Performance analysis of coated carbide tool in turning of Nimonic 80A superalloy under different cutting environments

Inconel 718 (IN-718) has excellent mechanical and chemical properties at high temperature, which has been widely applied in aerospace, ocean, and chemical industries. Meanwhile, the super high temperature properties lead to its poor machinability and machined surface integrity. Good machinability and surface integrity of IN-718 have always been challenging topics in the field of mechanical machining. The machinability and surface integrity of IN-718 was reviewed in this paper. The influences of chemical elements, microstructure, cutting parameters, and cutting environments on machinability were discussed. Cutting force, specific energy, and tool wear were analyzed as the main factors of machinability evaluation index. The characteristics of machined surface integrity including surface morphology (such as surface defects and surface roughness), microstructure modifications (such as plastic deformation, grain size, and white layer) and mechanical properties (such as microhardness and residual stress) related to IN-718 were presented. The state-of-the-art of machining IN-718 showed that most current research focused on its poor machinability and surface integrity in the aspect of material characteristics. There is lack of research on machinability from the viewpoint of geometrical structure of the part to be machined. Therefore, it is important to further study the manufacturability (the difficulty of the process from material to part) of IN-718 components with complex geometrical surface and profiles to achieve shape control and precise design.

Recent progress of machinability and surface integrity for mechanical machining Inconel 718: a review

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Advanced nickel nanoparticles technology: From synthesis to applications

State of the art of tool texturing in machining

Polymer matrix composites in 3D printing: A state of art review

Machinability of Nickel Based Superalloys: A Review

Adaptive neuro fuzzy model development for prediction of cutting forces in milling with rotary tools

A significant shift towards autonomous and connected cars is currently expected in the automotive sector. This creates new risks in terms of privacy and the complexity of electrical and electronic systems. Although safety risk management is still in its infancy, it is encouraging that the auto industry has well-known and regulated safety risk management practices. They propose a showed improved risk assessment framework and show its validity with an actual use-case to make it easier to derive safety procedures & safety solutions for automotive integrated devices. The adequacy of functional safety to existing methods and accessibility for non-safety professionals are some of the important aspects. Risk analysis is an essential element of the methodology used to assess properties and capacity risks. The level of hazard and the level of effect are estimated in the next stage of the risk management process. This step uses several existing guidelines and requirements to make amendments as necessary. To create high-level security objectives, a level of security has finally been determined. This architecture should be well adapted to the requirements of the automobile industry due to its excellent convergence with current principles and specifications.

A Safety Assessment Model for Automotive Embedded Systems Networks

Current work aims to modify process variables for steel production by using an Artificial Neural Network (ANN) based architecture and an integrated optimization module. The concept was originally designed to be used throughout the creation of inorganic nanoparticles in laser ablation processors. It was originally conceived for adjusting the control of material manufacturing processes to achieve the desired outputs. Further research has resulted in more universal techniques that can be used in a wide variety of different manufacturing and treatment scenarios. Based on the knowledge of the experts and the built system, an example of optimization of process conditions in a coherent steel casting was provided. More progress has been made in modeling the whole production line of the steelworks to mold process. The frameworks were stages of growth results are presented in the framework used to conduct certain statistical surveys.

Computation Approach in Steel Manufacturing Industries Through Artificial Neural Network

In the metal cutting process, the cutting temperature is a serious problem in the aerospace industry while machining of aerospace materials like nickel-based superalloys, titanium alloys, and structural composites. The cutting temperature plays an important role in metal cutting processes. The cutting temperature influences the tool life and surface roughness. Many researchers are focusing on the reduction of the cutting temperature. There are more techniques available to reduce the cutting temperature like flood coolant, minimum quantity lubrication (MQL) and nanocoolants. In this work, the cutting temperature was reduced by a new technique that is the self-propelled rotary tool (SPRT) with MQL and nanocoolants. The cutting temperature was measured using the infrared thermometer. The effect of cutting parameters on the cutting temperature was studied. The effect of the inclination angle in rotary milling was also analyzed. The results were obtained while machining of Inconel 625, a nickel-based superalloy using a self-propelled rotary face milling tool.

Gopala Rao Thellaputta

Papers

Machinability of Nickel Based Superalloys: A Review

Adaptive neuro fuzzy model development for prediction of cutting forces in milling with rotary tools

A Safety Assessment Model for Automotive Embedded Systems Networks

Computation Approach in Steel Manufacturing Industries Through Artificial Neural Network

Effect of Machining Variables on Cutting Temperature While Rotary Milling of Inconel 625