Prediction of Grinding Force by an Electroplated Grinding Wheel with Orderly-Micro-Grooves

The ability to predict a grinding force is important to control,monitor,and optimize the grinding process.Few theoretical models were developed to predict grinding forces when a structured wheel was used in a grinding process.This paper aimed to establish a single-grit cutting force model to predict the ploughing,friction and cutting forces in a grinding process.It took into the consideration of actual topography of the grinding wheel,and a theoretical grinding force model for grinding hardened AISI 52100 by the wheel with orderly-micro-grooves was proposed.The model was innovative in the sense that it represented the random thickness of undeformed chips by a probabilistic expression,and it reflected the microstructure characteristics of the structured wheel explicitly.Note that the microstructure depended on the randomness of the protruding heights and distribution density of the grits over the wheel.The proposed force prediction model was validated by surface grinding experiments,and the results showed(1)a good agreement of the predicted and measured forces and(2)a good agreement of the changes of the grinding forces along with the changes of grinding parameters in the prediction model and experiments.This research proposed a theoretical grinding force model of an electroplated grinding wheel with orderly-micro-grooves which is accurate,reliable and effective in predicting grinding forces.

Big data analytics with applications

In this paper,recent developments on the Internet of Things(IoT)and its applications are surveyed,and the impact of newly developed Big Data(BD)on manufacturing information systems is especially discussed.Big Data analytics(BDA)has been identified as a critical technology to support data acquisition,storage,and analytics in data management systems in modern manufacturing.The purpose of the presented work is to clarify the requirements of predictive systems,and to identify research challenges and opportunities on BDA to support cloudbased information systems.

Graph-based robot optimal path planning with bio-inspired algorithms

Recently,bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps.However,these approaches endure performance degradation as problem complexity increases,often resulting in lengthy search times to find an optimal solution.This limitation is particularly critical for real-world applications like autonomous off-road vehicles,where highquality path computation is essential for energy efficiency.To address these challenges,this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm,improved seagull optimization algorithm(iSOA)for rapid path planning of autonomous robots.A modified Douglas–Peucker(mDP)algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains.The resulting mDPderived graph is then modeled using a Maklink graph theory.By applying the iSOA approach,the trajectory of an autonomous robot in the workspace is optimized.Additionally,a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints.The proposed model is validated through simulated experiments undertaken in various real-world settings,and its performance is compared with state-of-the-art algorithms.The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction

The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation,with path planning emerging as a critical aspect.However,existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance.Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance,while neglecting road construction information,leading to potential time and energy inefficiencies in real-world scenarios involving infrastructure development.To address this issue,a digital twin-embedded multi-objective autonomous vehicle navigation is proposed under the condition of infrastructure construction.The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extrac-tion and modelling of the autonomous vehicle workspace.Additionally,a wavelet neural network is developed to predict real-time traffic flow,considering its inherent charac-teristics.Moreover,a multi-objective brainstorm optimisation(BSO)-based method for path planning is introduced,which optimises total time-cost and energy consumption objective functions.To ensure optimal trajectory planning during infrastructure con-struction,the algorithm incorporates a real-time updated digital twin throughout autonomous vehicle operations.The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction.The results highlight the improved performance and reli-ability of the autonomous vehicle system when equipped with the authors’approach,demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.

Support vector machine and ROC curves for modeling of aircraft fuel consumption

To estimate the fuel consumption of a civil aircraft,we propose to use the receiver operating characteristic(ROC)curve to optimize a support vector machine(SVM)model.The new method and procedure has been developed to build,train,validate,and apply an SVM model.A conceptual support vector network is proposed to model fuel consumption,and the flight data collected from routes are used as the inputs to train an SVM model.During the training phase,an ROC curve is defined to evaluate the performance of the model.To validate the applicability of the trained model,a case study is developed to compare the data from an aircraft performance manual and from the implemented simulation model.The investigated aircraft in the case study is a Boeing 737-800 powered by CFM-56 engines.The comparison has shown that the trained SVM model from the proposed procedure is capable of representing a complex fuel consumption function accurately for all phases during the flight.The proposed methodology is generic,and can be extended to reliably model the fuel consumption of other types of aircraft,such as piston engine aircraft or turboprop engine aircraft.

An emerging technology-wearable wireless sensor networks with applications in human health condition monitoring

Monitoring health conditions over a human body to detect anomalies is a multidisciplinary task,which involves anatomy,artificial intelligence,and sensing and computing networks.A wearable wireless sensor network(WWSN)turns into an emerging technology,which is capable of acquiring dynamic data related to a human body’s physiological conditions.The collected data can be applied to detect anomalies in a patient,so that he or she can receive an early alert about the adverse trend of the health condition,and doctors can take preventive actions accordingly.In this paper,a new WWSN for anomaly detections of health conditions has been proposed,system architecture and network has been discussed,the detecting model has been established and a set of algorithms have been developed to support the operation of the WWSN.The novelty of the detected model lies in its relevance to chronobiology.Anomalies of health conditions are contextual and assessed not only based on the time and spatial correlation of the collected data,but also based on mutual relations of the data streams from different sources of sensors.A new algorithm is proposed to identify anomalies using the following procedure:(1)collected raw data is preprocessed and transferred into a set of directed graphs to represent the correlations of data streams from different sensors;(2)the directed graphs are further analyzed to identify dissimilarities and frequency patterns;(3)health conditions are quantified by a coefficient number,which depends on the identified dissimilarities and patterns.The effectiveness and reliability of the proposed WWSN has been validated by experiments in detecting health anomalies including tachycardia,arrhythmia and myocardial infarction.

Friction predication on pin-to-plate interface of PTFE material and steel

In this paper,the friction behavior at a pin-to-plate interface is investigated.The pin and plate are made of Polytetrafluoroethylene(PTFE) and steel,respectively,and there is a reciprocating motion at the interface.Governing mathematical models for the relations of design variables and frictions are investigated,and a general procedure is proposed to solve the developed models and predict the friction forces at the interface subjected to given test conditions.Novel models have been developed to represent intrigued friction behaviors affected by various factors such as pin geometrics and finishes,lubrication conditions,and reciprocating speed.The test data from experiments is used to verify the effectiveness of the proposed models.

State of the art of friction modelling at interfaces subjected to elastohydrodynamic lubrication(EHL)

Elastohydrodynamic lubrication(EHL)is a type of fluid-film lubrication where hydrodynamic behaviors at contact surfaces are affected by both elastic deformation of surfaces and lubricant viscosity.Modelling of contact interfaces under EHL is challenging due to high nonlinearity,complexity,and the multi-disciplinary nature.This paper aims to understand the state of the art of computational modelling of EHL by(1)examining the literature on modeling of contact surfaces under boundary and mixed lubricated conditions,(2)emphasizing the methods on the friction prediction occurring to contact surfaces,and(3)exploring the feasibility of using commercially available software tools(especially,Simulia/Abaqus)to predict the friction and wear at contact surfaces of objects with relative reciprocating motions.

Effect of laser-discrete-quenching on bonding properties of electroplated grinding wheel with AISI 1045 steel substrate and nickel bond

To improve the bonding strength between the nickel bond and the hub of the electroplated diamond grinding wheel,a hybrid technique was proposed to combine laser prequenching steel substrate and post-electroplating nickel.To validate the effectiveness of the proposed technique,AISI 1045 substrate was nickel-coated.The bonding properties between the electroplated nickel coating and substrate with or without laser-discrete-quenching were discussed comparatively by scratch,indentation,and thermal shock tests.The results show that the prequenching treatment leads to phase transformation of AISI 1045 microstructure from the mixed pearlite and ferrite phases into the martensitic phase.Since the martensitic phase is characterized as a high corrosion resistance,the interface of substrate/coating is smooth and flat in the prequenched zone,and the coating is bonded well with the steel substrate.In contrast to the steel substrate without pre-quenching treatment,the proposed technique significantly enhanced the bonding strengths of the electroplated nickel-coating.On one hand,the average hardness of electroplated nickel-coating on the laser pre-quenched zone is increased by 18.7%,and the scratch depth with the same load become narrower and shallower.On the other hand,the coefficient of friction(CoF)and the vibration amplitude are reduced,and the coating is bonded effectively with the substrate to inhibit the crack initialization at the interface.This prevents effectively the coating from peeling off and improves significantly the thermal shock resistance property.