I. Cambero

Metrology of Surfaces Applied to the Electrochemical Polishing Process

This study presents an integrated approach to the teaching of surface metrology and the regulation of electrochemical polishing process control parameters. The electropolishing processes permits different ranges of surface finish through different combinations of the process parameters of current density (J) and electropolishing time (t), and students must have a sound knowledge of the procedure for selecting filters (λs, λc, λf). Thus, experimental trials are undertaken to establish the influence of current density (J) and electropolishing time (t) on surface finish by measuring the arithmetic average roughness parameter (Ra), and setting the filters and measurement procedure for each range of surface finish. The integrated learning of both disciplines enables students to consolidate their knowledge on the methodology for measuring surface roughness (Ra), and to establish direct correlations between variation in process control parameters and the surface finish obtained by characterizing the behaviour of the process.

Application of Techniques Based on Reliability for the Improvement of the Teaching of Mechanisms in Industrial Design

The aim of this study is to analyse the importance of promoting the teaching of reliability of mechanisms in teaching degree in industrial design. An assessment is made of the need for these features provided from the earliest stages of product design, as this allows for a higher quality and economy. The industrial design aims to create a manufacture, device, machine or useful system that is efficient, safe and practical. This is only possible if the correct definition of the functions and design specifications, where an important aspect is the reliability of the designed object, something that should be thoroughly detailed in the conceptual design stage. The experiment was carried out with students in the degree of Industrial Design and Product Development at the University of Extremadura (Spain).

A Tool Wear Monitoring System for Steel and Aluminium Alloys Based on the same Sensor Signals and Decision Strategy

This paper presents a tool wear monitoring system that uses the same signals and prediction strategy for monitoring the machining process of different materials, i.e., a steel and an aluminium alloy. It is an important requirement for a monitoring system to be applied in real applications. Experiments have been performed on a lathe over a range of different cutting conditions, and TiN coated tools were used. The monitoring signals used are the AC feed drive motor current and the cutting vibrations. The geometry tool parameters used as inputs are the tool angle and the radius. The performance of the proposed system was validated against different experiments. In particular, different tests were performed using different numbers of experiments obtaining a rmse for tool wear estimation of 17.63 μm and 13.45 μm for steel and aluminium alloys respectively.

Flank Wear and Surface Roughness Estimation in Steel Turning

This paper presents a method to detect tool wear and surface roughness during steel dry turning. It is important to note that the objective of the proposed method is to fulfill the needs in the development of these monitoring systems according with the research community in this area, and they are: 1) A trade-off between the number of sensors used and their cost, and the performance of the monitoring system, 2) A sufficiently reduced computing time that allows to change the tool before the wear exceeds the fixed threshold and/or stop the machining process before the surface roughness exceeds its threshold, and 3) The use of sensors that do not disturb the machining process. The monitoring signals are the feed motor current and the vibrations. The results show that the proposed system can be used for this purpose due to its high accuracy and reliability.

Surface Roughness Obtained With And Without The Use Of A Mechanical Spindle Speeder In Milling: A Comparative Study

In this paper a comparative study between the surface roughness obtained in a milling process and in the same process using a mechanical spindle speeder with aluminum workpieces is presented. Mechanical spindle speeders are one of the cheapest solutions to upgrade a conventional machine tool to high‐speed machining. The two machining processes were made under the same cutting conditions, covering a wide range of the cutting conditions with all the experiments. The surface roughness (Ra) is the value measured to obtain the comparative between the two machining operations. It must be noted that when the cutting speed is low (less than 6000 rpm), independently of the feed rate, the surface roughness obtained with the spindle speeder is higher. Nevertheless, the surface roughness is less at higher values of the tool cutting speed when the spindle speeder is used for machining. As result, the optimal cutting conditions for the use of mechanical spindle speeders are determined.

A Time‐Domain Modal Parameter Identification for CNC Machine Tools Using Singular Spectrum Analysis (SSA)

Modal parameter identification of machine tool axis drives is conventionally achieved by first obtaining frequency response curves from experimental data and then implementing a curve fitting procedure. The principal shortcomings of this procedure are the inherent limitations of the Fourier Transform method, used in obtaining the frequency responses, especially when non‐stationary signals appear. As an alternative to the conventional method, a new time‐domain modal parameter identification technique based in Singular Spectrum Analysis (SSA) is proposed and applied to CNC machine tool axis drive using output‐data only.

Feature Selection for Tool Condition Monitoring in Turning Processes

The aim of the present work is to develop a tool condition monitoring system (TCMS) using sensor fusion and artificial neural networks. Particular attention is paid to the manner in which the most correlated features with tool wear are selected. Experimental results show that the proposed system can reliably detect tool condition in turning operations and is viable for industrial applications. This study leads to the conclusion that the vibration in the feed direction and the motor current signals are best suited for the development of a TCMS than the sound signal, which should be used as an additional signal.