Experimental investigation and statistical modelling of cutting temperature with surface and tool morphological analyses in laser-assisted turning of Inconel X-750
Sep 2026· Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering· 31 references
Advanced machining processes and optimization
Abstract
Inconel X-750 is extensively used in aerospace, nuclear and power generation industries because of its excellent mechanical strength and oxidation resistance at elevated temperatures. However, its high strength, work-hardening tendency and poor thermal conductivity make conventional machining difficult. Laser-assisted turning (LAT) has been widely adopted to improve the machinability of hard-to-cut materials. The influence of machining parameters on cutting temperature and the associated changes in surface and tool morphology during LAT of Inconel X-750 have not been systematically investigated. In this study, LAT experiments were performed on Inconel X-750 using a Taguchi L27 orthogonal array by considering laser power, spindle speed, feed rate and depth of cut (DoC) as the machining variables, with cutting temperature selected as the primary response. The experimental data were analysed using analysis of variance and regression analysis to establish the relationship between machining parameters and cutting temperature. Scanning electron microscopy and microhardness measurements were used to characterize the machined surface and cutting tool. Laser power had the greatest influence on cutting temperature, contributing 84.11%, followed by DoC (6.38%). The regression model showed good agreement with the experimental results, with an R 2 value of 97.57%. The validation test confirmed the model with an accuracy of 94.83%. Moderate cutting temperatures produced more uniform surface morphology, whereas excessive temperatures promoted material smearing, adhesion and diffusion-related changes on the cutting tool. LAT reduced the microhardness because of thermal softening, although the hardness remained above the annealed condition of the alloy.
The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.
Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al.· IEEE Transactions on Softwar...· 178 citations· ⚡14
Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.
M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al.· e-Informatica Software Engin...· 157 citations· ⚡17
This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.
Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al.· Empirical Software Engineeri...· 127 citations· ⚡15
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.
Marko Ikonen, Petri Kettunen, Nilay V. Oza et al.· EUROMICRO Conference on Soft...· 67 citations· ⚡9
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduSep 14, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.