New insights into the methods for predicting ground surface roughness in the age of digitalisation

Grinding is a multi-length scale material removal process that is widely employed to machine a wide variety of materials in almost every industrial sector. Surface roughness induced by a grinding operation can affect corrosion resistance, wear resistance, and contact stiffness of the ground components. Prediction of surface roughness is useful for describing the quality of ground surfaces, evaluate the efficiency of the grinding process and guide the feedback control of the grinding parameters in real-time to help reduce the cost of production. This paper reviews extant research and discusses advances in the realm of machining theory, experimental design and Artificial Intelligence related to ground surface roughness prediction. The advantages and disadvantages of various grinding methods, current challenges and evolving future trends considering Industry-4.0 ready new generation machine tools are also discussed

Optimization of robot plasma coating efficiency using genetic algorithm and neural networks / S.Prabhu and B.K.Vinayagam

This work describes the Taguchi analysis coupled with Artificial Neural network and Genetic algorithm to optimize the robot deposition parameters used for plasma coating on titanium aluminum alloy material. L27 orthogonal array have been used for coating the work piece using robot. The Arc current (Amp), Arc voltage (volt), powder feed rate(mm/sec), substrate Surface Roughness (μm), Spray gun distance (mm) and TiO2 content in feedstock (%) have been considered as input parameters and coating efficiency is considered as output parameters. Using feed forward Artificial Neural Networks (ANNs) trained the experimental values with the Levenberg–Marquardt algorithm, the most influential of the factors were determined. Regression analysis are used to predict the robot coating efficiency and ANOVA analysis are used to contribute the individual process parameter on robot deposition coating efficiency. The developed mathematical model was further analyzed with Genetic algorithm to find out the optimum conditions leading to the maximum coating efficiency

Prediction of performance parameters in Wire EDM of HcHcr steel using Artificial Neural Network

Electrical discharge machining has been extensively used for cutting intricate contours or delicate cavities that would be difficult to produce with a conventional machining methods or tools. Wire EDM is in use for a long time for cutting punches and dies, shaped pockets and other complex shaped parts. Performance of the process is mainly depends on many parameters used during process. Machining input parameters provided by the machine tool builder cannot always meet the operator’s requirements. So, artificial neural network is introduced as an efficient approach to predict the values of performance parameters. In the present research, experimental investigations have been conducted to develop predictive models for the effect of input parameters on the responses such as Material Removal Rate, surface finish and kerf width. Material tested was HcHcr steel material. Molybdenum wires of diameters 0.18 mm were used for the WEDM machine. A feed forward back propagation artificial neural network (ANN) is used to model the influence of current, pulse-ON and pulse-OFF time on material removal rate, kerf width & surface roughness. Multilayer perception model has been constructed with feed forward back propagation algorithm using peak current, pulse-ON and pulse-OFF time as input parameters and MRR and surface roughness and kerf width as the output parameters. The predicted results based on the ANN model are found to be in very close agreement with the unexposed experimental data set. The modeling results confirm the feasibility of the ANN and its good correlation with the experimental results

Selection of parameters for advanced machining processes using firefly algorithm

AbstractAdvanced machining processes (AMPs) are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM) and abrasive water jet machining (AWJM) are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA) is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70gm/min to 181.6723gm/min, while “Ra” and “REWR” are decreased from 6.21μm to 3.6767μm and 6.21% to 6.324×10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858mm to 0.3704mm and 5.41mm to 4.443mm respectively. In both the processes, the obtained results show a significant improvement in the responses

Understanding the Mechanism of Abrasive-Based Finishing Processes Using Mathematical Modeling and Numerical Simulation

Recent advances in technology and refinement of available computational resources paved the way for the extensive use of computers to model and simulate complex real-world problems difficult to solve analytically. The appeal of simulations lies in the ability to predict the significance of a change to the system under study. The simulated results can be of great benefit in predicting various behaviors, such as the wind pattern in a particular region, the ability of a material to withstand a dynamic load, or even the behavior of a workpiece under a particular type of machining. This paper deals with the mathematical modeling and simulation techniques used in abrasive-based machining processes such as abrasive flow machining (AFM), magnetic-based finishing processes, i.e., magnetic abrasive finishing (MAF) process, magnetorheological finishing (MRF) process, and ball-end type magnetorheological finishing process (BEMRF). The paper also aims to highlight the advances and obstacles associated with these techniques and their applications in flow machining. This study contributes the better understanding by examining the available modeling and simulation techniques such as Molecular Dynamic Simulation (MDS), Computational Fluid Dynamics (CFD), Finite Element Method (FEM), Discrete Element Method (DEM), Multivariable Regression Analysis (MVRA), Artificial Neural Network (ANN), Response Surface Analysis (RSA), Stochastic Modeling and Simulation by Data Dependent System (DDS). Among these methods, CFD and FEM can be performed with the available commercial software, while DEM and MDS performed using the computer programming-based platform, i.e., "LAMMPS Molecular Dynamics Simulator," or C, C++, or Python programming, and these methods seem more promising techniques for modeling and simulation of loose abrasive-based machining processes. The other four methods (MVRA, ANN, RSA, and DDS) are experimental and based on statistical approaches that can be used for mathematical modeling of loose abrasive-based machining processes. Additionally, it suggests areas for further investigation and offers a priceless bibliography of earlier studies on the modeling and simulation techniques for abrasive-based machining processes. Researchers studying mathematical modeling of various micro- and nanofinishing techniques for different applications may find this review article to be of great help

A review on conventional and nonconventional machining of SiC particle-reinforced aluminium matrix composites

AbstractAmong the various types of metal matrix composites, SiC particle-reinforced aluminum matrix composites (SiCp/Al) are finding increasing applications in many industrial fields such as aerospace, automotive, and electronics. However, SiCp/Al composites are considered as difficult-to-cut materials due to the hard ceramic reinforcement, which causes severe machinability degradation by increasing cutting tool wear, cutting force, etc. To improve the machinability of SiCp/Al composites, many techniques including conventional and nonconventional machining processes have been employed. The purpose of this study is to evaluate the machining performance of SiCp/Al composites using conventional machining, i.e., turning, milling, drilling, and grinding, and using nonconventional machining, namely electrical discharge machining (EDM), powder mixed EDM, wire EDM, electrochemical machining, and newly developed high-efficiency machining technologies, e.g., blasting erosion arc machining. This research not only presents an overview of the machining aspects of SiCp/Al composites using various processing technologies but also establishes optimization parameters as reference of industry applications

Applications of optimization techniques for parametric analysis of non-traditional machining processes: A Review

The constrained applications of conventional machining processes in generating complex shape ge-ometries with the desired degree of tolerance and surface finish in various advanced engineering materials are being gradually compensated by the non-traditional machining (NTM) processes. These NTM processes usually have higher procurement, maintenance, operating and tooling cost. Hence, in order to attain their maximum machining performance, they are usually operated at their optimal or near optimal parametric settings which can easily be determined by the application of dif-ferent optimization techniques. In this paper, 133 international research papers published during 2012-16 on parametric optimization of NTM processes are extensively reviewed to have an idea on the selected process parameters, observed responses, work materials machined and optimization techniques employed in those processes while generating varying part geometries for their industrial use. It is observed that electro discharge machining is the mostly employed NTM process, applied voltage is the identified process parameter with maximum importance, surface roughness and material removal rate are the two maximally preferred responses, different steel grades are the mostly machined work materials and grey relational analysis is the most popular tool utilized for para-metric optimization of NTM processes. These observations would help the process engineers to attain the machining performance of the NTM processes at their fullest extents for different work material and shape feature combinations

Advanced cutting tools and technologies for drilling carbon fibre reinforced polymer (CFRP) composites: a review

Carbon fibre reinforced polymer (CFRP) composites have excellent specific mechanical properties, these materials are therefore widely used in high-tech industries like the automobile and aerospace sectors. The mechanical machining of CFRP composites is often necessary to meet dimensional or assembly-related requirements; however, the machining of these materials is difficult. In an attempt to explore this issue, the main objective of the present paper is to review those advanced cutting tools and technologies that are used for drilling carbon fibre reinforced polymer composites. In this context, this paper gives a detailed review and discussion of the following: (i) the machinability of CFRP including chip removal mechanisms, cutting force, tool wear, surface roughness, delamination and the characteristics of uncut fibres; (ii) cutting tool requirements for CFRP machining; and (iii) recent industrial solutions: advanced edge geometries of cutting tools, coatings and technologies. In conclusion, it can be stated that advanced geometry cutting tools are often necessary in order to effectively and appropriately machine required quality features when working with CFRP composites.publishe

Comparison between the machinability of different titanium alloys (Ti-6Al-4V and Ti-6Al-7Nb) employing the multi-objective optimization

Titanium and its alloys are amongst the most important metallic materials used by many industries, such as those pertaining to the aerospace, automotive, and biomedical sectors. This is due to the reliability and functionality of titanium components, in addition to their high strength-to-weight ratio and corrosion resistance. Thus, titanium and its alloys are of great importance to the challenging operations of these sectors. The manufacturing of titanium requires great accuracy to ensure that resulting products meet quality requirements, due to its difficult machinability. In this study, the cutting forces and surface roughness of the turning were analysed to compare different titanium alloys, Ti–6Al–4V and Ti–6Al–7Nb, with CVD-coated and uncoated inserts. The effect of control factors on the response variables was measured using ANOVA. Response surface methodology was applied to the creation of a model of responses and to a bi-objective optimization process via the normalized normal constraint method. The Pareto-optimal sets of both alloys were achieved, which may be applied to practical situations to achieve optimal results for these responses. The models and optimization results confirmed the similarity of machinability values between the Ti–6Al–4 V and Ti–6Al–7Nb alloys. The uncoated inserts yielded the best surface roughness and cutting force results when used with both titanium alloys.publishe