Learning Algorithm Based Modeling and Process Parameters Recommendation System for Binder Jetting Additive Manufacturing Process

2015 ◽  
Author(s):  
Han Chen ◽  
Yaoyao F. Zhao
Keyword(s):  
Additive Manufacturing ◽  
Product Quality ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Process Model ◽  
Recommendation System ◽  
Powder Materials ◽  
Quality Properties ◽  
Binder Jetting ◽  
Printing Time

Binder Jetting (BJ) process is an additive manufacturing process in which powder materials are selectively joined by binder materials. Products can be manufactured layer by layer directly from 3D model data. It is not always easy for manufacturing engineers to choose proper BJ process parameters to meet the end-product quality and fabrication time requirements. This is because the quality properties of the products fabricated by BJ process are significantly affected by the process parameters. And the relationships between process parameters and quality properties are very complicated. In this paper, a process model is developed by Backward Propagation (BP) Neural Network (NN) algorithm based on 16 groups of orthogonal experiment designed by Taguchi Method to express the relationships between 4 key process parameters and 2 key quality properties. Based on the modeling results, an intelligent parameters recommendation system is developed to predict end-product quality properties and printing time, and to recommend process parameters selection based on the process requirements. It can be used as a guideline for selecting the proper printing parameters in BJ to achieve the desired properties and help to reduce the printing time.

Process parameters optimization for improving surface quality and manufacturing accuracy of binder jetting additive manufacturing process

2016 ◽  
Vol 22 (3) ◽  
pp. 527-538 ◽  
Author(s):  
Han Chen ◽  
Yaoyao Fiona Zhao
Keyword(s):  
Additive Manufacturing ◽  
Product Quality ◽  
Process Parameters ◽  
Parameters Optimization ◽  
Powder Materials ◽  
Optimal Parameters ◽  
Content Type ◽  
Quality Properties ◽  
Printing Parameters ◽  
Binder Jetting

Purpose Binder jetting (BJ) process is an additive manufacturing (AM) process in which powder materials are selectively joined by binder materials. Products can be manufactured layer-by-layer directly from three-dimensional model data. The quality properties of the products fabricated by the BJ AM process are significantly affected by the process parameters. To improve the product quality, the optimal process parameters need to be identified and controlled. This research works with the 420 stainless steel powder material. Design/methodology/approach This study focuses on four key printing parameters and two end-product quality properties. Sixteen groups of orthogonal experiment designed by the Taguchi method are conducted, and then the results are converted to signal-to-noise ratios and analyzed by analysis of variance. Findings Five sets of optimal parameters are concluded and verified by four group confirmation tests. Finally, by taking the optimal parameters, the end-product quality properties are significantly improved. Originality/value These optimal parameters can be used as a guideline for selecting proper printing parameters in BJ to achieve the desired properties and help to improve the entire BJ process ability.

Toward Metamodels for Composable and Reusable Additive Manufacturing Process Models

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Paul Witherell ◽  
Shaw Feng ◽  
Timothy W. Simpson ◽  
David B. Saint John ◽  
Pan Michaleris ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Metal Powder ◽  
Manufacturing Process ◽  
Process Model ◽  
Model Development ◽  
Process Models ◽  
Powder Bed Fusion ◽  
Powder Bed ◽  
Future Work ◽  
Am Processes

In this paper, we advocate for a more harmonized approach to model development for additive manufacturing (AM) processes, through classification and metamodeling that will support AM process model composability, reusability, and integration. We review several types of AM process models and use the direct metal powder bed fusion AM process to provide illustrative examples of the proposed classification and metamodel approach. We describe how a coordinated approach can be used to extend modeling capabilities by promoting model composability. As part of future work, a framework is envisioned to realize a more coherent strategy for model development and deployment.

Effect of Additive Manufacturing Process Parameters on Turbine Cooling

2019 ◽  
Author(s):  
Jacob C. Snyder ◽  
Karen A. Thole
Keyword(s):  
Heat Transfer ◽  
Additive Manufacturing ◽  
Surface Morphology ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Pressure Loss ◽  
Cooling Performance ◽  
Micro Scale ◽  
Turbine Cooling ◽  
External Cooling

Abstract Turbine cooling is a prime application for additive manufacturing because it enables quick development and implementation of innovative designs optimized for efficient heat removal, especially at the micro-scale. At the micro-scale, however, the surface finish plays a significant role in the heat transfer and pressure loss of any cooling design. Previous research on additively manufactured cooling channels has shown the surface roughness increases both heat transfer and pressure loss to similar levels as highly-engineered turbine cooling schemes. What has not been shown, however, is whether opportunities exist to tailor additively manufactured surfaces through control of the process parameters to further enhance the desired heat transfer and pressure loss characteristics. The results presented in this paper uniquely show the potential of manipulating the parameters within the additive manufacturing process to control the surface morphology, directly influencing turbine cooling. To determine the effect of parameters on cooling performance, coupons were additively manufactured for common internal and external cooling methods using different laser powers, scan speeds, and scanning strategies. Internal and external cooling tests were performed at engine relevant conditions to measure appropriate metrics of performance. Results showed the process parameters have a significant impact on the surface morphology leading to differences in cooling performance. Specifically, internal and external cooling geometries react differently to changes in parameters, highlighting the opportunity to consider process parameters when implementing additive manufacturing for turbine cooling applications.

Predictive-Inspection Based Process Control in End Milling Operations

2005 ◽  
Author(s):  
Aftab Khan ◽  
Dawn Tilbury ◽  
James Moyne
Keyword(s):  
Process Control ◽  
Product Quality ◽  
Manufacturing Process ◽  
Process Model ◽  
Manufacturing Industry ◽  
End Milling ◽  
Loop Control ◽  
Diagnostic Data ◽  
Milling Operations ◽  
Final Inspection

In the manufacturing industry, product quality control is often treated as a completely different problem than process diagnostics. Diagnostics methods are used to quickly identify faults during the process, whereas product quality is assessed at the final inspection stage, after the process is completed. The large amount of data that is collected to enable the on-line diagnostics, however, can be used to predict the product quality before the part is measured at the final inspection stage. This predicted quality can be used as feedback to a control system that improves the process quality by adjusting the process inputs. In this paper we propose a predictive inspection based process control solution for a manufacturing process. A manufacturing process is modelled as an input-output system with the machine settings as inputs and two kinds of outputs: diagnostic data and quality. This model is obtained from off-line experiments using a combination of process inputs and external disturbances. In the online implementation the predictive process model is updated with the diagnostic data collected during runtime and quality is improved by using a two-loop control strategy, on a part-to-part or run-to-run (R2R) basis. The proposed approach is applied to and developed for an end milling operation and simulation and experimental results are presented.

Molecular Dynamics Simulation of Nanoparticle Infiltration During Binder Jet Printing Additive Manufacturing Process: A Preliminary Study

2019 ◽  
Author(s):  
Sagil James ◽  
Cristian Navarro
Keyword(s):  
Molecular Dynamics ◽  
Additive Manufacturing ◽  
Molecular Dynamics Simulation ◽  
Simulation Model ◽  
Manufacturing Process ◽  
Operating Temperature ◽  
Dynamics Simulation ◽  
Infiltration Process ◽  
Operating Temperatures ◽  
Binder Jetting

Abstract Binder Jetting Process involves binding layers of powder material through selective deposition of a liquid binder. Binder jetting is a fast and relatively inexpensive process which does not require a high-powered energy source for printing purpose. Additionally, the binder jetting process is capable of producing parts with extreme complexities without using any support structures. These characteristics make binder jetting an ideal choice for several applications including aerospace, biomedical, energy, and several other industries. However, a significant limitation of binder jetting process is its inability to produce printed parts with full density thereby resulting in highly porous structures. A possible solution to overcome the porosity problems is to infiltrate the printed structures with low-melting nanoparticles. The infiltrating nanoparticles help fill up the voids to densify the printed parts and also aids in the sintering of the printed green parts. In addition to increasing the density, the nanoparticle infiltration also helps improve the mechanical, thermal and electrical properties of the printed part along with bringing multi-functionality aspect. Currently, there is a lack of clarity of the nanoparticle infiltration process performed to improve the quality of parts fabricated through binder jetting. This research employs Molecular Dynamics simulation techniques to investigate the nanoparticle infiltration during binder jetting additive manufacturing process. The simulation is performed at different operating temperatures of 1400 K, 1500 K, and 1600 K. The study found that the infiltration process is significantly affected by the operating temperature. The infiltration height is found to be highest at the operating temperature of 1600 K while the porosity reduction is found to be maximum at 1500 K. The infiltration kinetics is affected by the cohesion of the nanoparticles causing blockage of channels at higher operating temperatures. The simulation model is validated by comparing with the Lucas-Washburn infiltration model. It is seen that the simulation model deviates from the theoretical prediction suggesting that multiple mechanisms are driving the infiltration process at the nanoscale.

Role of process parameters during additive manufacturing by direct metal deposition of Inconel 718

2017 ◽  
Vol 23 (5) ◽  
pp. 919-929 ◽  
Author(s):  
Bo Chen ◽  
Jyoti Mazumder
Keyword(s):  
Additive Manufacturing ◽  
Cooling Rate ◽  
Process Parameters ◽  
Manufacturing Process ◽  
Inconel 718 ◽  
Processing Parameters ◽  
Forming Quality ◽  
Content Type ◽  
Forming Characteristics ◽  
The Relationship

Purpose The aim of this research is to study the influence of laser additive manufacturing process parameters on the deposit formation characteristics of Inconel 718 superalloy, the main parameters that influence the forming characteristics, the cooling rate and the microstructure were studied. Design/methodology/approach Orthogonal experiment design method was used to obtain different deposit shape and microstructure using different process parameters by multiple layers deposition. The relationship between the processing parameters and the geometry of the cladding was analyzed, and the dominant parameters that influenced the cladding width and height were identified. The cooling rates of different forming conditions were obtained by the secondary dendrite arm spacing (SDAS). Findings The microstructure showed different characteristics at different parts of the deposit. Cooling rate of different samples were obtained and compared by using the SDAS, and the influence of the process parameters to the cooling rate was analyzed. Finally, micro-hardness tests were done, and the results were found to be in accordance with the micro-structure distribution. Originality/value Relationships between processing parameters and the forming characteristics and the cooling rates were obtained. The results obtained in this paper will help to understand the relationship between the process parameters and the forming quality of the additive manufacturing process, so as to obtain the desired forming quality by appropriate parameters.

scholarly journals Performance of wearables and the effect of user behavior in additive manufacturing process

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
JuYoun Kwon ◽  
Namhun Kim
Keyword(s):  
Mechanical Properties ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Manufacturing Process ◽  
High Performance ◽  
Essential Role ◽  
User Behavior ◽  
Process Parameter ◽  
Am Processes

AbstractAdditive manufacturing (AM) which can be a suitable technology to personalize wearables is ideal for adjusting the range of part performance such as mechanical properties if high performance is not required. However, the AM process parameter can impact overall durability and reliability of the part. In this instance, user behavior can play an essential role in performance of wearables through the settings of AM process parameter. This review discusses parameters of AM processes influenced by user behavior with respect to performance required to fabricate AM wearables. Many studies on AM are performed regardless of the process parameters or are limited to certain parameters. Therefore, it is necessary to examine how the main parameters considered in the AM process affect performance of wearables. The overall aims of this review are to achieve a greater understanding of each AM process parameter affecting performance of AM wearables and to provide requisites for the desired performance including the practice of sustainable user behavior in AM fabrication. It is discussed that AM wearables with various performance are fabricated when the user sets the parameters. In particular, we emphasize that it is necessary to develop a qualified procedure and to build a database of each AM machine about part performance to minimize the effect of user behavior.

Sign in / Sign up

Export Citation Format

Share Document