Behavior Of Machine Research Articles

Simulation of machining behaviour of two-phase brittle materials during grinding by modelling single-grain scratching using a combination of FE and SPH methods

Two-phase, brittle-hard materials are widely used not only in the tool industry but also increasingly in the aerospace industry. Due to the two-phase nature, the materials have unique material properties adapted to the respective application. But the material properties also lead to challenging machinability. Therefore, two-phase, brittle-hard materials are mostly ground. The analogy process of single-grain scratching is used to analyse the material removal behaviour and design the grinding process. Since single-grain scratching is time-consuming and costly, it is desirable to substitute the analogy process with numerical simulation. This paper discusses the suitability of the Smooth Particle Hydrodynamic (SPH) simulation method in combination with the Finite Element Method (FEM) for single-grain scratching of two-phase, brittle-hard materials. The approach is validated using the examples of tungsten carbide-cobalt (WC-Co) cemented carbides and Silicon carbide fibre-reinforced silicon carbide (SiC/SiC) ceramics. For both applications, the material removal behaviour was optically analysed and in good agreement with the experimental results and theoretical assumptions. For SiC/SiC ceramics, several surface phenomena could be identified in the simulation as well as in the experimental results. The scratching forces were compared qualitatively and were in good agreement with the experimental results for both applications.

Hybrid modelling method for the prediction and experimental validation of 3D printing resource consumption

In this study, a hybrid modelling scheme for 3D Printing (3DP) technology is proposed for predicting three resource consumption processes: production time, electric energy and material usage. First, the manufacturing process is classified into five machine operations: axial moving, material processing, unit heating, material feeding and auxiliary. Then, according to the machine behaviours, a Gantt chart and power profile (GP) diagram is generated for understanding operation processes and power variations. Based on the GP diagram, physical models and data-driven models are created for simulating the resource consumption level of each operation. The time and material consumed during part printing are physically modelled based on a computer numerical control (CNC) language: G-code. The power of each operation and the time consumed while preheating machine units are modelled through experimental measurements under different related process parameter values. The collected experimental data are regressed to obtain the functional relationships between the power, preheating time and process parameters. With the time and power submodels for all operations, the resource consumption models for the 3DP machine are assembled. To verify the effectiveness of this method, hybrid prediction modelling is demonstrated for two fused deposition modelling (FDM) machines, and the prediction accuracies are tested for real printing tasks. The proposed method can be adopted to other additive manufacturing processes by comprehensively considering related factors, including part geometries, process parameters, G-codes and machine behaviours.

Real-time energy flow mapping: A VSM-based proposal for energy efficiency

Rising energy costs and the current concern about sustainability issues directly impact the competitiveness and survival of organizations, promoting the search for continuous improvement in industrial production processes. The present study, by identifying the absence in the literature of models based on Value Stream Mapping and integrated with Industry 4.0 technologies that promote better energy efficiency practices in industrial processes, proposes a new Lean Management model called Real-time Energy Flow Mapping. This innovative model, provide capabilities to a visual, digital and sustainable management system of all energy behavior in machines and equipment using fully parameterizable metrics and rules. Also, estimulating stakeholders with the ability to predict and monitor energy costs, as well as identify possible problems due to changes in the behavior of energy consumption in machines, thus helping managers to make decisions faster and more dynamically. As a result, the literature search turned up 17 Value Stream Mapping based models with a focus on energy efficiency, along with 31 metrics grouped on energy, environmental and production processes. After discussions by the Focus Groups, 16 resulting metrics were submitted to expert panel, in which the least relevant were discarded and the 13 most relevant classified using Fuzzy Delphi. An analysis to verify the interrelationships between the metrics is performed using the Fuzzy DEMATEL, being finally applied in the proposed model. A case study of the model was carried out, submitting it operationally at the LabFaber 4.0, empirically validating the study. The Real Time Energy Flow Map fills the gap in the literature on Value Stream Mapping models to improve energy efficiency, monitoring energy consumption through dynamic mapping of the energy flow of production processes, identifying the area with increased potential for energy savings through changes in operating behavior that reduce overall energy consumption and environmental impacts.

Thermal Evaluation of a Short-Operating-Duty Dual-Lane Fault-Tolerant Actuator for Aerospace Applications

This paper presents design considerations for a short-operating-duty, fault-tolerant actuator for aerospace applications, with the research focus placed on thermal management. A fully enclosed and naturally ventilated permanent magnet (PM) synchronous machine with a dual-lane modular stator-winding topology is analysed. The aim is to assess alternative solutions for satisfying both sufficient heat removal and simple and robust machine construction design targets. The thermal management is particularly challenging because there are limited means for an effective heat removal from the machine body. Selected design choices impacting the machine's thermal behaviour, including alternative electrical insulation systems, winding impregnation quality, thermal contact interfaces and different winding and housing configurations are investigated. A three-dimensional (3D) transient finite element (FE) analysis has been carried out alongside an experiment informed low-order thermal network sensitivity evaluation. The theoretical body of work has been supplemented with thermal tests on an array of motorettes fabricated using alternative electrical insulation systems. The theoretical and experimental findings suggest that the winding impregnation quality has a dominant impact on the actuator's thermal behaviour, where both heat removal path (thermal resistance) and heat storage (thermal capacitance) need to be well balanced for the short-duty transient operation under a faulted condition.

Machine learning or behaviour heuristics? The synergy of approaches to defeat advanced ransomware threats

This paper focuses on a successful and fruitful combination of machine learning (ML)-based approach and heuristics-based approach in the case of Advanced Ransomware Defence, where the advanced ransomware is the ransomware that maliciously exploits the trusted context of execution, so it is the case of ransomware injection into well-known trusted processes, system services, that are used for the disguise of the malicious encryption. ML is used for malicious or benign classification of call stacks that match injections into trusted processes. The heuristics-based technique is based in our case on just one of the examples of injections, using such API as CreateRemoteThread and WriteProcessMemory. This approach has been used with good results to the case of Ryuk ransomware, one of the deadliest malware weapons. We show how the ML helps to find those call stacks which match malicious injections with high probability. Then we augment the results of the ML classifier with the special detection of threads, created in the trusted process, using other sensors, including kernel drivers. This combination provides the maximum accuracy and the ability to remediate the attack. This paper also presents the architectural materials as well as the links and references to the hands-on demonstration of collecting suspicious stacks. We also show how to use ML decisions and pair these decisions with the thread creation events as the sensor examples. The links with demonstrations use the execution of the real-world Ryuk malware strain. The analysis of the events flow is also shown in the kernel debugger coupled with the case analysis with the help of other tools like Process monitor.

Design and Implementation of a Control System for an Autonomous Reforestation Machine Using Finite State Machines

Reforestation is performed after the final felling as an important and often law-mandated step to ensure that wood production is sustainable. In Sweden alone, over 400 millions seedlings are planted annually. This work is physically demanding and the quality is uneven. Therefore, automatic production systems are under research and development. A necessary effort in this endeavor is presented in this paper: the development and evaluation of a mission supervisor utilized to control the mission and behavior of a full-scale autonomous forest regeneration machine tested in realistic environments. The mission supervisor is implemented in the Robot Operating System framework using a finite state machine package called SMACH. A terrain machine built as a research platform with an added full-scale forwarder crane is used as a base machine. First, we describe the scenario in which planting is conducted, whereupon we develop the composite tasks required as states. A simplified simulator then enables an intermediate step before field experiments. The system is implemented and operated in real time on a full-scale machine. Results show that the developed SMACH mission supervisor can be used as a sound basis for an autonomous forest regeneration machine and the chosen communication solution between different systems works well. The simulations show good agreement with the experiments. The results also show that crane movements take 70% of the machine time, emphasizing the importance of limiting crane movement, improving the actuator movement speed and integrating the composite solutions. Further development with a holistic approach is required before the concept can reach the prototype level.

Transient thermal models of induction machines under Inter‐turn short‐circuit fault conditions

AbstractInduction machines are the working horses in a lot of industries. Inter‐turn short‐circuit (ITSC) fault is one of the most common failure modes taking place in them. It can generate large fault currents that lead to a local temperature rise in the faulty stator slot, which deteriorates the working performance of the machine or even cascades to a complete machine breakdown. This article focuses on developing transient thermal models for an induction machine under ITSC faults. The aim of these thermal models is to understand the thermal behaviour of the induction machine at different ITSC fault propagation stages. The first thermal model is based on finite element method (FEM) simulation that includes a physics‐based model for the thermal contact resistance. The second thermal model is a lumped parameter thermal network that models the stator as inter‐connected slot‐number parts. They are both capable of modelling the characteristic of non‐uniform temperature along the circumferential direction for the induction machine operating under ITSC fault conditions. After area‐weighted averaging the results of the FEM simulation, it is found they are quantitatively consistent, with an average relative error magnitude of 5%, as well.

Social displays – Die Fabrikation sozialweltlicher Zurechenbarkeit in der Robotik

AbstractEven if attributes such as being social, smart, autonomous or intelligent are too ambitious in ontological terms, ‘social’ robots, ‘smart’ agents, ‘autonomous’ vehicles and comparable machines demonstrate a non-trivial mode of automatization. This mode requires the human counterpart to develop a pragmatic understanding of the role, the function and the type of behaviour of such machines. Based on this problem, the article addresses the communicative relevance of design elements such as appearance and behavioural stylisation as well as the use of sign systems in order to create social accountability. The aim is to identify the taxonomies or role figures to which non-trivial machines are socially assigned through their design and which are further differentiated with the development of new designs. This, however, presupposes a fundamental sociological understanding of the structure and function of such displays. The present article and the analytical concept of social displays pursue this intermediated objective.

Tooth Base Geometrical Variability Effect on the Vibration Behavior of the Stator

The magnetic pressure generated in the stator of electric machines can be a significant source of noise and vibration. The vibration behavior of the stator and such mechanical structures primarily depends on their geometry. Although the stator is often considered a simple structure, its teeth are not included in this assumption. In this paper, we endeavor to evaluate the impact of stator geometrical variability on the vibration behavior of electric machines. Our sensitivity study reveals that natural frequencies and frequency response functions are highly sensitive to geometrical variability. To account for this, we conducted a variability propagation study using a stochastic finite element model. Our findings show negligible variability on the natural frequencies but a high variability on the frequency response functions, even with low input variability. As anticipated, our study highlights the critical role of accounting for geometrical variability to enhance the accuracy of finite element models and strengthen their predictive power. However, the main contribution of this work is that the vibration behavior of the stator is not coupled, implying that the impact on natural frequencies does not necessarily reflect the same effect on the frequency response function.

Modelling and Analysis of Inter-Turn Short-Circuit Faults for Large-Power SPM Wind Generators

This paper proposes a general analytical model for large-power surface-mounted permanent magnet (SPM) wind generators under inter-turn short-circuit (ITSC) faults. In the model, branch currents rather than phase currents are used as state variables to describe the electromagnetic behavior of the faulty machine. In addition, it is found that the multiphase Clarke transformation can be used to simplify the proposed fault model with the inductances calculated analytically or numerically using finite element analysis. With the latter, both linear and nonlinear inductances can be obtained, and the non-linear inductances are used for the fault modelling of large power rating machines due to larger electrical loading and heavier magnetic saturation. With the developed fault model, studies of scaling effects (different power ratings such as 3 kW, 500 kW and 3 MW) and the influence of fault location on the electromagnetic performance of SPM generators with series-parallel coil connections have been carried out. The simulation results show that large-power SPM wind generators are vulnerable to ITSC faults when a relatively small number of turns are short-circuited and a single-turn short-circuit fault at the top of the slot is found to be the worst case.

Play it Again

Contemporary discourses accompanying the deployment of machine learning tend to fit within a meta-narrative of automation. Whether heralded as inevitable or criticized as reductive, this process of automation tends to be considered as concurrent to technology’s extension in general. By drawing on the social and technical history of machine learning, I would like to suggest that play offers at least one other way of framing machine learning’s development, one that could help foster other expectations and evaluations of machine learning performances. The genealogy of machine learning I will provide draws on certain historical tropes to problematize contemporary debates. I will distinguish two normative paradigms: automation and play. Each one expresses differing, albeit not incompatible values, expectations and objectives when evaluating machine behaviors and their interactions with human ones. Whereas automation pushes us to consider machines as ideally working by themselves, play requires a social and affective engagement whose outcome is always partially unpredictable. It helps account for the relative open-endedness, intractability and recursiveness of machine learning systems embedded in social practices. More specifically, I underline why play provides a sweet spot for thinking about performances such as those we are increasingly seeing in machine learning systems, that combine both rule-following behaviors and forms of improvisation upon those rules. Play includes automaticity as a level of behavior among others. The more general claim I am making is that play gives us a window onto a different history of machine learning and for imagining other forms of social interaction with and through technology.

Characterization of the Machine Behavior During Hollow Embossing Rolling of Metallic Bipolar Half Plates

Characterization of the Machine Behavior During Hollow Embossing Rolling of Metallic Bipolar Half Plates

Insight on turning of stainless steel 431 and modelling by ANN

ABSTRACT An insight study has been addressed to communicate the machining behavior of SS 431 in terms of tool wear rate (TWR) and chip study. Experimental investigation shows that cutting speed (VC) as the most significant parameter to have TWR for SS 431. At low VC 19.5 m/min, chip formed was continuous hairy structure with regular saw fractured morphology, principally promoted due to intensely concentrated shear bands between neighboring segments resulting from shear localized instability in the primary shear zone. 25.9 and 30.6 m/min produces continuous ribbon shape chip and twisted chip, respectively. At high VC 30.6 m/min, owing to high-temperature generation, shear deformation zone was softer and perfectively plastic with least chip contact length encouraged to produce least TWR. The parameters 30.6 m/min, 0.08 mm/rev, and 0.15 mm of VC, feed, and depth-of-cut found optimum to reach least TWR. Further ANN was exercised to predict TWR is good agreement with the experiments.

Influence of Austenite Stability on the Machinability of Different Grades of Austempered Ductile Iron (ADI) During Dry Turning Machining

This work evaluated the machinability characteristics of three different austempered ductile irons using three machinability indicators: cutting force, tool wear, and surface roughness. Additionally, this paper addresses the interplay among the production conditions of ADIs, microstructural development, the TRIP effect arising from transformations of the retained austenite into martensite, and the machinability behavior of ADIs. Various characterization studies including hardness measurement, X-ray diffraction analysis, light and electron microscope were used. Machinability results indicated that the increase in cutting force is always associated with a corresponding increase in the martensite content when the cutting depth was increased from 0.5 to 2.0 mm. Such correlation is more evident with ADI grade austempered at high austempering temperature, 375 °C (ADI-375). It was noticed that although ADI-375 has only 0.46% martensite in the structure before machining, this ratio increases with the highest depth of cut of 2.0 mm to reach about ~ 23% martensite with a corresponding increase in the cutting force. Ferrite content and morphology have a great impact on the overall surface finish of each grade as it leads to deteriorating the surface characteristics of the investigated alloys. This could explain the highest roughness values across all processing experiments obtained in the ADI grade austenitized in the intercritical region (IADI), which mainly contains the highest ferrite content. Tool wear and chip characteristics were also studied in this work.

Mimicking Electronic Gaming Machine Player Behavior Using Reinforcement Learning

This study looks at how Reinforcement Learning (RL) approaches can be used to understand player behavior in Electronic Gaming Machines (EGMs) found in venues like casinos. The gaming business is keen to learn about the many types of player behavior and create virtual players mimicking these behaviors. To achieve this, we trained RL models to mimic player behavior by grouping different playing styles with K-means clustering and determining termination states for one of the playing behaviors. The Proximal Policy Optimization (PPO) and Actor Critic using Kronecker-Factored Trust Region (ACKTR) models were subsequently implemented, with the agents being rewarded based on their proximity to the termination states. Our findings suggest that the ACKTR model performed better than the PPO model, with the generated playing behavior demonstrating a high level of statistical similarity to real-world player behavior within the selected cluster.

Socially intelligent machines that learn from humans and help humans learn.

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL)by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human-machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans byproactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'.

INVESTIGATIONS ON MECHANICAL, WEAR AND MACHINING BEHAVIOR OF TITANIUM-REINFORCED AlMg0.5Si COMPOSITES

AlMg0.5Si (AA6063) as a base matrix composite with different weight percentages of Titanium (Ti) has been synthesized by the stir casting method. Objective of this research is to investigate the effect of Ti addition on AA6063 to produce aluminium matrix composites (AMCs). AA6063, AA6063/4%-Ti, AA6063/8%-Ti, and AA6063/12%-Ti are different volume fractions. The Scanning electron microscope (SEM) analysis is used to examine the modified grain refinement and worn surface on fabricated AMCs. The evenly distributed structure was attained on the processed AA6063/Ti combinations. The mechanical properties of processed composites were investigated with the following experimental tests such as tensile strength, impact strength, micro hardness, compressive, and flexural strength. The entire mechanical properties of fabricated AMCs were enhanced with increasing Ti reinforcement particles. The CNC drilling of AA6063/Ti composites was machined by spindle speed, feed and depth of cut. The gray relational analysis (GRA) was used to carry out the optimal machining parameters with their responses such as material removal rate and surface roughness. From the analysis of ANOVA, spindle speed parameter achieved most influencing parameters.

Influence of Drilling Parameters on the Delamination and Surface Roughness of Insulative-Coated Glass/Carbon-Hybrid Composite

Drilling in synthetic fiber-reinforced polymer composites is facing challenges due to their anisotropic, inhomogeneity, and abrasive machining behavior. The joining of composite parts using fasteners is commonly done by the drilling, and the generated heat is one of the main causes to damage the drilled hole in the composite. Moreover, the quality of drilled hole is crucial for joining parts effectively. The paper presents the design, fabrication, and drilling of a hybrid fiber-reinforced polymer (HFRP) based on insulative coating. These composites were fabricated using vacuum infusion molding (VIM) and coated with different thicknesses to investigate the influence of drilling parameters and associated damages. Cutting speed, feed rate, and coating thicknesses were varied, and a full factorial design of the experiment was formulated. High-speed steel (HSS) twist drill bit was used to drill the coated composite and test samples, and delamination factor and surface roughness were measured. ANOVA and full factorial response optimizer were used to evaluate the influence and optimum drilling parameters. The delamination factor (DF) at the entry and surface roughness were found to decrease with the increasing cutting speed. However, the DF at the exit showed the opposite. Coating thickness influenced the delamination at the entry whereas delamination at the exit has been found insignificant. For drilling HFRP composite with 1 mm coating thickness, 3000 RPM spindle speed and 0.08 mm/rev feed rate were found optimum parameters in minimizing surface roughness and delamination damage. However, 6000 RPM and 0.02 mm/rev were found optimum parameters for drilling HFRP composite with 1.5 mm coating thickness.

Modelling and Analysis of Inter-Turn Short-Circuit Fault of PM Machines With Parallel-Connected Coils

This paper presents a general analytical fault model in a compact matrix form for surface-mounted permanent magnet (SPM) machines with parallel-connected coils, which is useful to study the machine performance under the inter-turn short circuit (ITSC) fault. To simplify the fault model, the multiphase Clarke transformation has been proposed. In the model, the branch currents rather than the phase currents are employed as state variables to describe machine behaviours under fault. Additionally, self- and mutual-inductances are obtained by winding function approach (WFA) plus slot permeance method. The proposed analytical fault model is applied to a 3 kW 96-slot 32-pole SPM machine and validated by time-stepping FE simulations. A small scale 12-slot 4-pole SPM machine prototype has also been built to further validate the accuracy of the proposed fault model.

A discrete state Markov chain model and decomposition solution method for the transient behaviour of buffered long serial transfer lines

As demand for greater manufacturing efficiency over shorter production runs in serial manufacturing lines increase, models for transient behaviour are required for insight into short-term production operation and control. This paper proposes a method to model the discrete time transient behaviour of paced long serial transfer lines with finite buffers. The method provides for the solution of lines comprised of machines that represent a variety of complex discrete state behaviours. The solution decouples the line into two-machine, one buffer models and adds external starvation and blockage coupling states and harmonizes the system behaviour by solving for transition probabilities into these external states with optimization. The efficacy of the approach is illustrated with three examples, a twenty-machine serial line with geometric machine behaviour, a twenty-machine serial line with complex machine behaviour, and the design of a transient line for a CONWIP strategy.