systems. Full article

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems. Full article

This work presents a method to improve the diagnostic performance of empirical classification system (ECS), which is used to estimate the degradation state of components based on measured signals. The ECS is embedded in a homogenous continuous-time, finite-state semi-Markov model (HCTFSSMM), which adjusts diagnoses based on the past history of components. The combination gives rise to a homogeneous continuous-time finite-state hidden semi-Markov model (HCTFSHSMM). In an application involving the degradation of bearings in automotive machines, the proposed method is shown to be superior in classification performance compared to the single-stage ECS. Full article

Autonomously driving delivery robots are developed all around the world, and the first prototypes are tested already in last-mile deliveries of packages. Estonia plays a leading role in this field with its, start-up Starship Technologies, which operates not only in Estonia but also in foreign countries like Germany, Great Britain, and the United States of America (USA), where it seems to provide a promising solution of the last-mile problem. But the more and more frequent appearance of delivery robots in public traffic reveals shortcomings in the regulatory framework of the usage of these autonomous vehicles—despite the maturity of the underlying technology. The related regulatory questions are reaching from data protection over liability for torts performance to such mundane fields as traffic law, which a logistic service provider has to take into account. This paper analyses and further develops the regulatory framework of autonomous delivery robots for packages by highlighting legal implications. Since delivery robots can be understood as cyber-physical systems in the context of Industry 4.0, the research contributes to the related regulatory framework of Industry 4.0 in international terms. Finally, the paper discusses future perspectives and proposes specific modes of compliance. Full article

In this study, improved analytical models, numerical parametric explorations, and experimental characterization are presented for a mechanical inerter to bring out dependencies for dynamic mass amplification under low rates (<5 Hz) of excitation. Two common realizations of the inerter—the ball-screw and the rack-and-pinion versions—are considered. Theoretical models incorporating component inertias and sizing were developed for both versions. The dependence of the specific inertance on key design parameters is explored through simulations. Based on these simulations, a prototype rack-and-pinion inerter delivering a specific inertance above 90 was designed, fabricated, and tested under low-rate displacement and acceleration-controlled excitations. The measured specific inertance was found to display an exponential decline with an increase in excitation frequency for both cases. Deviations from predictions are attributable to the frequency dependence of internal stiffness and damping in the fabricated prototype. Using a phase-matching procedure for a representative lumped model, the internal stiffness and damping in the prototype were estimated. Examination of the phase spectra reveals an influence of the excitation frequency on the internal stiffness, damping, and consequently specific inertance. Further, based on the results of this study, design perspectives for such mechanical inerters, which are seeing increasing use in several low-frequency applications, are also presented. It is envisioned that this approach can be utilized to subsume the specific nonlinear characteristics of individual inerters into a simple yet unsimplistic model that can be used to more efficiently and accurately predict the behavior of multi-element, inerter-based systems that employ them. Full article

Typical rotating machinery drive trains are prone to torsional vibrations. Especially those drive trains that comprise one or more couplings which connect the multiple shafts. Since these vibrations rarely produce noise or vibration of the stationary frame, their presence is hardly noticeable. Moreover, unless an expensive torsional-related problem has become obvious, such drive trains are not instrumented with torsional vibration measurement equipment. Excessive levels can easily cause damage or even complete failure of the machine. So, when designing or retrofitting a machine, a comprehensive and detailed numerical torsional vibration analysis is crucial to avoid such problems. However, to accurately calculate the torsional modes, one has to account for the penetration effect of the shaft in the coupling hub, indicated by the shaft penetration factor, on the torsional stiffness calculation. Many guidelines and assumptions have been published for the stiffness calculation, however, its effect on the damping and the dynamic amplification factor are less known. In this paper, the effect of the shaft penetration factor, and hence coupling hub-to-shaft connection, on the dynamic torsional response of the system is determined by an experimental study. More specifically, the damping is of major interest. Accordingly, a novel academic test setup is developed in which several configurations, with each a different shaft penetration factor, are considered. Besides, different amplitude levels, along with both a sweep up and down excitation, are used to identify their effect on the torsional response. The measurement results show a significant influence of the shaft penetration factor on the system’s first torsional mode. By increasing the shaft penetration factor, and thus decreasing the hub-to-shaft interference, a clear eigenfrequency drop along with an equally noticeable damping increase, is witnessed. On the contrary, the influence of the sweep up versus down excitation is less pronounced. Full article

Mechanical fatigue testing of materials, prototype structures or sensors is often required prior to the deployment of these components in industrial applications. Such fatigue tests often requires the continuous long-term use of an appropriate loading machine, which can incur significant costs when outsourcing and can limit customization options. In this work, design and implementation of a low-cost small-scale machine capable of customizable fatigue experimentation on structural beams is presented. The design is thoroughly modeled using FEM software and compared to a sample experiment, demonstrating long-term endurance of the machine. This approach to fatigue testing is then evaluated against the typical cost of outsourcing in the UK, providing evidence that, for long-term testing of at least 373 h, a custom machine is the preferred option. Full article

The efficiency of cold rolling of steel strip in the main depends on the quality of technological lubricant and its cost. In this regard, it is important to develop new compositions of effective metalworking coolants that are low cost and provide maximum reduction in the friction coefficient. We developed and tested the new compositions of metalworking coolants on the basis of vegetable oil and chicken fat. The metalworking coolants were tested in Donbas State Engineering Academy (DSEA) on a laboratory rolling mill, 100 × 100, in cold rolling of 08Kp steel. The efficiency of the coolants was determined by the stretch ratio λ and the friction coefficient μ in the deformation zone, which was found by the forward slip method. We found the metalworking coolant with 100% concentration of boric acid esters of mono- and diglycerides is the most effective in the rolling of thin steel strips. Thus, the new metalworking coolants (MWC) on the basis of boric acid esters of mono- and diglycerides, synthesized on the basis of sunflower oil, can be recommended for use in the rolling of structural steels on account of its availability, high efficiency and low cost. Full article

This paper is based on the theoretical analysis designs of a dry cutting orthogonal test in order to study a phenomenon that the radial force is larger than the main cutting force when a PCBN (polycrystalline cubic boron nitride) tool hard turns GCr15. Finite element modelling and cutting tests show the cutting depth and the spindle speed having an impact on the main cutting force, the radial force, and the axial force. In this study, due to the shear function of the cutting process, the squeezing effect between the tool and the workpiece, and the metal softening effect of the workpiece material, the different cutting depth and the spindle speed bring about different cutting force changes, and also different spindle speeds have different effects on the three components of the total cutting force. The research result provides a basis for further study on dry turning of hardened bearing steel. Full article

Profile end-milling processes are very susceptible to vibrations caused by cutter runout especially when it comes to operations where the cutter diameter is ranging in few millimeters scale. At the same time, the cutting conditions that are chosen for the milling process have a complementary role on the excitation mechanisms that take place in the cutting area between the cutting tool and the workpiece. Consequently, the study of milling processes in the case that a cutter runout exists is of special interest. The subject of this paper is the experimental analysis of the effect of cutter runout on cutter vibration and, by extension, how this affects the chip removal and, thereby, the workpiece topomorphy. Based on cutting force measurements correlated with the workpiece topomorphy under various cutting process parameters, such as the cutting speed, feed rate, and the axial cutting depth, some useful results are extracted. Hence, the effect of vibration phenomena, caused by cutter runout, on the workpiece topomorphy in end milling can be evaluated. Full article