To achieve greater productivity, titanium alloy requires cutting at higher speeds (above 100 m min−1) that affects the tool life and energy consumption during the machining process. This research work correlates the wear progression and Specific Cutting Energy (SCE) in turning Ti-6Al-4V alloy using H13 tools (uncoated carbide) in dry conditions from low to high cutting speeds. Cutting condition employed in this study were selected from published wear map developed for titanium (Ti-6Al-4V alloy) with the same tool. Flank wear growth of the tool has been investigated at different length of cuts in correlation with the SCE under different cutting conditions. The useful tool life was found to be shorter at high-speed machining conditions, thus the end of useful tool life criteria (ISO 3685) was reached at a much shorter length of cuts as compared to low-speed machining conditions. The cutting conditions corresponding to high wear rate also resulted in high SCE. Finally, SCE and wear have been related by a linear relationship that can be used to monitor wear and/or SCE utilization during machining. The results help in the selection of appropriate cutting conditions that will enhance the tool life and minimize SCE consumption during machining titanium alloy.

This paper presents the machinability performance of uncoated, single-coated, and multicoated carbide tools during turning of Grade 5 (Ti6Al4V) Titanium alloy, which is challenging to machine due to its distinctive material properties. Coated tools with single-coated Titanium Aluminium Nitride (TiAlN) and multi-coated layer of Titanium Aluminium Nitride with Aluminium Chromium Nitride (TiAlN + AlCrN) coated inserts were utilized to assess surface roughness (Ra), tool wear rate (R), and chip morphologies under various cutting conditions using dry machining. Analysis of the used tools revealed that coated tools exhibited improved tool life and surface quality compared to uncoated tools across all cutting conditions. Multi-coated tools of TiAlN + AlCrN demonstrated a tool life increase of up to 15% compared to uncoated and single-coated tools, with surface roughness improvements ranging from 30 to 45% depending on cutting speed. Chip morphology analysis indicated an increase in the chip reduction coefficient with higher cutting speeds for all tool types. Coated tools exhibited the lowest chip-reduction coefficient due to the presence of TiAlN and AlCrN coatings, which control the tool chip contact length. Conversely, uncoated chip morphology resulted in larger chip thickness values compared to coated tools, particularly at cutting speeds above 100 m/min, attributed to poor heat dissipation and chemical reactions at the tool chip interface. Energy dispersive X-ray scanning electron microscopy (SEM/EDXS) analysis of worn uncoated inserts revealed a higher tendency towards Titanium adhesion compared to coated tools. The proposed multi-layer coatings of (TiAlN + AlCrN) used for dry machining proved highly beneficial for achieving economic machining objectives and may reduce the need for lubrication when processing Ti6Al4V alloys.

Flexible manufacturing systems (FMS) are highly adaptable production systems capable of producing a wide range of products in varying quantities. While this flexibility caters to evolving market demands, it also introduces complex scheduling and control challenges, making it difficult to optimize productivity, quality, and energy efficiency. This paper explores the application of digital twin technology to tackle these challenges and enhance FMS optimization and control. A digital twin, constructed by integrating simulation models, data acquisition, and machine learning algorithms, was employed to replicate the behavior of a real-world FMS. This digital twin enabled real-time dynamic optimization and adaptive control of manufacturing operations, facilitating informed decision making and proactive adjustments to optimize resource utilization and process efficiency. Computational experiments were conducted to evaluate the digital twin implementation on an FMS equipped with robotic material handling, CNC machines, and automated inspection. Results demonstrated that the digital twin significantly improved FMS performance. Productivity was enhanced by 14.53% compared to conventional methods, energy consumption was reduced by 13.9%, and quality was increased by 15.8% through intelligent machine coordination. The dynamic optimization and closed-loop control capabilities of the digital twin significantly improved overall equipment effectiveness. This research highlights the transformative potential of digital twins in smart manufacturing systems, paving the way for enhanced productivity, energy efficiency, and defect reduction. The digital twin paradigm offers valuable capabilities in modeling, prediction, optimization, and control, laying the foundation for next-generation FMS.