Economical and technological study of surface grinding versus face milling in hardened AISI D3 steel machining operations

[EN] This work deals with the technological and economic considerations required to select face milling vs. surface grinding operations in the manufacture of hardened steel flat surfaces for dies and moulds. In terms of technological considerations, factors such as component geometry, material and surface quality (dimensional tolerance and surface finish) are taken into account. The economic considerations include the cost of machine depreciation, labour and consumables (cutting tools in face milling vs. grinding wheels and dressing tool in surface grinding). A case study is presented based on the prismatic components in ceramic tile moulds and their associated manufacturing operations. Surface grinding and face milling experimentation was conducted on cold work steel AISI D3 (with hardness of 60 HRC) with aluminium oxide grinding wheels and coated tungsten carbide cutting tool, respectively. Technological attributes and economics of face milling are compared with surface grinding of this type of mould components. The main conclusion is that face milling with chamfered edge preparation in coated tungsten carbide tools is a competitive process, compared with surface grinding, in terms of product quality and economics.The research team would like to acknowledge the main support of the Caja Castello-Bancaixa Foundation and Universitat Jaume I, which support the project: "Integration of Planning, Execution and Control of High Speed Machining Operations in Collaborative Engineering Environments: Application in Moulds for Tile Industry", the ceramic tile mould company MACER S.L., and would like to extend their gratitude to Roberto Menendez, student of industrial engineering. Particular thanks go to the Programme Alssan: European Union Programme of High Level Scholarships for Latin America (scholarship no. E04D030982MX). Additional support was provided by Tecnologico de Monterrey through the research group in Mechatronics and Intelligent Machines (http://cidyt.mty.itesm/cimec).Vila Pastor, C.; Siller, H.; Rodríguez, C.; Bruscas Bellido, G.; Serrano, J. (2012). Economical and technological study of surface grinding versus face milling in hardened AISI D3 steel machining operations. International Journal of Production Economics. 138(2):273-283. doi:10.1016/j.ijpe.2012.03.028S273283138

Contribution of machining to the fatigue behaviour of metal matrix composites (MMCs) of varying reinforcement size

The high cycle constant stress amplitude fatigue performance of metal matrix composite (MMC) components machined by a milling process was investigated in this study as a function of machining speed, feed rate and reinforcement particle size. The presence of reinforcement and particle size were found to be the most influential factors that affected the fatigue life. In contrast to this, the effect of feed and speed on tool-particle interaction, strain hardening and heat generation during milling of MMCs were balanced in such a way that the contributions of feed and speed on fatigue life were negligible. The interactions of different parameters contributed significantly to the fatigue life which indicated that the modelling of fatigue life based on these three parameters was relatively complex. The fatigue life of the machined MMC samples increased with decreasing particle size and increasing feed. However, the fatigue life was not influenced by speed variation. The presence of smaller or no particles induced a complete separation of failed samples, in contrast to that of specimens containing larger reinforcing particles where crack growth was arrested or deflected by the reinforcing particles

Principles and Characteristics of Different EDM Processes in Machining Tool and Die Steels

Electric discharge machining (EDM) is one of the most efficient manufacturing technologies used in highly accurate processing of all electrically conductive materials irrespective of their mechanical properties. It is a non-contact thermal energy process applied to a wide range of applications, such as in the aerospace, automotive, tools, molds and dies, and surgical implements, especially for the hard-to-cut materials with simple or complex shapes and geometries. Applications to molds, tools, and dies are among the large-scale initial applications of this process. Machining these items is especially difficult as they are made of hard-to-machine materials, they have very complex shapes of high accuracy, and their surface characteristics are sensitive to machining conditions. The review of this kind with an emphasis on tool and die materials is extremely useful to relevant professions, practitioners, and researchers. This review provides an overview of the studies related to EDM with regard to selection of the process, material, and operating parameters, the effect on responses, various process variants, and new techniques adopted to enhance process performance. This paper reviews research studies on the EDM of different grades of tool steel materials. This article (i) pans out the reported literature in a modular manner with a focus on experimental and theoretical studies aimed at improving process performance, including material removal rate, surface quality, and tool wear rate, among others, (ii) examines evaluation models and techniques used to determine process conditions, and (iii) discusses the developments in EDM and outlines the trends for future research. The conclusion section of the article carves out precise highlights and gaps from each section, thus making the article easy to navigate and extremely useful to the related research communit

Prediction of Surface Quality Using Artificial Neural Network for the Green Machining of Inconel 718

Inconel 718 is a nickel-based heat resistant super-alloy (HRSA) that is widely used in many aerospace and automotive applications. It possesses good properties like corrosion resistance, high strength, and exceptional weld-ability but it is considered as one of the most difficult alloys to cut. Recently researchers have focused on employing many machining strategies to improve machinability of Inconel 718. This research work presents the experimentation of wet milling of Inconel 718 using a carbide tool with biodegradable oil. Surface quality is the major aspect of machinability. Hence input parameters such as depth of cut, cutting speed, and feed rate are considered to study their effect on surface quality. Nine experimental runs based on an L9 orthogonal array are performed. Additionally, analysis of variance (ANOVA) is applied to identify the most significant factors among cutting speed, feed rate, and depth of cut. Moreover, this research work presents the Artificial Neural Network (ANN) model for predicting the surface roughness based on experimental results. The ANN based-decision-making model is trained by using acquired experimental values. Visual Gene Developer 2.0 software package is used to study the efficiency of ANN. The presented ANN model demonstrates a very good statistical performance with a high correlation and extremely low error ratio between the actual and predicted values of surface roughness and tool wear

Milling of Inconel 718: an experimental and integrated modeling approach for surface roughness

Inconel 718, a hard-to-cut superalloy is reputed for having poor machining performance due to its low thermal conductivity. Consequently, the surface quality of the machined parts suffers. The surface roughness value must fall within the stringent limits to ensure the functional performance of the components used in aerospace and bioimplant applications. One doable way to enhance its machinability is the adequate dissipation of heat from the machining zone through efficient and ecofriendly cooling environment. With this perspective, an experimental and integrated green-response surface machiningbased- evolutionary optimization (G-RSM-EO) approach is presented during this investigation. The results are compared with two base-line techniques: the traditional flooded approach with Hocut WS 8065 mineral oil, and the dry green approach. A Box-Behnken response surface methodology (RSM) is employed to design the milling tests considering three control parameters, i.e., cutting speed (vs), feed/flute (fz), and axial depth of cut (ap). These control parameters are used in the various experiments conducted during this research work. The parametric analysis is then accomplished through surface plots, and the analysis of variance (ANOVA) is presented to assess the effects of these control parameters. Afterwards, a multiple regression model is developed to identify the parametric relevance of vs, fz, and ap, with surface roughness (SR) as the response attribute. A residual analysis is performed to validate the statistical adequacy of the predicted model. Lastly, the surface roughness regression model is considered as the objective function of the particle swarm optimization (PSO) model to minimize the surface roughness of the machined parts. The optimized SR results are compared to the widely employed genetic algorithm (GA) and RSM-based desirability function approach (DF). The confirmatory machining tests proved that the integrated optimization approach with PSO being an evolutionary technique is more effective compared to GA and DF with respect to accuracy (0.05% error), adequacy, and processing time (3.19 min). Furthermore, the study reveals that the Mecagreen 450 biodegradable oil-enriched flooded strategy has significantly improved the milling of Inconel 718 in terms of eco-sustainability and productivity, i.e., 42.9% cost reduction in cutting fluid consumption and 73.5% improvement in surface quality compared to the traditional flooded approach and the dry green approach. Moreover, the G-RSM-EO approach presents a sustainable alternative by achieving a Ra of 0.3942 μm that is finer than a post-finishing operation used to produce close tolerance reliable components for aerospace industry

Smooth particle hydrodynamics study of surface defect machining for diamond turning of silicon

Acknowledgments The authors would like to thank EPSRC (EP/K018345/1) and Royal Society-NSFC International Exchange Scheme for providing financial support to this research.Peer reviewedPublisher PD

Proceedings of the 4th International Conference on Innovations in Automation and Mechatronics Engineering (ICIAME2018)

The Mechatronics Department (Accredited by National Board of Accreditation, New Delhi, India) of the G H Patel College of Engineering and Technology, Gujarat, India arranged the 4th International Conference on Innovations in Automation and Mechatronics Engineering 2018, (ICIAME 2018) on 2-3 February 2018. The papers presented during the conference were based on Automation, Optimization, Computer Aided Design and Manufacturing, Nanotechnology, Solar Energy etc and are featured in this book

Trochoidal Milling of AlSiCp with CVD Diamond Coated End Mills

Metal matrix composites have seen a rise in demand within the last decade. Aluminum alloy reinforced with silicon carbide particles is a type of particle metal matrix composite that has seen applications in the aerospace, ground transportation, and electronics industry. However, the abrasive SiC particles have made this material difficult to machine through conventional machining strategies. This research will focus on using computer aided manufacturing with trochoidal tool paths to maximize machining productivity and extend the tool life of CVD diamond coated end mills. The focus of this research will be on AlSiCp with a high volume fraction of reinforcement (30%) to expand the potential applications of this pMMC. The cutting experiments are divided into three parts: cutting test, confirmation test, and endurance test. Taguchi method will be used to perform an analysis of variance and signal-to-noise ratio to optimize a combination of material removal rate, average cutting forces, and surface roughness. The optimal cutting conditions were found to be 254 mm/min, 30°, and 9500 r/min for MRR+AvgFxy+Ra, 1524 mm/min, 30°, and 9500 r/min for MRR+AvgFxy, and 1524 mm/min, 90°, and 9500 r/min. The cutting conditions for MRR+AvgFx+Ra was not considered for the endurance tests as the machining productivity was too low to be considered a feasible option in the industry. It was concluded that trochoidal milling under wet cutting conditions produced nearly half the tool wear as previous research with conventional milling strategies. However, the longer the CVD diamond coated end mills were engaged in the AlSiCp workpiece, the more dominant the abrasive wear mechanisms appear and cause tool damage. It was concluded that square end mills may not be suitable for machining AlSiCp and that future research should focus on varying the tool geometry or utilizing ball end mills

Adaptive control optimization in micro-milling of hardened steels-evaluation of optimization approaches

Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high-quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micro-manufacturing. In this paper, an adaptive control optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: (1) a monitoring sensor system composed of a dynamometer, (2) an estimation module with Artificial Neural Networks models, (3) an optimization module with evolutionary optimization algorithms, and (4) a CNC interface module. In order to operate in a nearly real-time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as particle swarm optimization (PSO), genetic algorithms (GA), and simulated annealing (SA) in terms of accuracy, precision, and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3 and 29 % in comparison with conservative and high-production strategies, respectively

Review of current best-practices in machinability evaluation and understanding for improving machining performance

Machinability is a generalized framework that attempts to quantify the response of a workpiece material to mechanical cutting, which has been developed as one of the key factors that drive the final selection of cutting parameters, tools, and coolant applications. Over the years, there are many attempts have been made to develop a standard evaluation method of machinability. However, due to the complexity of the influence factors, i.e., from work material and cutting tool to machine tool, that can affect the materials machinability, currently there is no uniquely defined quantification of machinability. As one of the outcomes from the CIRP's Collaborative Working Group on “Integrated Machining Performance for Assessment of Cutting Tools (IMPACT)”, this paper conducts an extensive study to learn interacting machinability parameters to evaluate the overall machining performance. Specifically, attention is focused on recent advances made towards the determination of the machinability through tool wear, cutting force and temperature, chip form and breakability, as well as the surface integrity. Furthermore, the advanced methods that have been developed over the years to enable the improvement of machinability have been reviewed