https://escholarship.org/content/qt4h56k1ch/qt4h56k1ch.pdf

Advanced monitoring of machining operations

Titanium and nickel alloys represent a significant metal portion of the aircraft structural and engine components. When these critical structural components in aerospace industry are manufactured with the objective to reach high reliability levels, surface integrity is one of the most relevant parameters used for evaluating the quality of finish machined surfaces. The residual stresses and surface alteration (white etch layer and depth of work hardening) induced by machining of titanium alloys and nickel-based alloys are very critical due to safety and sustainability concerns. This review paper provides an overview of machining induced surface integrity in titanium and nickel alloys. There are many different types of surface integrity problems reported in literature, and among these, residual stresses, white layer and work hardening layers, as well as microstructural alterations can be studied in order to improve surface qualities of end products. Many parameters affect the surface quality of workpieces, and cutting speed, feed rate, depth of cut, tool geometry and preparation, tool wear, and workpiece properties are among the most important ones worth to investigate. Experimental and empirical studies as well as analytical and Finite Element modeling based approaches are offered in order to better understand machining induced surface integrity. In the current state-of-the-art however, a comprehensive and systematic modeling approach based on the process physics and applicable to the industrial processes is still missing. It is concluded that further modeling studies are needed to create predictive physics-based models that is in good agreement with reliable experiments, while explaining the effects of many parameters, for machining of titanium alloys and nickel-based alloys.

Machining induced surface integrity in titanium and nickel alloys: A review

An overview of the machinability of aeroengine alloys

Achieving sustainability in manufacturing requires a holistic view spanning not just the product, and the manufacturing processes involved in its fabrication, but also the entire supply chain, including the manufacturing systems across multiple product life-cycles. This requires improved models, metrics for sustainability evaluation, and optimization techniques at the product, process, and system levels. This paper presents an overview of recent trends and new concepts in the development of sustainable products, processes and systems. In particular, recent trends in developing improved sustainability scoring methods for products and processes, and predictive models and optimization techniques for sustainable manufacturing processes, focusing on dry, near-dry and cryogenic machining as examples, are presented.

Sustainable manufacturing: Modeling and optimization challenges at the product, process and system levels

Surface integrity in material removal processes: Recent advances

Density and mechanical properties in selective laser melting of Invar 36 and stainless steel 316L

Microscale orthogonal cutting tests were conducted on normalized AISI 1045 steel and the resulting chips examined using optical and Scanning Electron Microscopy (SEM). As the uncut chip thickness approaches the size of the smallest average grain type in the material, chip formation changes from continuous to a new type of chip called a quasi-shear-extrusion chip. Results indicate that the pearlite and softer ferrite grains play distinct roles in the plastic deformation process. A Finite Element (FE) model was developed to illustrate the behaviour of the chip formation process during microscale cutting of alternating hard and soft layers of material. The FE model is not an optimization tool, but simply an aid in understanding the mechanics of the microcutting process. The resulting chip morphology is compared to the FE model, and discussed. Stick–slip friction observed at the tool–chip interface is found to affect the transition between shearing and ploughing during the cutting process, chip curl, and the plastic deformation process throughout the chip. The ramifications of the results and model predictions are presented.

Chip formation during microscale cutting of a medium carbon steel

Invar 36 has gained considerable popularity in many industries, including the aerospace industry, because of its low coefficient of thermal expansion. In this paper, a brief overview for the research needs in metal additive manufacturing is presented. A thorough study for the influence of process parameters on the quality of the parts produced is presented. This study is beneficial for the long-term growth of the additive manufacturing industry. The paper aims to select the process parameters that can be used to fabricate dense parts from Invar 36 (UNS K93600) using the selective laser melting process. In this research, a group of cubes was fabricated using different process parameters from Invar 36 powder using a selective laser melting machine. The density, microstructures, and surface features of these cubes were measured. Experimental observations were drawn from the results of the preliminary analyses. The influence of the process parameters on the density of the parts produced is discussed in this paper.

The selection of process parameters in additive manufacturing for aerospace alloys

This paper presents a systematic study of various monitoring methods suitable,for automated monitoring of manufacturing processes. In general, monitoring is composed of two phases: learning and classification. In the learning phase, the key issue is to establish the relationship between monitoring indices (selected signature, features) and the process conditions. Based on this relationship and the current sensor signals, the process condition is then estimated in the classification phase. The monitoring methods discussed in this paper include pattern recognition, fuzzy systems, decision trees, expert systems and neural networks. A brief review of signal processing techniques commonly used in monitoring, such as statistical analysis, spectral analysis, system modeling, bi-spectral analysis and lime-,frequency distribution, is also included

Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods

Computer simulation of high speed machining processes can provide a unique insight and reduce the number of design iterations required to advance and optimize the process. Predictive modeling of high speed machining of exotic materials has been hindered by the nonlinear behavior of this type of materials at extremely high strain, strain rate, and temperatures. This paper presents a physics-based modeling technology that includes the change in the material constitutive equation and the friction characterization at cutting speeds up to 400 m min−1. The dependence of the accuracy of the predicted parameters, such as the chip formation on cutting forces, chip/tool/workpiece interface temperature, stress and strain distributions are also discussed. The fundamentals of metal cutting were utilized to understand the effect of parameter changes in regimes that are outside current empirical knowledge databases.

Mohamed Elbestawi

Papers

Density and mechanical properties in selective laser melting of Invar 36 and stainless steel 316L

Chip formation during microscale cutting of a medium carbon steel

The selection of process parameters in additive manufacturing for aerospace alloys

Automated Monitoring of Manufacturing Processes, Part 1: Monitoring Methods

Physics-based simulation of high speed machining