Image processing approach to automate feature measuring and process parameter optimizing of laser additive manufacturing process

被引:12
作者
Patil, Deepika B. [1 ]
Nigam, Akriti [1 ]
Mohapatra, Subrajeet [1 ]
机构
[1] Birla Inst Technol Mesra, Dept Comp Sci & Engn, Ranchi 835215, Jharkhand, India
关键词
Laser additive manufacturing; Image processing; Geometric features; Measurement; Optimization; Edge detection; ALGORITHM; CLASSIFICATION;
D O I
10.1016/j.jmapro.2021.07.064
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The present research work focuses on the development of image processing technique that can automatically extract the deposition geometric features and optimize the process parameters required for manufacturing components by laser additive manufacturing process. This paper reports (i) manufacturing of vertical and horizontal wall components and capturing its images, (ii) developing robust image-processing technique for feature extraction and measurement, (iii) formulating a component sorting methodology with a capability to accept and reject component, and (iv) developing the process parameter optimizing model to identify the optimized combination of process parameters used to manufacture components. The developed image processing algorithm has been validated against the manual measurement method and CAD model. It has been observed that the proposed image processing algorithm can measure the geometric features of the vertical and horizontal wall components with an error of less than 3%. The optimization study gave the optimized value of laser power as 820 W, 850 W, 800 W and 860 W, scanning speed as 500 mm/min, 500 mm/min, 730 mm/min and 700 mm/min, and powder feed rate as 6 g/min, 10 g/min, 7 g/min and 7 g/min for effective vertical wall width, effective vertical wall height, effective horizontal wall width and effective horizontal wall height, respectively. The optimized process parameters were validated experimentally on laser based additive manufacturing process. The optimized values of effective vertical wall width, effective vertical wall height, effective horizontal wall width and effective horizontal wall height are 5.003, 14.003, 20.003 and 6.002, respectively, with corresponding experimental values as 5.028, 14.016, 20.018 and 6.028, respectively. Therefore, for the fast-growing additive manufacturing industry the proposed image processing methodology will offer benefits of automatic feature measuring process and process parameter optimizing with high accuracy and less human interference. In future, the image processing algorithm will be further developed for the real-time feature extraction of the depositions done by laser based additive manufacturing process.
引用
收藏
页码:630 / 647
页数:18
相关论文
共 28 条
[11]   A PROBABILISTIC HOUGH TRANSFORM [J].
KIRYATI, N ;
ELDAR, Y ;
BRUCKSTEIN, AM .
PATTERN RECOGNITION, 1991, 24 (04) :303-316
[12]   Selection of Process Parameters for Near-Net Shape Deposition in Wire Arc Additive Manufacturing by Genetic Algorithm [J].
Kumar, Ashish ;
Maji, Kuntal .
JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE, 2020, 29 (05) :3334-3352
[13]   Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network [J].
Liu, Huaming ;
Qin, Xunpeng ;
Huang, Song ;
Jin, Lei ;
Wang, Yongliang ;
Lei, Kaiyun .
INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING, 2018, 19 (07) :1061-1070
[14]   Randomized Hough Transform: Improved ellipse detection with comparison [J].
McLaughlin, RA .
PATTERN RECOGNITION LETTERS, 1998, 19 (3-4) :299-305
[15]   Automation of geometric feature computation through image processing approach for single-layer laser deposition process [J].
Patil, Deepika Bhanudas ;
Nigam, Akriti ;
Mohapatra, Subrajeet .
INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING, 2020, 33 (09) :895-910
[16]   Image-based adaptive crosshatch toolpath generation for laminated object manufacturing This paper proposes an algorithm for preparation of mapped layer image, placement of small and large tiles, and avoidance of uncut area [J].
Putthawong, Sarinya ;
Koomsap, Pisut ;
Chansri, Natthavika .
VIRTUAL AND PHYSICAL PROTOTYPING, 2014, 9 (04) :233-249
[17]   Rapid surface quality assessment of green 3D printed metal-binder parts [J].
Rane, Kedarnath ;
Castelli, Kevin ;
Strano, Matteo .
JOURNAL OF MANUFACTURING PROCESSES, 2019, 38 :290-297
[18]   Classification and identification of surface defects in friction stir welding: An image processing approach [J].
Ranjan, Ravi ;
Khan, Aaquib Reza ;
Parikh, Chirag ;
Jain, Rahul ;
Mahto, Raju Prasad ;
Pal, Srikanta ;
Pal, Surjya K. ;
Chakravarty, Debashish .
JOURNAL OF MANUFACTURING PROCESSES, 2016, 22 :237-253
[19]  
Sathiya P, 2011, INT J ENG SCI TECHNO, V2, P6, DOI [10.4314/ijest.v2i6.63710, DOI 10.4314/IJEST.V2I6.63710]
[20]   Theoretical modeling and finite element simulation of dilution in micro-plasma transferred arc additive manufacturing of metallic materials [J].
Sawant, Mayur S. ;
Jain, N. K. ;
Nikam, Sagar H. .
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, 2019, 164