Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0
Abstract
:1. Introduction
2. Experimental Procedure
2.1. Test Materials
2.2. Cutting Tool
2.3. Equipment and Machining Conditions
3. Results
3.1. Cutting Resistance
3.2. Dynamic Characteristics of the System
3.3. Analysis of Short-Time Fourier Transform Signals
- The vibrating length of the drill in the transverse and axial directions does not change during the drilling process, thus maintaining a rather constant frequency,
- Natural frequencies in the lateral and axial directions of the MHC system during drilling are essentially insensitive to the drill diameter, which simplifies monitoring for a wide range of drill sizes,
- The vibration in the X, Y and Z directions is influenced by the cutting torque and thrust force, which are the main sources of excitation during drilling.
3.3.1. Signal Analysis in the Time Domain
3.3.2. Vibration Spectra in the Transverse X and Y Directions
3.3.3. Vibration Spectra in the Z Direction
3.4. Permutation Entropy
4. Conclusions
- Identification of the material being processed during the drilling process is possible based on both cutting force Fc, cutting torque Mc and acceleration signals.
- Identification based on the cutting force and cutting torque signals is based on the value of the unit cutting resistance kc1.1 and on the basis of the change in the value of PermEn Hp.
- Identification based on STFT analysis of the acceleration signals in specific directions X, Y and Z uses the assessment of the dominant frequency amplitudes depending on the material being processed. There is no need to know the signal history to be able to identify the processed material.
- This article presents the usefulness of the cutting torque signal in the drilling process of wood-based materials, with the aim of identifying the material during the cutting process. As expected, the unit cutting resistance kc1.1 varied in value depending on the type of material being processed, which allowed for a clear distinction between materials. The results show that the proposed methodology can be used as an intelligent technique in the drilling process to identify machined materials.
- Additionally, the applied material identification based on changes in the PermEn Hp value of the cutting force Fc signal during the drilling process worked reliably for all processed materials analyzed. This measure turned out to be insensitive to the combinations of drilling parameters used in the investigations. The proposed method enables the reliable detection of tool contact with the workpiece material and identification of the material during the drilling process.
- There are no clearly visible differences in the recorded vibroacoustic signals in the time domain when changing the processed material. However, after transforming the signal into the frequency domain, characteristic frequency ranges with dominant amplitude can be observed depending on the material being processed.
- A methodology is proposed that can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool. Typically, in the woodworking industry, processing parameters are selected to correspond to the most difficult to process material in stacks at runtime, which increases cutting time, efficiency and processing quality. Intelligent machining techniques contribute to the real-time adaption of cutting parameters to the identified material.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Test Material | Density (kg/m3) | Bending Strength (MPa) | Elasticity Modulus (MPa) | Janka Hardness (N) |
---|---|---|---|---|
Plywood board | 650 (32) | 85 (9) | 5600 (261) | 3228 (189) |
MDF | 730 (23) | 38 (4) | 2530 (176) | 4932 (245) |
HPL | 1470 (29) | 110 (13) | 9250 (349) | - |
Chipboard | 690 (19) | 12 (3) | 1850 (138) | 2367 (153) |
Cutting Speed vc (m/min) | Feed Per Tooth fz (mm) | Feed Rate vf (mm/min) | Rotational Speed of Tool n (rpm) |
---|---|---|---|
78 | 0.15 | 375 | 2500 |
0.20 | 450 | ||
0.25 | 525 | ||
94 | 0.15 | 500 | 3000 |
0.20 | 600 | ||
0.25 | 700 | ||
109 | 0.15 | 625 | 3500 |
0.20 | 750 | ||
0.25 | 875 |
Thickness of the Cutting Layer b (mm) | Feed Per Tooth fz (mm) | Feed Rate vf (mm/min) |
---|---|---|
0.05 | 0.07 | 177 |
0.10 | 0.14 | 354 |
0.15 | 0.21 | 530 |
0.20 | 0.28 | 707 |
0.25 | 0.35 | 884 |
0.30 | 0.42 | 1061 |
0.35 | 0.49 | 1237 |
Test Material | Thickness of the Cutting Layer (mm) | ||||||
0.05 | 0.10 | 0.15 | 0.20 | 0.25 | 0.30 | 0.35 | |
Cutting torque (Nm) | |||||||
Plywood board | 0.21 | 0.32 | 0.28 | 0.40 | 0.47 | 0.66 | 0.71 |
MDF | 0.08 | 0.12 | 0.15 | 0.17 | 0.20 | 0.24 | 0.28 |
HPL | 0.45 | 0.62 | 0.80 | 0.87 | 0.99 | 1.08 | 1.18 |
Chipboard | 0.11 | 0.15 | 0.22 | 0.23 | 0.26 | 0.31 | 0.33 |
Workpiece Material | kc1.1 (N/mm2) | mc |
---|---|---|
MDF | 56 | −0.42 |
Chipboard | 72 | −0.44 |
Plywood board | 138 | −0.43 |
HPL | 248 | −0.52 |
Test Material | Hp |
---|---|
MDF | 0.32 |
Chipboard | 0.53 |
Plywood board | 0.48 |
HPL | 0.21 |
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Szwajka, K.; Zielińska-Szwajka, J.; Trzepieciński, T. Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0. Materials 2024, 17, 2033. https://doi.org/10.3390/ma17092033
Szwajka K, Zielińska-Szwajka J, Trzepieciński T. Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0. Materials. 2024; 17(9):2033. https://doi.org/10.3390/ma17092033
Chicago/Turabian StyleSzwajka, Krzysztof, Joanna Zielińska-Szwajka, and Tomasz Trzepieciński. 2024. "Experimental Analysis of Smart Drilling for the Furniture Industry in the Era of Industry 4.0" Materials 17, no. 9: 2033. https://doi.org/10.3390/ma17092033