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Humping is a common defect in direct energy deposition processes that reduces the geometric integrity of printed products. The available literature on humping detection is deemed reactive, as they focus on detecting late-stage melt pool spatial abnormalities. Therefore, this work introduces a novel, proactive indicator designed to detect early-stage spatiotemporal abnormalities. Specifically, the proposed indicator monitors the variability of instantaneous melt pool solidification-front speed (VIMPS). The solidification front dynamics quantify the intensity of cyclic melt pool elongation induced by early-stage humping. VIMPS tracks the solidification front dynamics based on the variance in the melt pool infrared radiations. Qualitative and quantitive analysis of the collected infrared data confirms VIMPS’s utility in reflecting the intricate humping-induced dynamics and defects. Experimental results proved VIMPS’ proactivity. By capturing early spatiotemporal abnormalities, VIMPS predicted humping by up to 10 s before any significant geometric defects. In contrast, current spatial abnormality-based methods failed to detect humping until 20 s after significant geometric defects had occurred. VIMPS’ proactive detection capabilities enable effective direct energy deposition control, boosting the process’s productivity and quality.

The fifth Industrial revolution (I5.0) prioritizes resilience and sustainability, integrating cognitive cyber-physical systems and advanced technologies to enhance machining processes. Numerous research studies have been conducted to optimize machining operations by identifying and reducing sources of uncertainty and estimating the optimal cutting parameters. Virtual modeling and Tool Condition Monitoring (TCM) methodologies have been developed to assess the cutting states during machining processes. With a precise estimation of cutting states, the safety margin necessary to deal with uncertainties can be reduced, resulting in improved process productivity. This paper reviews the recent advances in high-performance machining systems, with a focus on cyber-physical models developed for the cutting operation of difficult-to-cut materials using cemented carbide tools. An overview of the literature and background on the advances in offline and online process optimization approaches are presented. Process optimization objectives such as tool life utilization, dynamic stability, enhanced productivity, improved machined part quality, reduced energy consumption, and carbon emissions are independently investigated for these offline and online optimization methods. Addressing the critical objectives and constraints prevalent in industrial applications, this paper explores the challenges and opportunities inherent to developing a robust cyber–physical optimization system.

Tool Condition Monitoring systems are essential to achieve the desired industrial competitive advantage in terms of reducing costs, increasing productivity, improving quality, and preventing machined part damage. A sudden tool failure is analytically unpredictable due to the high dynamics of the machining process in the industrial environment. Therefore, a system for detecting and preventing sudden tool failures was developed for real-time implementation. A discrete wavelet transform lifting scheme (DWT) was developed to extract a time-frequency representation of the AErms signals. A long short-term memory (LSTM) autoencoder was developed to compress and reconstruct the DWT features. The variations between the reconstructed and the original DWT representations due to the induced acoustic emissions (AE) waves during unstable crack propagation were used as a prefailure indicator. Based on the statistics of the LSTM autoencoder training process, a threshold was defined to detect tool prefailure regardless of the cutting conditions. Experimental validation results demonstrated the ability of the developed approach to accurately predict sudden tool failures before they occur and allow enough time to take corrective action to protect the machined part. The developed approach overcomes the limitations of the prefailure detection approach available in the literature in terms of defining a threshold function and sensitivity to chip adhesion-separation phenomenon during the machining of hard-to-cut materials.

In this paper, the tool wear mechanisms for low-frequency vibration-assisted drilling (LF-VAD) of carbon fiber-reinforced polymer (CFRP)/Ti6Al4V stacks are investigated at various machining parameters. Based on the kinematics analysis, the effect of vibration amplitude on the chip formation, uncut chip thickness, chip radian, and axial velocity are examined. Subsequently, the effect of LF-VAD on the cutting temperature, tool wear, delamination, and geometrical accuracy was evaluated for different vibration amplitudes. The LF-VAD with the utilization of minimum quantity lubricant (MQL) resulted in a successful drilling process of 50 holes, with a 63% reduction in the cutting temperature. For the rake face, LF-VAD reduced the adhered height of Ti6Al4V by 80% at the low cutting speed and reduced the crater depth by 33% at the high cutting speed. On the other hand, LF-VAD reduced the flank wear land by 53%. Furthermore, LF-VAD showed a significant enhancement on the CFRP delamination, geometrical accuracy, and burr formation.

The use of lightweight material such as CFRP/Ti6Al4V in stacked structures in the aerospace industry is associated with improved physical and mechanical characteristics. The drilling process of nonuniform structures plays a significant role prior to the assembly operation. However, this drilling process is typically associated with unacceptable CFRP delamination, hole accuracy, and high tool wear. These machining difficulties are attributed to high thermal load and poor chip evacuation mechanism. Low-frequency vibration-assisted drilling (LF-VAD) is an advanced manufacturing technique where the dynamic change of the uncut chip thickness is used to manipulate the cutting energy. An efficient chip evacuation mechanism was achieved through axial tool oscillation. This study investigates the effect of vibration-assisted drilling machining parameters on tool wear mechanisms. The paper also presents the effect of tool wear progression on drilled hole quality. Hole quality is described by CFRP entry and exit delamination and hole accuracy. The results showed a significant reduction in the thrust force, cutting torque, cutting temperature, and flank wear-land.

Carbon fiber-reinforced polymers (CFRP) are widely used in the aerospace industry. A new generation of aircraft is being built using CFRP for up to 50% of their total weight, to achieve higher performance. Exit delamination and surface integrity are significant challenges reported during conventional drilling. Exit delamination influences the mechanical properties of machined parts and, consequently, reduces fatigue life. Vibration-assisted drilling (VAD) has much potential to overcome these challenges. This study is aimed at investigating exit delamination and geometrical accuracy during VAD at both low- and high-frequency ranges. The kinematics of VAD are used to investigate the relationship between the input parameters (cutting speed, feed, vibration frequency, and amplitude) and the uncut chip thickness. Exit delamination and geometrical accuracy are then evaluated in terms of mechanical and thermal load. The results show a 31% reduction in cutting temperature, as well as a significant enhancement in exit delamination, by using the VAD technology.