Data Driven Modeling and Optimization for Energy Efficiency in Additive Manufacturing Process With Geometric Accuracy Consideration

2018 ◽  
Author(s):  
Junfeng Ma ◽  
Wenmeng Tian ◽  
Morteza Alizadeh
Keyword(s):  
Energy Consumption ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Complex Geometry ◽  
Statistical Regression ◽  
Geometric Accuracy ◽  
Fused Deposition ◽  
Optimization Framework ◽  
Accuracy Measure ◽  
The Impact

Despite of its tremendous merits in producing parts with complex geometry and functionally graded materials, additive manufacturing (AM) is inherently an energy expensive process. Prior studies have shown that process parameters, such as printing resolution, printing speed, and printing temperature, are correlated to energy consumption per part. Moreover, part geometric accuracy is another major focus in AM research, and extensive studies have shown that the geometric accuracy of final parts is highly dependent on those process parameters as well. Though both energy consumption and part geometric accuracy heavily depend on the process parameters in AM processes, jointly considering the dual outputs in AM process is not fully investigated. The proposed study aims to obtain a quantitative understanding of the impact of these process parameters on AM energy consumption given part quality requirements, such as geometric accuracy. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical regression analysis technologies, the study quantifies the correlation between AM process parameters and energy consumption as well as the final geometric accuracy measure. An optimization framework is proposed to minimize the energy consumption per part. The Kuhn-Tucker non-linear optimization algorithm is used to identify the optimal process parameters. This study is of significance to AM energy consumption in terms of jointly considering energy consumption and final part geometric accuracy in the optimization framework.

scholarly journals Effect of Process Parameters on Energy Consumption, Physical, and Mechanical Properties of Fused Deposition Modeling

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2406
Author(s):  
Emmanuel U. Enemuoh ◽  
Stefan Duginski ◽  
Connor Feyen ◽  
Venkata G. Menta
Keyword(s):  
Energy Consumption ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Fused Deposition Modeling ◽  
Function Optimization ◽  
Free Form ◽  
Production Time ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
The Impact

The application of the fused deposition modeling (FDM) additive manufacturing process has increased in the production of functional parts across all industries. FDM is also being introduced for industrial tooling and fixture applications due to its capabilities in building free-form and complex shapes that are otherwise challenging to manufacture by conventional methods. However, there is not yet a comprehensive understanding of how the FDM process parameters impact the mechanical behavior of engineered products, energy consumption, and other physical properties for different material stocks. Acquiring this information is quite a complex task, given the large variety of possible combinations of materials–additive manufacturing machines–slicing software process parameters. In this study, the knowledge gap is filled by using the Taguchi L27 orthogonal array design of experiments to evaluate the impact of five notable FDM process parameters: infill density, infill pattern, layer thickness, print speed, and shell thickness on energy consumption, production time, part weight, dimensional accuracy, hardness, and tensile strength. Signal-to-noise (S/N) ratio analysis and analysis of variance (ANOVA) were performed on the experimental data to quantify the parameters’ main effects on the responses and establish an optimal combination for the FDM process. The novelty of this work is the simultaneous evaluation of the effects of the FDM process parameters on the quality performances because most studies have considered one or two of the performances alone. The study opens an opportunity for multiobjective function optimization of the FDM process that can be used to effectively minimize resource consumption and production time while maximizing the mechanical and physical characteristics to fit the design requirements of FDM-manufactured products.

A Preliminary Experimental Study of Additive Manufacturing Energy Consumption

2017 ◽  
Author(s):  
Daniel Dunaway ◽  
James Dillon Harstvedt ◽  
Junfeng Ma
Keyword(s):  
Energy Consumption ◽  
Additive Manufacturing ◽  
Experimental Design ◽  
Surface Area ◽  
Surface Geometry ◽  
Fused Deposition ◽  
Part Geometry ◽  
Industrial Sectors ◽  
Part Surface ◽  
The Impact

Additive manufacturing (AM) refers to a group of manufacturing techniques that produce components by melting and bonding material powders in a layer-by-layer fashion. By virtue of its capability of producing parts with complex geometry and functionally graded materials, AM is leading the charge of the “third industrial revolution” and has attracted great attention in multiple industrial sectors, such as manufacturing, healthcare, aerospace, and others. Sustainability of AM remains an open question. AM is inherently an energy expensive process and may be energy inefficient as compared to the traditional manufacturing process. Thus, there exists an urgent need to identify the key influence factors and quantify the energy consumption during AM production. The proposed study aims to obtain a preliminary understanding of the impact of part surface geometry on AM energy consumption. The study addresses the effect of part geometry on AM energy consumption through experimental design method. Part geometry consists of two level meanings, part surface area and part surface complexity. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical analysis technologies, the study firstly identifies the correlation between part geometry and AM energy consumption. The result shows that part surface area is positively correlated with AM energy consumption and no significant statistical evidence to support that part surface complexity is associated with AM energy consumption. Such findings are of significance to AM energy consumption in terms of both qualitative and quantitative analysis. In addition, the study has significant potentials to guide the future AM energy consumption model development and to be extended to future AM process improvement.

scholarly journals The impact of process parameters on mechanical properties of parts fabricated in PLA with an open-source 3-D printer

2015 ◽  
Vol 21 (5) ◽  
pp. 604-617 ◽  
Author(s):  
Antonio Lanzotti ◽  
Marzio Grasso ◽  
Gabriele Staiano ◽  
Massimo Martorelli
Keyword(s):  
Mechanical Properties ◽  
Open Source ◽  
Process Parameters ◽  
Computer Aided Design ◽  
Fused Deposition Modeling ◽  
Testing Machine ◽  
Main Process ◽  
Content Type ◽  
Fused Deposition ◽  
The Impact

Purpose – This study aims to quantify the ultimate tensile strength and the nominal strain at break (ɛf) of printed parts made from polylactic acid (PLA) with a Replicating Rapid prototyper (Rep-Rap) 3D printer, by varying three important process parameters: layer thickness, infill orientation and the number of shell perimeters. Little information is currently available about mechanical properties of parts printed using open-source, low-cost 3D printers. Design/methodology/approach – A computer-aided design model of a tensile test specimen was created, conforming to the ASTM:D638. Experiments were designed, based on a central composite design. A set of 60 specimens, obtained from combinations of selected parameters, was printed on a Rep-Rap Prusa I3 in PLA. Testing was performed using a JJ Instruments – T5002-type tensile testing machine and the load was measured using a load cell of 1,100 N. Findings – This study investigated the main impact of each process parameter on mechanical properties and the effects of interactions. The use of a response surface methodology allowed the proposition of an empirical model which connects process parameters and mechanical properties. Even though results showed a high variability, additional ideas on how to understand the impact of process parameters are suggested in this paper. Originality/value – On the basis of experimental results, it is possible to obtain practical suggestions to set common process parameters in relation to mechanical properties. Experiments discussed in the present paper provide a variety of data and insight regarding the relationship among the main process parameters and the stiffness and strength of fused deposition modeling-printed parts made from PLA. In particular, this paper underlines the shortage in existing literature concerning the impact of process parameters on the elastic modulus and the strain to failure for the PLA. The experimental data produced show a good degree of compliance with analytical formulations and other data found in literature.

scholarly journals Environmental and Economic Analysis of FDM, SLS and MJF Additive Manufacturing Technologies

Materials ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4161 ◽  
Author(s):  
Vincenzo Tagliaferri ◽  
Federica Trovalusci ◽  
Stefano Guarino ◽  
Simone Venettacci
Keyword(s):  
Additive Manufacturing ◽  
High Performance ◽  
Large Scale ◽  
Fused Deposition Modeling ◽  
Production Capacity ◽  
Optimal Choice ◽  
Scale Production ◽  
Fused Deposition ◽  
Manufacturing Technologies ◽  
The Impact

In this study, the authors present a comparative analysis of different additive manufacturing (AM) technologies for high-performance components. Four 3D printers, currently available on the Italian national manufacturing market and belonging to three different AM technologies, were considered. The analysis focused on technical aspects to highlight the characteristics and performance limits of each technology, economic aspects to allow for an assessment of the costs associated with the different processes, and environmental aspects to focus on the impact of the production cycles associated with these technologies on the ecosystem, resources and human health. This study highlighted the current limits of additive manufacturing technologies in terms of production capacity in the case of large-scale production of plastic components, especially large ones. At the same time, this study highlights how the geometry of the object to be developed greatly influences the optimal choice between the various AM technologies, in both technological and economic terms. Fused deposition modeling (FDM) is the technology that exhibits the greatest limitations hindering mass production due to production times and costs, but also due to the associated environmental impact.

Analysis of factors influencing energy consumption of material extrusion-based additive manufacturing using interpretive structural modelling

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramesh P. ◽  
Vinodh S.
Keyword(s):  
Energy Consumption ◽  
Additive Manufacturing ◽  
Influencing Factors ◽  
Structural Model ◽  
Ease Of Use ◽  
Content Type ◽  
Material Extrusion ◽  
Prototype Development ◽  
Factors Influencing ◽  
The Impact

Purpose Material extrusion (MEX) is a class of additive manufacturing (AM) process based on MEX principle. In the viewpoint of Industry 4.0 and sustainable manufacturing, AM technologies are gaining importance than conventional manufacturing route (subtractive manufacturing). Because of the ease of use and lesser operation skills, MEX had wide popularity in industry for product and prototype development. This study aims to analyze energy consumption of MEX-based AM process and its influencing factors. Design/methodology/approach A group of factors were identified pertaining to MEX-based AM process. In this viewpoint, this study presents the configuration of a structural model using interpretive structural modeling (ISM) to depict dominant factors in MEX-based AM process. A total of 18 influencing factors are identified and ranked using ISM methodology for MEX process. The Impact Matrix Cross-reference Multiplication Applied to a Classification analysis was done to categorize influencing factors into four groups for MEX-based AM process. Findings The derivation of structural model would enable AM practitioners to systematically analyze the factors and to derive key factors which enable comprehensive energy modeling and energy assessment studies. Also, it facilitates the development of energy efficient AM system. Originality/value The development of structural model for analysis of factors influencing energy consumption of MEX-based AM is the original contribution of the authors.

Finite Element Analysis of Additive Manufacturing Based on Fused Deposition Modeling: Distortions Prediction and Comparison With Experimental Data

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Alberto Cattenone ◽  
Simone Morganti ◽  
Gianluca Alaimo ◽  
Ferdinando Auricchio
Keyword(s):  
Additive Manufacturing ◽  
3D Printing ◽  
Fused Deposition Modeling ◽  
Step Size ◽  
Time Step ◽  
Time Step Size ◽  
Fused Deposition ◽  
Deposition Modeling ◽  
The Impact ◽  
Functional Components

Additive manufacturing (or three-dimensional (3D) printing) is constantly growing as an innovative process for the production of complex-shape components. Among the seven recognized 3D printing technologies, fused deposition modeling (FDM) covers a very important role, not only for producing representative 3D models, but, mainly due to the development of innovative material like Peek and Ultem, also for realizing structurally functional components. However, being FDM a production process involving high thermal gradients, non-negligible deformations and residual stresses may affect the 3D printed component. In this work we focus on meso/macroscopic simulations of the FDM process using abaqus software. After describing in detail the methodological process, we investigate the impact of several parameters and modeling choices (e.g., mesh size, material model, time-step size) on simulation outcomes and we validate the obtained results with experimental measurements.

scholarly journals Characterization and Optimization of Mechanical Properties of ABS Parts Manufactured by the Fused Deposition Modelling Process

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Godfrey C. Onwubolu ◽  
Farzad Rayegani
Keyword(s):  
Tensile Strength ◽  
Additive Manufacturing ◽  
Process Parameters ◽  
Group Method ◽  
Fused Deposition Modelling ◽  
Process Conditions ◽  
Research Issues ◽  
Practical Applications ◽  
Fused Deposition ◽  
Study Results

While fused deposition modelling (FDM) is one of the most used additive manufacturing (AM) techniques today due to its ability to manufacture very complex geometries, the major research issues have been to balance ability to produce aesthetically appealing looking products with functionality. In this study, five important process parameters such as layer thickness, part orientation, raster angle, raster width, and air gap have been considered to study their effects on tensile strength of test specimen, using design of experiment (DOE). Using group method of data handling (GMDH), mathematical models relating the response with the process parameters have been developed. Using differential evolution (DE), optimal process parameters have been found to achieve good strength simultaneously for the response. The optimization of the mathematical model realized results in maximized tensile strength. Consequently, the additive manufacturing part produced is improved by optimizing the process parameters. The predicted models obtained show good correlation with the measured values and can be used to generalize prediction for process conditions outside the current study. Results obtained are very promising and hence the approach presented in this paper has practical applications for design and manufacture of parts using additive manufacturing technologies.

Manufacturing Energy Consumption Estimation Using Machine Learning Approach

2017 ◽  
Author(s):  
Ruoyu Song ◽  
Yanglong Lu ◽  
Cassandra Telenko ◽  
Yan Wang
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Process Parameters ◽  
Cost Estimation ◽  
Fused Deposition Modeling ◽  
Design Stage ◽  
Regression Methods ◽  
Fused Deposition ◽  
Data Driven Approach ◽  
Efficient Data

Environmental impacts of manufacturing are often significant and influenced by part and process parameters. Energy consumption is one of the most critical factors for the overall environmental impact of manufacturing. To achieve energy reduction, one must estimate the manufacturing energy consumption throughout the design stage. This paper presents an efficient data-driven approach to utilize machine learning to estimate energy consumption of a manufacturing process from a CAD model. The approach enables quick cost estimation with limited knowledge about the exact process parameters. A case study of fused deposition modeling is used to illustrate the feasibility of this framework and test potential regression methods. Lasso and elastic net regressions were compared in this study. The potential application of this framework to other manufacturing processes is also discussed.

scholarly journals Additive Manufacturing of Wood Flour/PHA Composites Using Micro-Screw Extrusion: Effect of Device and Process Parameters on Performance

Polymers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1107
Author(s):  
Jing Tian ◽  
Run Zhang ◽  
Jiayuan Yang ◽  
Weimin Chou ◽  
Ping Xue ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Fused Deposition Modeling ◽  
Wood Flour ◽  
Screw Speed ◽  
Fused Deposition ◽  
Screw Extrusion ◽  
Deposition Modeling ◽  
Influencing Parameters ◽  
Printing Speed

Based on additive manufacturing of wood flour and polyhydroxyalkanoates composites using micro-screw extrusion, device and process parameters were evaluated to achieve a reliable printing. The results show that the anisotropy of samples printed by micro-screw extrusion is less obvious than that of filament extrusion fused deposition modeling. The type of micro-screw, printing speed, layer thickness, and nozzle diameter have significant effects on the performance of printed samples. The linear relationship between the influencing parameters and the screw speed is established, therefore, the performance of printed products can be controlled by the extrusion flow rate related to screw speed.

scholarly journals Model of Production System Evaluation with the Influence of FDM Machine Reliability and Process-Dependent Product Quality

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5806
Author(s):  
Iwona Paprocka ◽  
Wojciech M. Kempa
Keyword(s):  
Layer Thickness ◽  
Process Parameters ◽  
Production System ◽  
Job Shop ◽  
Failure Time ◽  
System Quality ◽  
Fused Deposition ◽  
The Impact ◽  
Quality Robustness ◽  
Peak Value

This paper investigates the Job Shop Scheduling Problem (JSSP) with FDM (Fused Deposition Modeling) machine unavailability constraints due to Predictive Maintenance (PdM) tasks, under the objective of minimizing the makespan, total tardiness and machine idle time. The Ant-Colony Optimization (ACO) algorithm is elaborated to deal with the JSSP. The reliability characteristics of the critical machine (FDM) influence the product as well as the production system quality. PdM periods are estimated based on historical data on failure-free times of the FDM machine components and deviations from the standards established for the key process parameters: infill density, layer thickness and extruder temperature. The standards for the key process parameters are identified based on investigation of the mechanical properties of printed elements. The impact of failure time and the number of nonstandard measurements of parameters on the quality of the Job Shop System (JSS) are observed. Failure rate of the FDM machine is corrected with the probability of a stoppage in the future period due to the “outlier” in measurements of any key parameters of the additive process. The quality robustness of production schedules increases with the disturbance-free operation of the FDM up to the peak value. After reaching the peak value the quality robustness decreases. The original issue of this paper is a model of scheduling production and maintenance tasks in a job shop system with an FDM machine as a bottleneck using ACO. Additionally, an original FDM-reliability model is also proposed. The model is based on weighted p-moving averages of the observed number of deviations from the norms, established for key process parameters such as fill density, layer thickness and extruder temperature.

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