Behavior Of Machine Research Articles

Reprogrammable Metamaterial Processors for Soft Machines.

Soft metamaterials have attracted extensive attention due to their remarkable properties. These materials hold the potential to program and control the morphing behavior of soft machines, however, their combination is limited by the poor reprogrammability of metamaterials and incompatible communication between them. Here, printable and recyclable soft metamaterials possessing reprogrammable embedded intelligence to regulate the morphing of soft machines are introduced. These metamaterials are constructed from interconnected and periodically arranged logic unit cells that are able to perform compound logic operations coupling multiplication and negation. The scalable computation capacity of the unit cell empowers it to simultaneously process multiple fluidic signals with different types and magnitudes, thereby allowing the execution of sophisticated and high-level control operations. By establishing the laws of physical Boolean algebra and formulating a universal design route, soft metamaterials capable of diverse logic operations can be readily created and reprogrammed. Besides, the metamaterials' potential of directly serving as fluidic processors for soft machines is validated by constructing a soft latched demultiplexer, soft controllers capable of universal and customizable morphing programming, and a reprogrammable processor without reconnection. This work provides a facile way to create reprogrammable soft fluidic control systems to meet on-demand requirements in dynamic situations.

Design of a Vending Machine Using Verilog HDL and Implementation in Genus & Encounter

This paper proposes the design, implementation, and verification of a vending machine using the Finite State Machine (FSM) methodology in Verilog HDL. The FSM is used to manage the multiple states of the vending machine, including “idle,” “accepting coins,” “dispensing item,” and “returning change.” The implementation of the vending machine is done in Verilog HDL, and the FSM is implemented as a state diagram. The design is then synthesized using the Genus synthesis tool and implemented using the Encounter implementation tool. The Genus tool uses advanced optimization techniques, such as timing-driven placement and clock tree synthesis, to improve the design’s performance and area. The Encounter tool performs physical design, including placement and routing, to meet the design’s timing, power, and area constraints. To validate the design’s correctness and functionality, a test bench is created to simulate the behavior of the vending machine. The simulation results are then used to verify that the design meets the required specifications and that the FSM behaves as expected. The proposed design is then can be implemented on a Field Programmable Gate Array (FPGA) to demonstrate its effectiveness in a real-world scenario. The results of the implementation are presented and analyzed to validate the design’s performance, power consumption, and area. Overall, the vending machine using FSM in Verilog HDL, implemented in Genus and Encounter, provides a reliable and efficient solution for users to purchase items from the machine. The proposed design and implementation demonstrate the feasibility and effectiveness of this approach, and the results show that the design meets the required specifications and performs well in a real-world scenario.

A Predictive Control Model of Bernoulli Production Line with Rework Loop for Real-Time WIP Optimization in Permutation Flowshop

Permutation flowshop design and optimization are crucial in industry as they have a direct impact on production scheduling and efficiency. The ultimate goal is to model the production system (PSM) based on revealing the fundamental principles of the production process, and to schedule or reschedule production release plans in real time without interrupting work-in-progress (WIP). Most existing PSMs are focused on static production processes which fail to describe the dynamic relationships between machines and buffers. Therefore, this paper establishes a PSM to characterize both the static and transient behaviors of automatic and manual machines in the permutation flowshop manufacturing system. Building upon the established PSM, based on Bernoulli’s theory, discrete event model predictive control is proposed in this paper; its aim is to realize real-time optimization of production release plans without interfering with work-in-progress. According to the results of numerical examples, the discrete event model predictive control proposed in this paper is feasible and effective. The model established in this paper provides a theoretical basis for optimizing the effective operation of work-in-progress and replacement process systems.

Advancing balancing machine technology: A cost-effective solution for laboratory and small-medium enterprises

Small-medium enterprises (SMEs) that provide maintenance services to power plant companies, notably on rotor components, encounter a significant challenge in balancing. The main concern is the high cost associated with outsourcing balancing services. The absence of portable balancing machines requires transporting components back to the workshop, resulting in substantial downtime lasting weeks before reinstallation. This paper presents a DIY-compatible balancing machine design and its assembly process, which can be applied to various balancing processes. The design primarily emphasizes mechanical aspects, excluding measurement and instrumentation components, which can be obtained or developed based on the user's software proficiency. The actual expenditure for developing this balancing machine is approximately RM11,698.00. In the development process, benchmarking, concept scoring, functionality evaluations, dynamic analysis, and ideation for workable balancing machine concepts were all involved. The modal analysis demonstrates that the first natural frequency at 279.5 Hz (equivalent to 16,770 rpm) is significantly higher than the motor's excitation frequency, which reaches a maximum speed of around 2800 rpm (46.67 Hz). This result indicates that the dynamic behaviour of the balancing machine does not significantly affect the dynamic results of the rotor balancing process.

Analysis of the Machine-Specific Behavior of Injection Molding Machines.

The performance of an injection molding machine (IMM) influences the process and the quality of the parts manufactured. Despite increasing data collection capabilities, their machine-specific behavior has not been extensively studied. To close corresponding research gaps, the machine-specific behavior of two hydraulic IMMs of different sizes and one electric IMM were compared with each other as part of the investigations. Both the start-up behavior from the cold state and the behavior of the machine at different operating points were considered. To complement this, the influence of various material properties on the machine-specific behavior was investigated by processing an unreinforced and glass-fiber-reinforced polyamide. The results obtained provide crucial insights into machine-specific behavior, which may, for instance, account for disparities between computer fluid dynamic (CFD) simulations and experimental results. Furthermore, it is expected that the description of the machine-specific behavior can contribute to transfer knowledge when applying transfer learning algorithms. Looking ahead to future research, it is advised to create what is referred to as a "machine fingerprint", and this proposal is accompanied by some preliminary recommendations for its development.

Applying Learning and Self-Adaptation to Dynamic Scheduling

Real-world production scheduling scenarios are often not discrete, separable, iterative tasks but rather dynamic processes where both external (e.g., new orders, delivery shortages) and internal (e.g., machine breakdown, timing uncertainties, human interaction) influencing factors gradually or abruptly impact the production system. Solutions to these problems are often very specific to the application case or rely on simple problem formulations with known and stable parameters. This work presents a dynamic scheduling scenario for a production setup where little information about the system is known a priori. Instead of fully specifying all relevant problem data, the timing and batching behavior of machines are learned by a machine learning ensemble during operation. We demonstrate how a meta-heuristic optimization algorithm can utilize these models to tackle this dynamic optimization problem, compare the dynamic performance of a set of established construction heuristics and meta-heuristics and showcase how models and optimizers interact. The results obtained through an empirical study indicate that the interaction between optimization algorithm and machine learning models, as well as the real-time performance of the overall optimization system, can impact the performance of the production system. Especially in high-load situations, the dynamic algorithms that utilize solutions from previous problem epochs outperform the restarting construction heuristics by up to ~24%.

Application of Ni-based superalloy in aero turbine blade: A review

A superalloy is a high-performance alloy used for achieving stability and strength at elevated temperatures. Generally, a temperature of about 800°C is developed at the first stage of the aero turbine blade. So it is a challenge to choose a material that can withstand such high temperatures. Nowadays advanced generation single crystal Ni-based superalloys are being used for the fulfillment of high-temperature requirements in aero turbine components. But the continuous effort is being carried out to manufacture new materials that can withstand more and more temperatures generated at aero turbine because the high-temperature operation of the turbine is directly proportional to the efficiency of a turbine engine. The requirement of high-temperature application upgraded the Ni-based superalloy and different generations of alloys were prepared from time to time those were discussed in the paper. Recent developments of superalloys in the paper mainly include fifth and sixth generations of superalloys those incorporate Hf, Re, and Ru-like elements in significant amounts. A brief extension discussion has been placed on the types, processing, and hot deformation characteristics of Ni-based superalloys. VIM melting, single crystal growth, directional solidification, and powder metallurgy techniques for manufacturing Ni-based superalloys are discussed in detail. The strengthening mechanism and machinability behavior depict the characteristics of superalloys in a mechanical direction. Heat treatment and hot deformation behavior of Ni-based superalloy was precisely discussed in the context so as to correlate the strength of the material with the temperature change. Overall, the paper describes the origin, processing and characteristics of Nickel based superalloy in detail.

Dry and Minimum Quantity Lubrication Machining of Additively Manufactured IN718 Produced via Laser Metal Deposition

Inconel 718 (IN718), a Ni-based superalloy, is immensely popular in the aerospace, nuclear, and chemical industries. In these industrial fields, IN718 parts fabricated using conventional and additive manufacturing routes require subsequent machining to meet the dimensional accuracy and surface quality requirements of practical applications. The machining of IN718 has been a prominent research topic for conventionally cast, wrought, and forged parts. However, very little attention has been given to the machinability of IN718 additively manufactured using laser metal deposition (LMD). This lack of research can lead to numerous issues derived from the assumption that the machining behavior corresponds to conventionally fabricated parts. To address this, our study comprehensively assesses the machinability of LMDed IN718 in dry and minimum quantity lubrication (MQL) cutting environments. Our main goal is to understand how LMD process variables and the cutting environment affect cutting forces, tool wear, surface quality, and energy consumption when working with LMDed IN718 walls. To achieve this, we deposited IN718 on SS309L substrates while varying the following LMD process parameters: laser power, powder feed rate, and scanning speed. The results unveil that machining the deposited wall closer to the substrate is significantly more difficult than away from the substrate, owing to the variance in hardness along the build direction. MQL greatly improves machining across all processing parameters regardless of the machining location along the build direction. Laser power is identified as the most influential parameter, along with the recommendation for a specific combination of power feed rate and scanning speed, providing practical guidelines for optimizing the machining process. While MQL positively impacts machinability, hourly energy consumption remains comparable to dry cutting. This work offers practical guidance for improving the machinability of LMDed IN718 walls and the successful adoption of LMD and the additive–subtractive machining chain. The outcomes of this work provide a significant and critical understanding of location-dependent machinability that can help develop targeted approaches to overcome machining difficulties associated with specific areas of the LMDed structure. The finding that MQL significantly improves machining across all processing parameters, particularly in the challenging bottom region, offers practical guidance for selecting optimal cutting conditions. The potential economic benefits of MQL in terms of tool longevity without a substantial increase in energy costs is also highlighted, which has implications for incorporating MQL in several advanced manufacturing processes.

INVESTIGATING CUTTING FORCE AND CUTTING POWER WHEN TURNING AA6082-T4 ALLOY AT CUTTING DEPTHS SMALLER THAN TOOL NOSE RADIUS

Aluminum alloys are widely preferred engineering materials in the manufacturing industry due to their high formability, good mechanical strength, and low density. Machining problems in aluminum alloys include built-up-edge formation, chip rupturing, and low surface quality, particularly in the 6xxx series due to the high Si content in the machining area. The aim of this study was to investigate the influence of cutting depth smaller than the tool corner radius, and various cutting parameters on cutting force and cutting power in machining AA6082-T4 alloy. In this context, the Johnson-Cook material model was established for AA6082-T4 alloy, and machining behaviors in terms of cutting force, and cutting power were investigated by performing finite element method (FEM) analyses using a full factorial design and variance analyses with different machining parameters. In conclusion, the lowest cutting forces were achieved with a cutting depth of 0.3 mm and a feed of 0.1 mm/rev, and the lowest cutting power was obtained with a cutting speed of 300 m/min, a cutting depth of 0.3 mm, and a feed of 0.1 mm/rev. In addition, the most effective machining parameters have been determined as cutting depth with a ratio of 91.74% for cutting force and cutting speed with a ratio of 33.13% for cutting power based on the results of variance and regression analysis.

Enhancing power control in doubly fed induction generator based wind turbines: a performance comparison of fuzzy logic and sliding mode control methods

Abstract Effective power regulation is critical for ensuring the stability and performance of Doubly Fed Induction Generator (DFIG)-based wind turbines. This study conducts a comparative study between two control methodologies: fuzzy logic control (FLC) and sliding mode control (SMC), to regulate power in DFIG-based wind turbines. The paper emphasizes the importance of power regulation in wind turbines and its impact on grid stability, presents mathematical equations governing DFIG-based wind turbine systems, encompassing machine equations, power equations, and control objectives related to power regulation. A comprehensive model of the DFIG-based wind turbine system is developed, accounting for dynamic machine behavior and grid interface considerations. The modeli ng process integrates relevant electrical and mechanical components, along with power regulation control strategies. Subsequently, FLC and SMC control methodologies are developed and implemented to regulate desired power. Detailed discussions on controller design and tuning are provided, aligning with specific power control requirements. The results highlight SMC’s effectiveness in achieving precise power regulation within DFIG-based wind turbines, showcasing its superior response characteristics. The study offers valuable insights into the advantages and trade-offs associated with each control approach, aiding researchers and practitioners in selecting the most suitable control methodology for their operational needs. In summary, this paper contributes to the field of wind turbine control systems through a comparative study of FLC and SMC controllers, focusing on regulating power in DFIG-based wind turbines. The findings demonstrate SMC’s superior performance in response, enhancing our understanding of control methodologies for optimizing wind turbine performance and grid integration in sustainable energy systems.

High-frequency Differential Mode Modeling of Universal Motor's Windings

The universal motor is a rotating electrical machine that can operate on either direct current or single-phase alternating current, similar to a DC motor. It has been widely used in various small and inexpensive drives for a long time, mostly in home appliances and hand tools. The noise generated by a universal motor is believed to be closely associated with the electromagnetic torque fluctuations of the machine, which are caused by variations in the current supplied to the motor. The power electronics utilized for controlling the motor's speed are responsible for these current changes. Accurate high-frequency motor models are crucial for reliable electromagnetic interference simulations in motor drive power electronic systems. Research efforts have expanded to explore different realistic configurations that can be used to investigate the electromagnetic compatibility behavior of electrical machines. This study describes a method for predicting the differential mode impedance of universal motor. The behavior of each motor winding has been individually studied through impedance measurements, starting with the armature, followed by the series winding, and finally the inductive compensating winding. The prediction results over a wide frequency range up to 1 MHz are in good agreement with the measurements and enabled us to propose a model circuit for each motor winding.

A model-based approach for gear train whine noise reduction by mesh phasing modification

Heavily loaded gear trains are known to produce a characteristic whine noise and vibration signature, which radiates through the bearings producing an undesired noise emission affecting the NVH behavior of the overall machine. This phenomenon is generated by the variable mesh stiffness and the static transmission error of the gear pairs in mesh within the gear train, but its magnitude strongly depends on how the meshing processes combine between each others. In this context, the present work proposes an analytical methodology to predict amplitude and direction of the variable loads applied on the bearings of idle gears, assuming quasi-static conditions. The dissertation demonstrates that the multiple mesh forces produce a variable load on idle gear bearings describing an ellipse whose shape depends on various geometrical parameters of the system. Numerical examples are shown to assess the potentials of the method and highlight how gear train spatial layout and phasing between different meshings may be used during gearbox design projects to limit the emitted noise of the gear train.

Nothing Like Compilation: How Professional Digital Fabrication Workflows Go Beyond Extruding, Milling, and Machines

Understanding how professionals use digital fabrication in production workflows is critical for future research in digital fabrication technologies. We interviewed thirteen professionals who use digital fabrication for the low-volume manufacturing of commercial products. From these interviews, we describe the workflows used for nine products created with a variety of materials and manufacturing methods. We show how digital fabrication professionals use software development to support physical production, how they rely on multiple partial representations in development, how they develop manufacturing processes, and how machine control is its own design space. We build from these findings to argue that future digital fabrication systems should support the exploration of material and machine behavior alongside geometry, that simulation is insufficient for understanding the design space, and that material constraints and resource management are meaningful design dimensions to support. By observing how professionals learn, we suggest ways digital fabrication systems can scaffold the mastery of new fabrication techniques.

EFFECT OF MACHINABILITY OF GNP–GFRP COMPOSITES ON TENSILE STRENGTH AND FATIGUE BEHAVIOR

The paper focuses on the cutting behavior of Glass Fiber Reinforced Polymer (GFRP) composites and GNP–GFRP composites that contain varying amounts of Graphene Nano Platelets (GNP). GFRP composites are increasingly being used in a variety of industrial applications due to their excellent mechanical properties, such as high strength, stiffness, and low weight. However, their machining and cutting behavior can be challenging due to the presence of the reinforcing fibers. Therefore, the study aims to investigate the machining behavior of GFRP composites and the effect of adding GNP on their cutting behavior. The effect of different parameters such as cutting speed, feed rate and reinforcement rate on cutting forces and delamination factor is investigated. In addition, the tensile strength and fatigue behavior of the composite materials with the best and worst delamination factors were also determined. Addition of up to 0.2 wt.% of GNP to GFRP composites resulted in an increase in cutting forces and delamination factor when drilling GFRP composites. While the cutting force and delamination factor decreased with the increase in cutting speed, the cutting force and delamination factor increased with the increase in the feed rate. Analysis of variance (ANOVA) was performed to determine the effects of drilling parameters and reinforcement ratio on cutting force and delamination factor according to full factorial experimental design. The most efficient factor on the cutting forces is found to be feed rate (84.97%), followed by the reinforced rate (6.48%) and cutting speed (6.13%). The most efficient factor on the delamination factor is determined to be feed rate for (44.49%), followed by the reinforced rate (29.20%) and cutting speed (21.73%).

Microstructure effect on the machinability behavior of additive and conventionally manufactured Inconel 718 alloys

The microstructural characteristics and mechanical properties of forged parts differ significantly from those of additively manufactured parts, which results in different machinability. In this paper, the microstructure effect on the machinability of Inconel 718 parts obtained by different manufacturing processes, laser directed energy deposition (LDED), laser powder bed fusion (LPBF) and forging, is evaluated and critically discussed in terms of milling forces, surface morphology, surface roughness, chip morphology and subsurface microstructure. The results show that due to having the smallest grain size, high dislocation density, and precipitation hardening provided by the reinforcing phases, the milling force is maximized when cutting LPBF Inconel 718 parts. In addition to grain size and dislocation density, the texture also has an effect on the plastic deformation of the subsurface after milling. Furthermore, LDED and forged Inconel 718 parts undergo a continuous dynamic recrystallization process characterized by sub-grain rotation transforming low angle grain boundaries into high angle grain boundaries during milling. This study provides some theoretical guidance for the integration of additive and subtractive processes in the machining of complex structural parts with Inconel 718 alloy.

Impact of radial rake angle and machining constraints on the machining behavior and functionality of additive manufactured inconel 718 alloy

The Additive manufactured (direct metal laser sintering) samples has shown greater surface roughness which leads to reduce the functionality of the components. Additionally, the cutting force requirement and obtainable surface finish show the vigorous role during the slot milling process of many materials. In this study, the slot milling process has been conducted on the Additive Manufactured (AM) Inconel 718 alloy under numerous cutter nomenclature such as −7°, 0° and +7° in radial rake angle and machining circumstances such as a cutting feed (5, 15, and 25 mm min−1) and cutting speed (14.13, 28.26, and 42.39 m min−1) to analyze its impact on the cutting force requirement and surface roughness of AM Inconel 718 alloy through TOPSIS statistical tool. Through the above analyses, it is found that the cutter nomenclature plays a vigorous role in the cutting force requirement and obtainable surface roughness followed by the cutting feed and cutting speed, owed to the establishment of moderate strain strengthening and thermal soothing effect.

PWM Voltage-Based Modeling for PM Machines With Interturn Short Circuit Fault Considering the Effect of Drives

This paper presents a novel analytical fault model based on PWM voltage rather than ideal sinewave phase voltage for permanent magnet (PM) machines with inter-turn short circuit fault. This model is important because PM machines are usually driven by the pulse width modulation (PWM) inverter. In addition, main contributors to the accuracy of the fault model such as (i) the cables between the inverter and the machine, (ii) the precision of the rotor position estimation and (iii) the inverter, in particular the metal-oxide semiconductor field-effect transistors (MOSFETs) have been considered. Therefore, this developed model can fully account for the effects of PWM harmonics and accurately predicts the machine's behaviors (particularly the fault current) under fault conditions considering the effect of the drives. This has rarely been studied in the literature as most existing fault modelling methods adopt sinewave phase voltage as inputs, which cannot account for the PWM harmonics in the fault currents. However, the investigations in this paper showed that the PWM harmonic component could be close to 25% of the fundamental component in the fault current, and hence cannot be neglected. The accuracy of the proposed fault model has been validated by a series of experimental tests.

Experimental Validation of a Mesh-to-Mesh Magnetic Force Projection for e-NVH Simulation

This article discusses the use of simulation for analyzing the vibro-acoustic behavior of radial flux electrical machines under magnetic forces. Numerical simulation enables a systematic investigation of electromagnetic Noise, Vibration and Harshness (e-NVH) issues, but requires accurate modeling and this despite manufacturing uncertainties. Experimental validation is a necessary step to setup a multiphysic virtual prototyping e-NVH workflow. In particular, a key point is the magneto-mechanical coupling. In this study, the Virtual Work Principle (VWP) is used for magnetic force calculation. The goal is to propose an accurate e-NVH model applied to a 12s10p radial flux electrical machine using a mesh-to-mesh projection. This e-NVH model enables to understand the origin of the noisiest orders along the whole speed range of the machine. Differences between simulation and experiments are discussed thanks to the modal participation factor of specific harmonics. Mesh-to-mesh projection results are compared to those obtained by using lumped tooth force, highlighting the contribution of the tooth tip moment to the vibration. Finally, the tooth modulation effect is discussed in the light of these results.

Impact of 13-93 bio-glass inclusion on the machinability, in-vitro degradation, and biological behavior of Y-TZP-based bioceramic composite

Nowadays, bio-ceramic composite material is demanded in the biomedical industry due to its remarkable biological and mechanical properties. But, the shaping of such material challenges the manufacturer. In this work, bioceramic composite materials, having general weight composition [(100-x) (3Y-TZP) – x (13-93 BG)] where x = 0 to 25 wt%, has been prepared, and their machinability in terms of MRR (material removal rate) and SR (surface roughness) is studied as a function of BG composition and machining temperature. It is found that MRR decreases with increasing the concentration of bioactive glass (BG) while it increases with increasing the machining temperature. The SR is found in the opposite trend. The MRR, SR, and material removal mechanics (MRM) are also confirmed by the HR-SEM and AFM (atomic force microscopy) analysis. The effect of BG addition on the physical, mechanical, in-vitro degradation, and cell culture behaviour of the composite materials are also evaluated. The relative density and mechanical properties, such as flexural and compressive strength, are found to increase up to 10 wt% of BG content; afterward, it decreases. The hardness is increased with BG addition in 3Y-TZP ceramics. The in-vitro degradation indicates the dissolution and apatite layer formation after 35 days of immersion in simulated body fluid (SBF) solution. The cell proliferation is examined by MTT assay over MG-63 cell lines. The cell proliferation ability of the composite is increased with increasing the BG concentration and found a maximum of up to 25 wt% of BG. Overall, the best findings were obtained with 10 wt% BG contained 3Y-TZP ceramic composite sintered at 1250 °C. The relative density, flexural strength, and compressive strength are about 98.34%, 397.9 MPa, and 488.7 MPa, respectively. Based on the obtained results, it may be recommended that 10 wt% BG concentration in 3Y-TZP bioceramic composite is the most suitable and potential material for biomedical application.

Stingy bots can improve human welfare in experimental sharing networks

Machines powered by artificial intelligence increasingly permeate social networks with control over resources. However, machine allocation behavior might offer little benefit to human welfare over networks when it ignores the specific network mechanism of social exchange. Here, we perform an online experiment involving simple networks of humans (496 participants in 120 networks) playing a resource-sharing game to which we sometimes add artificial agents (bots). The experiment examines two opposite policies of machine allocation behavior: reciprocal bots, which share all resources reciprocally; and stingy bots, which share no resources at all. We also manipulate the bot’s network position. We show that reciprocal bots make little changes in unequal resource distribution among people. On the other hand, stingy bots balance structural power and improve collective welfare in human groups when placed in a specific network position, although they bestow no wealth on people. Our findings highlight the need to incorporate the human nature of reciprocity and relational interdependence in designing machine behavior in sharing networks. Conscientious machines do not always work for human welfare, depending on the network structure where they interact.