Analysis and modeling of depth-of-cut during end milling of deposited material

This study addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. Online detection of depth-of-cut presents many technical difficulties. Researchers have used various types of sensors and methods to assess the depth-of-cut and surface errors. Due to the strong correlation between acoustic emission and cutting depth during the depth end milling process, it is useful to forecast the depth-of-cut from the acoustic emission signal. This work used regression analysis to model and detect the depth-of-cut. The experiments were carried out on a Fadal vertical 5-Axis computer numerical control machine using a carbide end-mill tool, and a piezoelectric sensor (Kistler 8152B211) was used to acquire the acoustic emission signal. A National Instruments real-time system, combined with a National Instruments LabVIEW graphical development environment, was used as a data acquisition system. A series of experiments were conducted to create a depth-of-cut model. The inputs were used to predict depth-of cut are the identified root mean square of the acoustic emission, spindle speed, feed rate, and tool status. The effects of these inputs were evaluated using a fractional factorial design-of-experiment approach --Abstract, page iii

Monitoring of hybrid manufacturing using acoustic emission sensor

The approach of hybrid manufacturing addressed in this research uses two manufacturing processes, one process builds a metal part using laser metal deposition, and the other process finishes the part using a milling machining. The ability to produce complete functioning parts in a short time with minimal cost and energy consumption has made hybrid manufacturing popular in many industries for parts repair and rapid prototyping. Monitoring of hybrid manufacturing processes has become popular because it increases the quality and accuracy of the parts produced and reduces both costs and production time. The goal of this work is to monitor the entire hybrid manufacturing process. During the laser metal deposition, the acoustic emission sensor will monitor the defect formation. The acoustic emission sensor will monitor the depth of cut during milling machining. There are three tasks in this study. The first task addresses depth-of-cut detection and tool-workpiece engagement using an acoustic emission monitoring system during milling machining for a deposited material. The second task, defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis (K-means clustering) in conjunction with a principal component analysis (PCA). In the third task, a study was conducted to investigate the ability of AE to detect and identify defects during laser metal deposition using a Logistic Regression Model (LR) and an Artificial Neural Network (ANN) --Abstract, page iv

Depth of Cut Monitoring for Hybrid Manufacturing using Acoustic Emission Sensor

Laser Metal Deposition LMD is a hybrid manufacturing process consist of a laser deposition system combined with a 5-axis CNC milling system. During laser deposition many parameters and their interaction affect the dimensional accuracy of the part produced, powder flow rate, laser power and travel speed are some of these parameters. Sensing the acoustic emission during milling marching gives feedback information regarding depth of metal being cut subsequent part dimensions, if an error in dimensions is found certain actions, such as remaching, close loop control, or laser remelting can be carried out to correct it. Thus a reliable hybrid manufacturing management system requires that a depth-of-cut detection system be integrated with the milling machine architecture. This work establishes, first a methodology to detect an acoustic emission signal, so that the acoustic emission characteristics of the milling could be analyzed. Second, it sought to relate these acoustic data to machining parameters to detect depth-of-cut

Defects Classification of Laser Metal Deposition Using Acoustic Emission Sensor

Laser metal deposition (LMD) is an advanced additive manufacturing (AM) process used to build or repair metal parts layer by layer for a range of different applications. Any presence of deposition defects in the part produced causes change in the mechanical properties and might cause failure to the part. In this work, defects monitoring system was proposed to detect and classify defects in real time using an acoustic emission (AE) sensor and an unsupervised pattern recognition analysis. Time domain and frequency domain, and relevant descriptors were used in the classification process to improve the characterization and the discrimination of the defects sources. The methodology was found to be efficient in distinguishing two types of signals that represent two kinds of defects. A cluster analysis of AE data is achieved and the resulting clusters correlated with the defects sources during laser metal deposition.Mechanical Engineerin

Multi-Objective Optimization of Operating Parameters for a H₂/Diesel Dual-Fuel Compression-Ignition Engine

This numerical study covers the engine performance and emissions of a dual-fuel compression-ignition engine fueled by hydrogen/diesel mixtures. Advanced simulations of the combustion process were performed by focusing on simulating the engine performance and emissions at different hydrogen quantities. Different factors that have significant effects on engine performance and emissions, such as exhaust gas recirculation and modifying diesel injection timing (IT), were also considered in this study. The relationship between the performance, emissions, and the operating parameters considered in this work are investigated and explained. A significant reduction of soot emissions by approximately 32.5% has been achieved by increasing hydrogen levels up to 37.5%; however, this has led to an increase in nitrogen oxides (NOx) emissions by ~22%. To overcome this problem, the optimum operating parameters that will lead to minimum emissions and maximum efficiency were also sought. Hydrogen rates, exhaust gas recirculation (EGR) rates, and diesel injection timing were the main operating conditions while the engine performance and NOx/soot emissions were the objectives. The best operating conditions for hydrogen/diesel engines were obtained by solving the multi-objective problem of maximizing the efficiency while minimizing the NOx and soot emissions. This multi-objective optimization problem (MOOP) with conflicting objectives was solved by using different optimization techniques, including regression analysis, artificial neural networks, and genetic algorithms. By solving MOOP, the first preferred operating condition at ~13% hydrogen, 4% EGR, and 30 BTDC of diesel injection timing was obtained