Industrial Engineering Commons^™

Process Optimization Of Directed Energy Deposition Via Dmaic Methodology And Lean Manufacturing Principles, Erick Gerardo Molina Castaneda

Open Access Theses & Dissertations

Additive Manufacturing (AM), particularly Direct Energy Deposition (DED), has gained increasing relevance in industrial and research environments due to its capability to manufacture complex geometries and repair high-value components. However, despite its technological advantages, DED processes often lack standardization, clear documentation, and structured workflows, especially in academic laboratories. This research aims to optimize and standardize DED processes through the application of Lean Manufacturing principles using the DMAIC (Define, Measure, Analyze, Improve, Control) methodology. The study was conducted at the Keck Center for 3D Innovation, focusing on pre-printing and printing activities. Key issues were identified. Improvement actions included the implementation of …

Multi-Material Printer Development, Jessica Hu, Phoebe Eplett

College of Engineering Summer Undergraduate Research Program

Surgeons require pre-surgical models to practice procedures and improve success rates. However, the current process of creating physical models for practice using 3D printing is limited to producing homogeneous mimics of the human body, while the human body is a heterogeneous structure composed of multiple materials with varying properties, including bones, muscles, and skin. Additive manufacturing methods cannot easily print multiple materials simultaneously, as current AM machines only print homogeneous material, unlike the human body. In this proposal, we aim to develop a multi-material additive manufacturing system, from software to hardware, to manufacture pre-surgical models. We will investigate multi-material AM …

Fff Process Parameter Identification With Machine Learning Models, Owen Davis Smith

Honors Theses

Additive manufacturing (AM) has seen increasing popularity in recent times, owing to its efficiency and high speeds, particularly with processes such as Fused Filament Fabrication (FFF). Input process parameters have large impacts on the final part. Incomplete process parameters, which can occur for a variety of reasons, make tasks such as replicating AM studies difficult. A machine learning model can be trained on in-situ layer-wise images collected during a print to combat this issue, predicting process parameters with sufficient data. In this study, two parameters were tested: infill pattern orientation and extrusion width. Twelve parts were produced per parameter and …

Representation Learning Of Point Cloud Data For Process Mining And Anomaly Detection In Complex Systems, Yujing Yang

Industrial, Manufacturing, and Systems Engineering Dissertations - Archive

Complex systems, e.g., advanced manufacturing systems, are largely associated with dynamic and transient behaviors, resulting in condition changes and anomalies. Sensor-based condition monitoring is critical in detecting anomalies and supporting process monitoring and performance improvement for complex manufacturing systems. Traditional sensor-based monitoring approaches primarily focus on one-dimensional (1D) signals and two-dimensional (2D) images, which are limited in their ability to capture high-resolution spatial patterns pertaining to anomalies induced by systems’ condition changes, especially subtle ones. Recent advancements in three-dimensional (3D) sensing present a unique opportunity to address this limitation by enabling the capture of 3D point cloud data with micro-level …

Accelerating Manufacturing Decisions Using Bayesian Optimization: An Optimization And Prediction Perspective, Taofeeq Olajire

Graduate Theses, Dissertations, and Problem Reports (ETD)

Manufacturing is a promising technique for producing complex and custom-made parts with a high degree of precision. It can also provide us with desired materials and products with specified properties. To achieve that, it is crucial to find out the optimum point of process parameters that have a significant impact on the properties and quality of the final product. Unfortunately, optimizing these parameters can be challenging due to the complex and nonlinear nature of the underlying process, which becomes more complicated when there are conflicting objectives, sometimes with multiple goals. Furthermore, experiments are usually costly, time-consuming, and require expensive materials, …

Minimax Registration For Point Cloud Alignment, Zhaohui Geng, Mauro Garcia, Bopaya Bidanda

Manufacturing & Industrial Engineering Faculty Publications

The alignment, or rigid registration, of three-dimensional (3D) point clouds plays an important role in many applications, such as robotics and computer vision. Recently, with the improvement in high precision and automated 3D scanners, the registration algorithm has become critical in a manufacturing setting for tolerance analysis, quality inspection, or reverse engineering purposes. Most of the currently developed registration algorithms focus on aligning the point clouds by minimizing the average squared deviations. However, in manufacturing practices, especially those involving the assembly of multiple parts, an envelope principle is widely used, which is based on minimax criteria. Our present work …

Minimizing Leakage In Thin Walled Structures Printed Through Selective Laser Melting, Andrew Spencer Yap

Master's Theses

In this project, the scan strategy of selective laser melting (SLM) for thin walled structures was investigated by changing laser parameters and tool path. Producing thin walled structures is difficult due to defects such as warpage and porosity. A layer on the SLM 125 consists of hatch volume, fill contours, and borders, however, for thin walls, hatch volume can become unavailable, resulting in a solely border/fill contour laser tool path.

Three central composite designs (CCD) were created to optimize the laser parameters of borders to minimize leakage rate and porosity. The two factors changed were border laser power and scanning …

Transfer Learning Approach To Powder Bed Fusion Additive Manufacturing Defect Detection, Michael Wu

Master's Theses

Laser powder bed fusion (LPBF) remains a predominately open-loop additive manufacturing process with minimal in-situ quality and process control. Some machines feature optical monitoring systems but lack automated analytical capabilities for real-time defect detection. Recent advances in machine learning (ML) and convolutional neural networks (CNN) present compelling solutions to analyze images in real-time and to develop in-situ monitoring.

Approximately 30,000 selective laser melting (SLM) build images from 31 previous builds are gathered and labeled as either “okay” or “defect”. Then, 14 open-sourced CNN were trained using transfer learning to classify the SLM build images. These models were evaluated by F1 …

Sustainability-Based Expert System For Additive Manufacturing And Cnc Machining, Josage Chathura Pamuditha Perera

Graduate Theses, Dissertations, and Problem Reports (ETD)

The development of technologies which enable resource efficient production is of paramount importance for the continued advancement of the manufacturing industry. In order to ensure a sustainable and clean energy future, manufacturers should be able to contrast and validate existing manufacturing technologies on a sustainability basis. In the post COVID-19 era of enterprise management, the use of artificial intelligence to simulate human expert decision making abilities will open new doors to achieving heightened levels of productivity and efficiency. The introduction of innovative technologies such as CNC machining and 3D printing to production systems has redefined the manufacturing landscape in a …

Redesign Of Cubesat For Beam Charging, Kuba Preis

Industrial and Manufacturing Engineering

This paper is intended to be a study in the applications of the design freedom granted by additive manufacture in the design of a 1U CubeSat frame. The main loads experienced by a CubeSat are structural (during launch) and thermal (solar radiation). Beam charging is an emerging technology which involves charging a CubeSat using a laser beam. In this paper, a CubeSat frame was redesigned to account for the structural loads induced during launch and the thermal loads induced when beam charging. The thermal, weight, design, and structural requirements for a new CubeSat design were derived. The 1U CubeSat frame …

Additive Manufacturing Powder Removal: Viper 2.0, Andrew D. Epperson, Sean Mccracken, Melissa O'Neil, Alex Ward

Mechanical Engineering

This report presents the final design review of this senior project team. The project is being sponsored by Lawrence Livermore National Laboratory, a federal design agency. Lawrence Livermore National Laboratory is interested in improving their metal additive manufacturing process. The goal of this senior project is to improve the efficiency and safety of a method currently being used to remove metal powders for additively manufactured components. A senior project team in 2017-2018 created the Vibration Induced Powder Evacuator and Reclaimer (VIPER), a device that uses a vibration motor to shake a printed part until it is clean from excess powder. …

Fracture Toughness And Fatigue Crack Growth Rate Characterization Of Inconel 718 Formed By Laser Powder Bed Fusion, Charles C. Hohnbaum

Theses and Dissertations

Continuing improvement in the field of AM of metals provides the opportunity for direct fabrication of aerospace parts. AM was once used in large part for rapid prototyping but improvements in technology and increases in the knowledge base of AM materials has provided the opportunity for manufacture of AM parts for operational use. The ability to create low numbers of unique parts without having to invest in expensive tooling provides great economic incentive to utilize this technique. IN718 is the most common high temperature alloy used in the aerospace industry and lends itself readily to formation by LPBF. The superior …

Development Of An Additive Manufacturing Compression Molding Process For Low Cost In-House Prototyping, Grant Forrester Warden

Industrial and Manufacturing Engineering

Composite parts can be manufactured using various processes. Generally, a mix of resin and fiber is formed into the desired geometry using a mold and pressure. One process used by Dr. Joseph Mello in his research is known as compression molding. Compression molds are generally made from large billets of aluminum or stainless steel, are machined by a CNC mill, and are then hand-finished with polishes and mold preparation products. This process is expensive, requires large machinery and experienced operators, and introduces long lead times relative to the design cycle of the part being manufactured. The nature of Dr. Mello's …

Predictive Modeling On The Piezoelectric Properties Of 3d Printed Functional Nanocomposites Using The Data Analytics Approach, Md Didarul Islam

Open Access Theses & Dissertations

This paper presents research done on prediction modeling using a data analytics approach to determine various factors affecting the piezoelectric properties of the 3D printed pressure sensors. Previously, the material extrusion 3D printing technique was used to fabricate pressure sensors composed of multiwall carbon nanotubes (CNT), barium titanate (BT), and polyvinylidene fluoride (PVDF) using simple fabrication and low-cost methods. This sensor produced a voltage output of 725 mV (0.13 pC/N) which is enough for pressure sensing applications. However, a holistic study to determine impacts of all factors was not carried out in the previous model. In this study, the design …