Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence
Directed energy deposition (DED) is an additive manufacturing process used in manufacturing free form geometries, repair applications, coating and surface modification, and fabrication of functionally graded materials. It is a process in which focused thermal energy is used to fuse materials by melt...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/10/5027 |
| _version_ | 1797501764226777088 |
|---|---|
| author | Metin Çallı Emre İsa Albak Ferruh Öztürk |
| author_facet | Metin Çallı Emre İsa Albak Ferruh Öztürk |
| author_sort | Metin Çallı |
| collection | DOAJ |
| description | Directed energy deposition (DED) is an additive manufacturing process used in manufacturing free form geometries, repair applications, coating and surface modification, and fabrication of functionally graded materials. It is a process in which focused thermal energy is used to fuse materials by melting. Thermal effects can cause distortions and defects on the parts during the DED process, therefore they should be evaluated and taken into account during the manufacturing of products. Melting pool control and DED bead geometries should be defined properly as well. In this work, an Artificial Neural Network model has been applied considering the DED process parameters in order to predict the geometrical patterns and create a local reinforced product as a hybrid manufacturing technology. Although lots of studies are available on topology optimization for manufacturing methods such as casting, extrusion, and powder bed fusion, topology optimization for the DED process is not widely taken into consideration to predict the design geometrical patterns. DOE RSM and ANN approaches were applied in this study to predict convenient dimensions, topology based geometrical patterns of local stiffeners and heat source power optimizing the energy, total mass, and peak force results of the hybrid part. A single bead track deposition is simulated in terms of validation of the numerical heat source model, and cross-sections of the beads are analysed. A cross-member structure is manufactured using the DED device and the structure is correlated under the three point bending physical conditions on test bench. It has been investigated that locally reinforced cross beam has much more energy absorption and peak force values than plain model. The results showed that the proposed NN-GA is a promising approach to generate the topology based geometrical patterns and process parameters which can be used to create a local reinforced product as hybrid manufacturing technologies. |
| first_indexed | 2024-03-10T03:23:13Z |
| format | Article |
| id | doaj.art-37235589066b4e6dab8d08e2f3f9fbcb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:23:13Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-37235589066b4e6dab8d08e2f3f9fbcb2023-11-23T09:56:39ZengMDPI AGApplied Sciences2076-34172022-05-011210502710.3390/app12105027Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial IntelligenceMetin Çallı0Emre İsa Albak1Ferruh Öztürk2Department of Automotive Engineering, Engineering Faculty, Bursa Uludağ University, Bursa 16059, TurkeyHybrid and Electric Vehicle Technology, Vocational School of Gemlik Asım Kocabıyık, Bursa Uludağ University, Bursa 16600, TurkeyDepartment of Automotive Engineering, Engineering Faculty, Bursa Uludağ University, Bursa 16059, TurkeyDirected energy deposition (DED) is an additive manufacturing process used in manufacturing free form geometries, repair applications, coating and surface modification, and fabrication of functionally graded materials. It is a process in which focused thermal energy is used to fuse materials by melting. Thermal effects can cause distortions and defects on the parts during the DED process, therefore they should be evaluated and taken into account during the manufacturing of products. Melting pool control and DED bead geometries should be defined properly as well. In this work, an Artificial Neural Network model has been applied considering the DED process parameters in order to predict the geometrical patterns and create a local reinforced product as a hybrid manufacturing technology. Although lots of studies are available on topology optimization for manufacturing methods such as casting, extrusion, and powder bed fusion, topology optimization for the DED process is not widely taken into consideration to predict the design geometrical patterns. DOE RSM and ANN approaches were applied in this study to predict convenient dimensions, topology based geometrical patterns of local stiffeners and heat source power optimizing the energy, total mass, and peak force results of the hybrid part. A single bead track deposition is simulated in terms of validation of the numerical heat source model, and cross-sections of the beads are analysed. A cross-member structure is manufactured using the DED device and the structure is correlated under the three point bending physical conditions on test bench. It has been investigated that locally reinforced cross beam has much more energy absorption and peak force values than plain model. The results showed that the proposed NN-GA is a promising approach to generate the topology based geometrical patterns and process parameters which can be used to create a local reinforced product as hybrid manufacturing technologies.https://www.mdpi.com/2076-3417/12/10/5027DED processadditive manufacturingtopology for DED processartificial neural networks |
| spellingShingle | Metin Çallı Emre İsa Albak Ferruh Öztürk Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence Applied Sciences DED process additive manufacturing topology for DED process artificial neural networks |
| title | Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence |
| title_full | Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence |
| title_fullStr | Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence |
| title_full_unstemmed | Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence |
| title_short | Prediction and Optimization of the Design and Process Parameters of a Hybrid DED Product Using Artificial Intelligence |
| title_sort | prediction and optimization of the design and process parameters of a hybrid ded product using artificial intelligence |
| topic | DED process additive manufacturing topology for DED process artificial neural networks |
| url | https://www.mdpi.com/2076-3417/12/10/5027 |
| work_keys_str_mv | AT metincallı predictionandoptimizationofthedesignandprocessparametersofahybriddedproductusingartificialintelligence AT emreisaalbak predictionandoptimizationofthedesignandprocessparametersofahybriddedproductusingartificialintelligence AT ferruhozturk predictionandoptimizationofthedesignandprocessparametersofahybriddedproductusingartificialintelligence |