Surface profile prediction and analysis applied to turning process

An approach for the prediction of surface profile in turning process using Radial Basis Function (RBF) neural networks is presented. The input parameters of the RBF networks are cutting speed, depth of cut and feed rate. The output parameters are Fast Fourier Transform (FFT) vector of surface profile for the prediction of surface profile. The RBF networks are trained with adaptive optimal training parameters related to cutting parameters and predict surface profile using the corresponding optimal network topology for each new cutting condition. A very good performance of surface profile prediction, in terms of agreement with experimental data, was achieved with high accuracy, low cost and high speed. It is found that the RBF networks have the advantage over Back Propagation (BP) neural networks. Furthermore, a new group of training and testing data were also used to analyse the influence of tool wear and chip formation on prediction accuracy using RBF neural networks

Experimental investigation on micromilling of oxygen-free, high-conductivity copper using tungsten carbide, chemistry vapour deposition and single-crystal diamond micro tools

Insufficient experimental data from various micro tools limit industrial application of the micromilling process. This paper presents an experimental comparative investigation into micromilling of oxygen-free, high-conductivity copper using tungsten carbide (WC), chemistry vapour deposition (CVD) diamond, and single-crystal diamond micromilling tools at a uniform 0.4mm diameter. The experiments were carried out on an ultra-precision micromilling machine that features high dynamic accurate performance, so that the dynamic effect of the machine tool itself on the cutting process can be reduced to a minimum. Micromachined surface roughness and burr height were characterized using white light interferometry, a scanning electron microscope (SEM), and a precision surface profiler. The influence of variation of cutting parameters, including cutting speeds, feedrate, and axial depth of cut, on surface roughness and burr formation were analysed. The experimental results show that there exists an optimum feedrate at which best surface roughness can be achieved. Optical quality surface roughness can be achieved with CVD and natural diamond tools by carefully selecting machining conditions, and surface roughness, Ra, of the order of 10nm can also be obtained when using micromilling using WC tools on the precision micromilling machine.EU FP6 MASMICRO projec

Latest Developments in Industrial Hybrid Machine Tools that Combine Additive and Subtractive Operations

Hybrid machine tools combining additive and subtractive processes have arisen as a solution to increasing manufacture requirements, boosting the potentials of both technologies, while compensating and minimizing their limitations. Nevertheless, the idea of hybrid machines is relatively new and there is a notable lack of knowledge about the implications arisen from their in-practice use. Therefore, the main goal of the present paper is to fill the existing gap, giving an insight into the current advancements and pending tasks of hybrid machines both from an academic and industrial perspective. To that end, the technical-economical potentials and challenges emerging from their use are identified and critically discussed. In addition, the current situation and future perspectives of hybrid machines from the point of view of process planning, monitoring, and inspection are analyzed. On the one hand, it is found that hybrid machines enable a more efficient use of the resources available, as well as the production of previously unattainable complex parts. On the other hand, it is concluded that there are still some technological challenges derived from the interaction of additive and subtractive processes to be overcome (e.g., process planning, decision planning, use of cutting fluids, and need for a post-processing) before a full implantation of hybrid machines is fulfilledSpecial thanks are addressed to the Industry and Competitiveness Spanish Ministry for the support on the DPI2016-79889-R INTEGRADDI project and to the PARADDISE project H2020-IND-CE-2016-17/H2020-FOF-2016 of the European Union's Horizon 2020 research and innovation program

Neural network modelling of Abbott-Firestone roughness parameters in honing processes

In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons.Peer ReviewedPostprint (author's final draft

Investigation of Correlation between Image Features of Machined Surface and Surface Roughness

Alternative approach to surface roughness evaluation is mostly based on the analysis of digital images of machined surfaces i.e. on extracting various features from the matrix mathematically representing a digital image. This paper analyses correlation between 23 different digital image features and the surface roughness for two different materials: aluminium and stainless steel. Machined surfaces for both materials were acquired by face milling. Factorial design 6 × 5 × 2 with two replicates was conducted for each material with cutting parameters being varied on various numbers of levels. Based on the correlation coefficients the results showed that the best ranked features regardless of the machined material were the features based on statistic measures

Measurement of micro burr and slot widths through image processing: Comparison of manual and automated measurements in micro‐milling

In this study, the burr and slot widths formed after the micro‐milling process of Inconel 718 alloy were investigated using a rapid and accurate image processing method. The measurements were obtained using a user‐defined subroutine for image processing. To determine the accuracy of the developed imaging process technique, the automated measurement results were compared against results measured using a manual measurement method. For the cutting experiments, Inconel 718 alloy was machined using several cutting tools with different geometry, such as the helix angle, axial rake angle, and number of cutting edges. The images of the burr and slots were captured using a scanning electron microscope (SEM). The captured images were processed with computer vision software, which was written in C++ programming language and open‐sourced computer library (Open CV). According to the results, it was determined that there is a good correlation between automated and manual measurements of slot and burr widths. The accuracy of the proposed method is above 91%, 98%, and 99% for up milling, down milling, and slot measurements, respectively. The conducted study offers a user‐friendly, fast, and accurate solution using computer vision (CV) technology by requiring only one SEM image as input to characterize slot and burr formation

A multi-sensor based online tool condition monitoring system for milling process

Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications

Performance Evaluation of Minimum Quantity Lubrication (MQL) When Machining High-Performance Materials

The manufacturing sector is among the fastest-growing in today\u27s industrialized world. Manufacturers are concerned about increasing their competitiveness and profitability. Increasing the efficiency and sustainability of manufacturing processes is one way to improve productivity and improve profit margins. Learning about cutting conditions and how they affect machined surfaces and tool life can help improve productivity. Nowadays, the goal is not just to increase productivity but also to make processes more environmentally friendly and cleaner. This research aims to analyze the machinability of difficult-to-cut magnesium alloys through different cooling and lubrication strategies and their impact on the environment. This study conducted controlled machining tests with dry and vegetable oil mist cutting settings to measure surface roughness, tool contact length, chip morphology, and flank wear. The present study provides insight into the cutting performance of coated carbide tools. To improve the machinability of magnesium alloys, the study also investigated tool wear mechanisms, surface roughness, and primary and secondary components of machining, such as effective shear angle, compression ratio, and coefficient of friction. In this study, we found that minimum quantity lubrication (MQL) performed well under various speed ranges for coated tools. Cutting speed and feed rate correlated closely with tool wear, surface roughness, and other output response parameters. MQL-based systems offer great potential to improve the machinability of magnesium alloys, and they should be explored further