Kurumun Atıf Alan Makalesi
Atıf Alan Yayın
A comprehensive review of machinability of difficult-to-machine alloys with advanced lubricating and cooling techniques
Scopus
Open Access Toplam 124 atıf DOI
Superalloys find widespread application in demanding environments owing to their robust strength and high resistance to heat and corrosion. Nonetheless, these very attributes render them difficult-to-cut materials. Enhancing machinability necessitates effective lubrication and cooling techniques, the efficacy of which varies depending on the specific alloy, manufacturing methodology, and machining conditions. This exhaustive review presents a fresh analysis elucidating the impact of diverse cooling/lubrication mothods—dry, flood, minimum quantity lubrication (MQL), cryogenic, and high-pressure cooling—on the machining of titanium, nickel, and steel-based superalloys fabricated through conventional and additive manufacturing processes. Key machining operations, including turning, milling, drilling, and grinding, are scrutinized. The ramifications of each cooling approach on critical machinability indicators such as surface roughness, cutting forces, tool wear, temperature, and environmental footprint are meticulously assessed through an extensive literature survey. Both conventionally produced and additively manufactured alloys are scrutinized to discern prevailing trends. The findings underscore the absence of a universally optimal technique across all scenarios. MQL and cryogenic methods exhibit notable efficacy in refining surface finish during titanium alloy machining. High-pressure cooling augments chip breakability and prolongs tool life in titanium machining, albeit yielding disparate outcomes in nickel alloy machining contingent upon wear mode. Additively manufactured alloys generally exhibit superior machinability compared to their wrought counterparts, although warranting further investigation. This holistic analysis furnishes fresh insights into aligning cooling strategies with alloy-process amalgamations to optimize machinability. It identifies extant challenges and avenues for advancing sustainable and efficient machining of difficult-to-cut materials.
Atıf Kaynağı
Atıf Yapan Yayın
Characterizing Machining Indicators with Machine Learning Models Under Cellulose Nanocrystal and Graphene-Based Nanofluid Conditions
Scopus
Havuzumuzda 4 atıf almış
With outstanding physical properties such as superior ductility and strength, ultra-high strength steels (UHSS) have recently been broadly preferred as industrial materials. In this context, this study investigates the machinability of UHSS S1100 material under different cooling/lubricating conditions. The efficacy of environmentally friendly cooling/lubricating techniques, namely dry, MQL and nanofluid cellulose nanocrystal and graphene nanoplatelets-based MQL, was investigated with different cutting parameters. This novel study evaluated the influence of machining conditions and parameters on responses such as tool wear, surface roughness, energy consumption, cutting temperatures and chip morphology while incorporating machine learning. In addition, correlation analysis was performed with machine learning and the relationships between input and output parameters were evaluated. Lubricating methods such as pure MQL, cellulose nanocrystal and graphene nanoplatelets-based nanofluid are pivotal in heat transfer management and decrease cutting temperatures, tool wear and energy consumption. NGPN-based nanofluid and pure MQL environments at low feed rates and high cutting speeds resulted in the best surface quality. This work provides important insights into the machinability improvement of UHSS S1100 material implementing nanofluids and machine learning models.
Atıf Yapan Makale Bilgileri
Kurumlar (2)
Nirma University, Institute of Technology
Ahmedabad, India
Selçuk Üniversitesi
Selçuklu, Turkey