Cyber-Physical Systems for High-Performance Machining of Difficult to Cut Materials in I5.0 Era-A Review.

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.

Experimental Investigation on Machinability of α/ß Titanium Alloys with Different Microstructures.

In the current study, Ti-6Al-4V (Ti64) and Ti-6Al-7Nb (Ti67) alloys were prepared by vacuum arc melting. The produced samples were then subjected to different heat treatment regimes. The evolved microstructures and their corresponding hardness were investigated. Computerized drilling tests using TiAlN-coated high-speed steel bits were performed to assess the machinability of the prepared specimen regarding cutting force, tool wear, and thickness of the deformed layer. It was observed that Ti64 specimens that were water quenched from either α/ß or ß range contained martensitic phase. In Ti67, samples showed martensite only when water quenched from the ß-phase range (1070 °C). Formation of martensite resulted in higher hardness and hence led to higher cutting forces and increased tool wear during the drilling process. Machined samples with higher hardness formed a thicker subsurface deformation area (white layer) and increased burr heights. Surface roughness in Ti64 workpieces was generally higher compared to Ti67 specimens. The coat of the drilling bit was partially attacked in the as-cast specimens, which was evident by elemental N on the machining chips. The machining tool deteriorated further by increasing the workpiece hardness through martensitic formation, where elements such as Cr, V, Fe, etc. that came from the tool steel were detected.

A Real-Time Deep Machine Learning Approach for Sudden Tool Failure Prediction and Prevention in Machining Processes.

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.

Influence of Microstructure and Alloy Composition on the Machinability of α/ß Titanium Alloys.

A comparative study was conducted for the machining of two α/ß titanium alloys, namely Ti-6Al-4V (Ti64) and Ti-6Al-7Nb (Ti67), using wire electric discharge machining (WEDM). The influence of cutting speed and cutting mode on the machined surfaces in terms of surface roughness (Ra), recast layer (RL), and micro-hardness have been evaluated. Rough cut (RC) mode at a cutting speed of 50 µm/s resulted in thermal damage; Ra was equal to 5.68 ± 0.44 and 4.52 ± 0.35 µm for Ti64 and Ti67, respectively. Trim-cut mode using seven cuts (TRC-VII) at the same speed decreased the Ra to 1.02 ± 0.20 µm for Ti64 and 0.92 ± 0.10 µm for Ti67. At 100 µm/s, Ra reduced from 2.34 ± 0.28 µm to 0.88 ± 0.12 µm (Ti64), and from 1.42 ± 0.15 µm to 0.90 ± 0.08µm (Ti67) upon changing from TRC-III to TRC-VII. Furthermore, a thick recast layer of 30 ± 0.93 µm for Ti64 and 14 ± 0.68 µm for Ti67 was produced using the rough mode, while TRC-III and TRC-VII modes produced layers of 12 ± 1.31 µm and 5 ± 0.72 µm for Ti64 and Ti67, respectively. Moreover, rough cut and trim cut modes of WEDM played a significant role in promoting the surface hardness of Ti64 and Ti67. By employing the Response Surface Methodology, it was found that the machining mode followed by cutting speed and the interaction between them are the most influential parameters on surface roughness. Finally, mathematical models correlating machining parameters to surface roughness were successfully developed. The results strongly promote the trim-cut mode of WEDM as a promising machining route for two-phase titanium alloys.

Tool Condition Monitoring for High-Performance Machining Systems-A Review.

In the era of the "Industry 4.0" revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems' generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.