scholarly journals Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Amir M. Aboutaleb ◽  
Mark A. Tschopp ◽  
Prahalad K. Rao ◽  
Linkan Bian
Keyword(s):  
Experimental Data ◽  
Additive Manufacturing ◽  
Process Optimization ◽  
Process Parameters ◽  
Process Conditions ◽  
Optimization Approach ◽  
Geometric Accuracy ◽  
Multi Objective Optimization ◽  
Fused Filament Fabrication ◽  
Multi Objective

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

High-pressure jet-assisted turning of AISI 304: Experimental and multi-objective optimization approach

2017 ◽  
Vol 232 (6) ◽  
pp. 734-750 ◽  
Author(s):  
Sayed E Mirmohammadsadeghi ◽  
H Amirabadi
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
High Pressure ◽  
Cutting Force ◽  
Process Parameters ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Aisi 304 ◽  
Multi Objective ◽  
304 Austenitic Stainless Steel

High-pressure jet-assisted turning is an effective method to decrease the cutting force and surface roughness. Efficiency of this process is related to application of proper jet pressure proportional to other process parameters. In this research, experiments were conducted for high-pressure jet-assisted turning in finishing AISI 304 austenitic stainless steel, based on response surface method. Against the expectations, the maximum jet pressure could not lead to the most efficient results, which means that applying high-pressure jet-assisted turning without considering optimal process parameters will diminish the improving effects of high-pressure jet assistance. For this purpose, two artificial neural networks were trained by genetic algorithm to model the surface roughness and cutting force based on the process parameters. Ultimately, nondominated sorting genetic algorithm was implemented for multi-objective optimization of process. Results demonstrated that the employed method provides an effective approach that indicates optimized range of process parameters.

Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication

2021 ◽  
Author(s):  
Berkcan Kapusuzoglu ◽  
Paromita Nath ◽  
Matthew Sato ◽  
Sankaran Mahadevan ◽  
Paul Witherell
Keyword(s):  
Process Parameters ◽  
Epistemic Uncertainty ◽  
Optimization Under Uncertainty ◽  
Model Parameters ◽  
Multi Objective Optimization ◽  
Bond Quality ◽  
Fused Filament Fabrication ◽  
Multi Objective ◽  
Part Quality ◽  
Trade Offs

Abstract This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input are considered in the optimization. Finally, Pareto surfaces are constructed to estimate the trade-offs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using actual manufacturing of the parts.

scholarly journals Quantifying Geometric Accuracy With Unsupervised Machine Learning: Using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Mojtaba Khanzadeh ◽  
Prahalada Rao ◽  
Ruholla Jafari-Marandi ◽  
Brian K. Smith ◽  
Mark A. Tschopp ◽  
...  
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Process Conditions ◽  
Complex Geometries ◽  
Self Organizing Map ◽  
Geometric Accuracy ◽  
Fused Filament Fabrication ◽  
Unsupervised Machine Learning ◽  
Geometric Deviations ◽  
Self Organizing

Although complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. Existing geometric dimensioning and tolerancing (GD&T) characteristics are defined based on simple landmark features, and thus, need to be customized to capture the subtle difference in parts with complex geometries. Hence, the objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning (ML) approach called the self-organizing map (SOM). The central hypothesis is that clusters recognized by the SOM correspond to specific types of geometric deviations, which in turn are linked to certain AM process conditions. This hypothesis is tested on parts made while varying process conditions in the fused filament fabrication (FFF) AM process. The outcomes of this research are as follows: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. The significance of this research is that this unsupervised ML approach resulted in less than 3% of over 1 million data points being required to fully quantify the part geometric accuracy.

scholarly journals A Decision-Support Model for Additive Manufacturing Scheduling Using an Integrative Analytic Hierarchy Process and Multi-Objective Optimization

2020 ◽  
Vol 10 (15) ◽  
pp. 5159
Author(s):  
Kasin Ransikarbum ◽  
Rapeepan Pitakaso ◽  
Namhun Kim
Keyword(s):  
Decision Support ◽  
Additive Manufacturing ◽  
Analytic Hierarchy Process ◽  
Supply Chain Design ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Analytic Hierarchy ◽  
Multi Objective ◽  
Trade Offs ◽  
Hierarchy Process

Additive manufacturing (AM) became widespread through several organizations due to its benefits in providing design freedom, inventory improvement, cost reduction, and supply chain design. Process planning in AM involving various AM technologies is also complicated and scarce. Thus, this study proposed a decision-support tool that integrates production and distribution planning in AM involving material extrusion (ME), stereolithography (SLA), and selective laser sintering (SLS). A multi-objective optimization approach was used to schedule component batches to a network of AM printers. Next, the analytic hierarchy process (AHP) technique was used to analyze trade-offs among conflicting criteria. The developed model was then demonstrated in a decision-support system environment to enhance practitioners’ applications. Then, the developed model was verified through a case study using automotive and healthcare parts. Finally, an experimental design was conducted to evaluate the complexity of the model and computation time by varying the number of parts, printer types, and distribution locations.

Multi-Objective Optimization of EDM Process Parameters by Using Passing Vehicle Search (PVS) Algorithm

2018 ◽  
Vol 382 ◽  
pp. 138-146 ◽  
Author(s):  
T. Ramprabhu ◽  
Vimal Savsani ◽  
Sohil Parsana ◽  
Nishil Radadia ◽  
Mohak Sheth ◽  
...  
Keyword(s):  
Mathematical Models ◽  
Process Parameters ◽  
Heuristic Algorithms ◽  
High Strength ◽  
Processing Parameters ◽  
Population Based ◽  
Automotive Application ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective

Electro-Discharge Machining (EDM) is very popular for machining high-strength conductive materials for aerospace and automotive application. These machining involve a range of processing parameters. In order to optimize these for the best performance, a trade-off has to be decided for the responses achieved through machining. Conventional algorithms have long been replaced by advanced optimization algorithms. Performance of meta-heuristic algorithms in relation to traditional deterministic approaches for multi-modal, non-linear engineering problems is very promising in recent days. In this paper, a multi-objective optimization approach is applied using a population-based meta-heuristic algorithm called Passing Vehicle Search (PVS) for optimizing process parameters of various mathematical models formulated by different authors. Different approaches depending on case have been adopted for formulating the multi-objective PVS algorithm and pareto front is obtained for each case to extract the desired results. The performance of multi-objective PVS is compared with different intelligent computing models employed in prior studies and better results are shown in case of former. This approach can be extended to various mathematical models besides those covered in the paper to obtain better optimization results.

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