Use of cutting force and vibro-acoustic signals in tool wear monitoring based on multiple regression technique for compreg milling

Abstract

This study focused on a computerised TCM (tool condition monitoring) system as a part of automated monitoring of the machining processes in the wood industry. The system’s principal task was to evaluate the actual state of tool wear without disrupting the normal course of machine tool exploitation for cutting force and vibro-acoustic signals analysis. During the experiment, five physical quantities that are generated during machining were measured and recorded: cutting forces in two directions (Fx, Fy), ultrasonic stress waves (acoustic emission – AE), acoustic pressure in the range of audible frequencies (noise – N), and acceleration of mechanical vibrations (V). Six pairs of tools were used in the experiment. One tool from each pair was experimental, the other was a control tool. Out of the five physical quantities generated during machining that were tested as an indirect source of information on the tool condition, signals of cutting forces and mechanical vibrations proved the most useful. Both acoustic emission and noise signals emerged as wholly inadequate as evidence to predict tool wear.

Full Article

Use of Cutting Force and Vibro-acoustic Signals in Tool Wear Monitoring Based on Multiple Regression Technique for Compreg Milling

Jarosław Górski, Karol Szymanowski, Piotr Podziewski,* Katarzyna Śmietańska, Paweł Czarniak, and Mariusz Cyrankowski

This study focused on a computerised TCM (tool condition monitoring) system as a part of automated monitoring of the machining processes in the wood industry. The system’s principal task was to evaluate the actual state of tool wear without disrupting the normal course of machine tool exploitation for cutting force and vibro-acoustic signals analysis. During the experiment, five physical quantities that are generated during machining were measured and recorded: cutting forces in two directions (Fx, Fy), ultrasonic stress waves (acoustic emission – AE), acoustic pressure in the range of audible frequencies (noise – N), and acceleration of mechanical vibrations (V). Six pairs of tools were used in the experiment. One tool from each pair was experimental, the other was a control tool. Out of the five physical quantities generated during machining that were tested as an indirect source of information on the tool condition, signals of cutting forces and mechanical vibrations proved the most useful. Both acoustic emission and noise signals emerged as wholly inadequate as evidence to predict tool wear.

Keywords: Machining; Tool wear; Tool condition monitoring

Contact information: Department of Mechanical Processing of Wood, Warsaw University of Life Sciences WULS-SGGW, Poland; *Corresponding author: piotr_podziewski@sggw.pl

INTRODUCTION

The automation of production processes is becoming a priority in the manufacturing of products made of wood-based materials. Computer numerical control (CNC) machine tools or automated production lines are routinely used for such automation. One of the problems that remains unsolved in this kind of production is the automated monitoring of the machining processes, and tool condition monitoring is a high priority (Iskra and Hernández 2012). It is worth noting that tool wear is a particularly important research topic that it is traditionally given a lot of attention (Szwajka and Trzepieciński 2016, 2017a,b). It is evident that gradual deterioration of the cutting edge results in decreased machining quality and increases the risk of a sudden catastrophic tool failure, which in turn may lead to such consequences as unplanned tool stoppage in problematic circumstances. Woodworking tool wear online measurement is an essential step in improving wood industrial automation (Wei et al. 2018). Therefore, a subject of interest in recent years is the idea of special computerised tool condition monitoring (TCM) systems designed to function in an on-line mode. Their principal task is to evaluate the current state of tool wear without disrupting the normal course of machine tool exploitation. Such systems are typically based on an indirect identification of the cutting edge’s wear on the measurement and analysis of cutting forces or vibro-acoustic signals generated in the cutting zone (Jemielniak et al. 2012). For wood-based materials, advanced scientific research into TCM systems machining has been conducted for years (Lemaster and Jackson 2000a, 2000b). They have chiefly consisted of systematic attempts to determine the most useful signals and their features that would allow for an unequivocal, fast, and reliable identification of the tool condition during the machining process (Wilkowski and Górski 2011; Kurek et al. 2016; Świderski et al. 2017). However, any reports of commercial or at least prototype TCM systems that could be applied in machine tools for wood-based boards are yet to materialise. Designing such systems requires further research using various tools and various wood-based materials.

Under these circumstances, it is advisable to develop a tool wear identification model in relation to the cutting force and vibro-acoustic signals analysis for compreg milling. Compreg is a special processed wood made of veneers impregnated with phenolic resins and compressed to reduce shrinking and swelling as well as to increase density and strength. Compreg is relatively easy to machine and is used for making berths of railway coaches and seats, boxes of heavy equipment, industrial pallets, marine decks and cabins, and many other products (Wilkowski and Górski 2011).

EXPERIMENTAL

Materials

A machining centre CNC (Jet 130; Busellato, Thiene, Italy) was used in the experimental studies. A machine tool was equipped with a single edge cutter head that was 40 mm in diameter (Faba SA, Baboszewo, Poland) with an exchangeable carbide cutting edge KCR08 (Fig. 1)

Fig. 1. General view of the cutter head (a.) and the scheme of the workpiece machining (b.); in the bottom right corner: 3-axis coordinate system used in CNC machine tool

The material used for experimental machining was 20-mm-thick compreg (Sklejka-Pisz Paged Sp. z o.o., Pisz, Poland). Its selected physical and mechanical properties were determined in accordance with valid standards that are shown in Table 1.

Table 1. Physical and Mechanical Parameters of Compreg

The workpieces were specimens made of compreg with measurements of 100 mm × 150 mm. In the experiments, a groove 6-mm-deep and 40-mm-wide was milled in the samples (Fig. 1). The machining was conducted with a rotational spindle speed of 18000 rpm. The feed rate on the cutting edge was 0.15 mm/rev. The above parameters were adopted as recommended by the cutter head manufacturer (Faba SA, Baboszewo, Poland) for the precise milling of wood-based materials. During the experiment, five physical quantities that are generated by machining were measured and recorded: machining forces in two directions, i.e., parallel to the X- and Y-axes defined according to Fig. 1 (F_x, F_y), ultrasonic stress waves (usually called acoustic emission – AE), acoustic pressure in range of audible frequencies (noise – N), and acceleration of mechanical vibrations (V). The measurements were possible with a special experimental setup that is presented in Fig. 2. The machined object was fixed on a platform, and a Kistler 9601 (Winterthur, Switzerland) sensor was installed on the inside of the platform to measure the forces in three directions (as mentioned above, only two of its channels were used to measure the forces F_x and F_y). The output signals from this sensor were transmitted to a Kistler 5036 (Winterthur, Switzerland) amplifier. The noise was measured with a standard B&K 4189 microphone (Nærum, Denmark) (frequency range: 6.6 Hz ÷ 20 kHz) that was placed just below the milling table at a distance of 200 mm from the cutting zone, and a B&K Type 2690-A Nexus microphone conditioner amplifier (Brüel & Kjær, Nærum, Denmark). To measure the vibrations of the platform that served as a jig (a device that holds a piece of work), a Kistler 8141A (Winterthur, Switzerland) accelerometer and a Kistler 5127B (Winterthur, Switzerland) amplifier were used. All four signals (F_x, F_y, N, and V) were sent to a connector box Nr 1, and then digitally recorded via an acquisition card NI PCI-6111 (the frequency of sampling was 50 kHz). To measure and record acoustic emission, a Kistler 8152B contact sensor (frequency range: 50 ÷ 400 kHz), a Kistler 5125B amplifier (Winterthur, Switzerland), a connector box Nr 2, and an NI PCI-6034E acquisition card (Austin, Texas, USA) (with a 2 MHz frequency of sampling) were used. The signal recording was conducted in the NI LabView (National Instruments Corporation, ver. 2015 SP1, Austin, Texas, USA) environment.

Six pairs of tools were used in the experiment. One tool from each pair was experimental, the other was a control tool. The experimental tools (marked with the symbols TE01 ÷ TE06) were gradually worn in a way that reflected normal exploitation in real industrial conditions, i.e., the machining of various wood-based materials without using the platform shown in Fig. 2. At some intervals, the standard tool wear indicator (VB), defined in Fig. 3, was measured by means of a workshop microscope (TM-505; Mitutoyo, Kawasaki, Japan).