Data-driven methods provided smart manufacturing with unprecedented opportunities to facilitate the transition toward Industry 4.0–based production. Machine learning and deep learning play a critical role in developing intelligent systems for descriptive, diagnostic, and predictive analytics for machine tools and process health monitoring. This paper reviews the opportunities and challenges of deep learning (DL) for intelligent machining and tool monitoring. The components of an intelligent monitoring framework are introduced. The main advantages and disadvantages of machine learning (ML) models are presented and compared with those of deep models. The main DL models, including autoencoders, deep belief networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), were discussed, and their applications in intelligent machining and tool condition monitoring were reviewed. The opportunities of data-driven smart manufacturing approach applied to intelligent machining were discussed to be (1) automated feature engineering, (2) handling big data, (3) handling high-dimensional data, (4) avoiding sensor redundancy, (5) optimal sensor fusion, and (6) offering hybrid intelligent models. Finally, the data-driven challenges in smart manufacturing, including the challenges associated with the data size, data nature, model selection, and process uncertainty, were discussed, and the research gaps were outlined.

A review on deep learning in machining and tool monitoring: methods, opportunities, and challenges

Sawing is the most common machining process and is present in both primary and secondary wood transformation sectors. The objective of this paper is to review how it is affected by different factor...

A review on wood machining: characterization, optimization, and monitoring of the sawing process

A methodology is presented for acoustic emission (AE) monitoring of Douglas fir wood in circular sawing process under extreme cutting conditions. An AE sensor was mounted on the saw guide to investigate the blade vibration, the interaction between the saw and workpiece, and the cut surface waviness. The acquired signal was filtered using a wavelet de-noising method. Signal processing was performed in both the time and frequency domains, and different features were extracted. The effects and significance of cutting parameters such as feed speed, rotation speed, and depth of cut on the AE signal were discussed. Both the statistical regression and artificial intelligence approach were employed to correlate the AE-extracted features with the cutting power and waviness. Adaptive neuro-fuzzy inference system (ANFIS) was used to predict the cutting power and waviness. Optimal feature selection was performed by combining the ANFIS with a metaheuristic optimization algorithm. Particle swarm optimization (PSO) method was used to find the AE-selected features that result in a minimum ANFIS model error. ANFIS was trained using both the PSO and a hybrid algorithm. The results indicate the effectiveness of the wavelet de-noising method for signals that are polluted by different low- and high-frequency sources. AE signals were highly influenced by the saw blade deflection and its vibration response. ANFIS modeling showed higher performance than statistical regression for cutting power and waviness prediction. Optimal feature selection improved the model accuracy, which was further enhanced when the optimal sensory features were combined with the studied cutting parameters. The optimal ANFIS performance was achieved when the network was trained using PSO algorithm. The presented methodology can successfully monitor the cut surface waviness.

Acoustic emission monitoring of sawing process: artificial intelligence approach for optimal sensory feature selection

An ANSYS/LS-DYNA simulation and experimental study of circular saw blade cutting system of mulberry cutting machine

A methodology was developed using the sound signal based on wavelet analysis and self-organizing neural network (NN) to monitor cutting accuracy in an extremely noisy machining process. Sawing experiments were conducted under different levels of feed speed, depth of cut, and rotation speed to monitor the longitudinal waviness of sawn samples using laser displacement sensors as an index of cutting accuracy and sawing deviation. The sound of the cutting and idling processes was recorded using a microphone. The acquired acoustic signals were pre-processed using the wavelet de-noising method for background noise elimination. As the signal still encompasses the low-frequency components corresponding to the idling process (machine motor, saw vibration, etc.), a systematic wavelet thresholding method was applied to the coefficients of the decomposed signal at different levels to discard the sound signal components associated with the idling process. Inverse wavelet transform was then applied to make a synthesized signal from the original one. Different features were extracted from the original and synthesized signals and used to train a self-organizing NN. Group method of data handling (GMDH) NN was utilized for predicting the waviness from the sensory features. The GMDH model trained with features extracted from the synthesized signal outperformed the one trained with the original signal features. The results suggested that employing the proposed wavelet-based methodology enables the acoustic signal to be used in monitoring the manufacturing processes in an extremely noisy environment. Self-organizing NN was shown to have a promising performance without facing the difficulties in finding the network optimal architecture, which is a typical challenge in the conventional backpropagation NNs.

Intelligent Machining Monitoring Using Sound Signal Processed With the Wavelet Method and a Self-Organizing Neural Network

This paper investigates the effect of rotation speed and vibration response of a circular saw on the sawing process of Douglas-fir wood. An idling test was conducted on a guided circular saw to det...

The effect of rotation speed on the power consumption and cutting accuracy of guided circular saw: Experimental measurement and analysis of saw critical and flutter speeds

Lack of a monitoring system for guided circular saws marks one of the most critical machines in sawmills as a production bottleneck. Monitoring systems are being researched and developed for machin...

Development of a monitoring system for guided circular saws: an experimental investigation

Vibration Characteristics of Guided Circular Saws: Experimental and Numerical Analyses

“Guided splined disks” are defined as flat thin disks in which the inner radius of the disk is splined and matches a splined arbor that provides the driving torque for rotating the disk. Lateral constraint for the disk is provided by space fixed guide pads. Experimental lateral displacement of run-up tests of such a system is presented, and the flutter instability zones are identified. The results indicate that flutter instability occurs at speeds when a backward travelling wave of a mode meets a reflected wave of a different mode. Sometimes, the system cannot pass a flutter zone, and transverse vibrations of the disk lock into that flutter instability zone. The governing linear equations of transverse motion of such a spinning disk, with assumed free inner and outer boundary conditions, are derived. A lateral constraint is introduced and modeled as a linear spring. Rigid body translational and tilting degrees of freedom are included in the analysis of the total motion of the spinning disk. The eigenvalues of the system are computed numerically, and the flutter instability zones are defined. The results show that the mathematical model can predict accurately the flutter instability zones measured in the experimental tests.

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Flutter Instability Speeds of Guided Splined Disks: An Experimental and Analytical Investigation

In the wood processing industry, splined arbour saws are widely used in the breakdown of logs to boards of varying dimensions. In a splined saw, the inner radius of the blade is splined and matches a splined arbour that provides the driving torque for the saw. Lateral constraint for these saws is provided by space fixed guide pads which constrain the lateral motion of the saw. Experiences indicate that the splined saw is able to operate at speeds in excess of its lowest critical speed. This paper presents analytical and experimental investigations of a guided splined saw. A computer model is developed to consider the idling characteristics of the system. The model considers relevant geometrical details, friction, contact between components, geometrical non-linearity, interaction between the splined arbour and the splined blade. Experimental results are presented that support the validity of the analysis. The experimental results indicate that flutter instability occurs at speeds when a backward tr...

Ahmad Mohammadpanah

Papers

The effect of rotation speed on the power consumption and cutting accuracy of guided circular saw: Experimental measurement and analysis of saw critical and flutter speeds

Development of a monitoring system for guided circular saws: an experimental investigation

Vibration Characteristics of Guided Circular Saws: Experimental and Numerical Analyses

Flutter Instability Speeds of Guided Splined Disks: An Experimental and Analytical Investigation

Maximum operation speed of splined saws