Optimization of morphological process parameters in contactless laser scanning system using modified particle swarm algorithm

Pathak, Vimal Kumar; Singh, Amit Kumar

doi:10.1016/j.measurement.2017.05.049

Cited by 31 publications

(16 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental outcome at the optimized settings was also analyzed with respect to the predicted performance. The utilization of the scanning probe using PI (and GRG) at the optimal settings was predicted using (10), as follows. Figure 11: PI response graphs.…”

Section: Discussionmentioning

confidence: 99%

“…The techniques based on neural network and ANOVA were employed to predict response, based on the combination of input parameters. Lately, Pathak and Singh, [10] developed a prediction model to measure the influence of scanning angle and distance of the laser beam from the part surface using response surface methodology and ANOVA. They further optimized the parameters using modified particle swarm optimization algorithm for improved digitization accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of Scanning Parameters in Coordinate Metrology Using Grey Relational Analysis and Fuzzy Logic

Mian

Umer

Alkhalefah

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The phenomenon of coordinate measuring machines has led to a significant improvement in accuracy, adaptability, and reliability for measurement jobs. The coordinate measuring machines with scanning capabilities provide the alternative to output precise acquisition at a faster rate. However, they are less accurate as compared to discrete probing systems and slower than the noncontact techniques. Therefore, the data acquisition using a scanning touch probe needs improvement, so that it can provide commendable performance both in terms of accuracy and scanning time. The determination of appropriate scanning parameters is crucial to minimize the inaccuracy and time associated with the scanning process. However, it can be demanding as well as unreliable owing to the presence of uncertainty from a multitude of factors that may influence the measurement process. The optimization of data acquisition using a scanning touch probe is a multiresponse process which involves definite uncertainties from various sources. Therefore, multioptimization tools based on grey relational analysis coupled with principal component analysis and fuzzy logic were employed to enhance the utilization of the scanning touch probe. The work described here has the objective to identify the appropriate combination of scanning factors which can simultaneously boost the accuracy and lessen the scanning time. This study demonstrates the capability and effectiveness of the uncertainty theory based optimization methods in coordinate metrology. It also suggests that the uncertainty associated with the parameter optimization can be significantly reduced using these techniques. It has also been noticed that the results from the two techniques are in accord, which corroborates their application in coordinate metrology. The result from this study can be applied to other probing systems and can be broadened to include more experiments and parameters in various scenarios as needed by the specific application.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Scanning Parameters in Coordinate Metrology Using Grey Relational Analysis and Fuzzy Logic

Mian

Umer

Alkhalefah

2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Mathematical morphology is a nonlinear filtering method, which generally includes erosion, dilation, open operation, and close operation [18][19][20]. Mathematical morphology uses mathematical tools to process image structure components.…”

Section: Image Optimizationmentioning

confidence: 99%

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Gao

et al. 2020

Agronomy

View full text Add to dashboard Cite

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

show abstract

“…Another algorithm teaching learning based (TLBO) is a parameter less algorithm which uses the philosophy of teaching and learning based concept [42]. There are some improved algorithms [43][44][45] proposed recently by named as Modified Particle Search Algorithm and Hybrid teaching-learning based algorithm proposed by [46] combination of TLBO and PSO to solve and identify the design variables for the problem. For any algorithm, there are two phases one is exploration and exploitation.…”

Section: Synthesis Of Mechanismsmentioning

confidence: 99%

Design and development of vibratory cultivator using optimization algorithms

2019

View full text Add to dashboard Cite

Tillage system design is one of the important areas of interest for farming community. Oscillatory tillage is one such area which reduces the draft consumption and plays a crucial role to farmers during soil manipulation process. The paper deals to design a vibratory mechanism to provide a continuous motion to the tillage tool for following a particular path adopted from the literature through proper synthesis theory and procedure. A four bar mechanism is designed through proper synthesis procedure to identify the dimensions. Analytical and optimal synthesis method is followed during the design process. Optimization algorithms such as hybrid teaching-learning particle swarm optimization based algorithm (HTLPSO), teaching-learning based algorithm, and particle swarm optimization is used to find the values of the design variables. MATLAB is the software used for the synthesis and analysis process. It is observed in the study that designed four mechanism follows the required path for vibratory tillage operation. The results attained through optimization algorithm in HTLPSO performed better for the required path than other nature-inspired algorithms. Also the developed vibratory cultivator performed better in the field trials.

show abstract

Optimization of morphological process parameters in contactless laser scanning system using modified particle swarm algorithm

Cited by 31 publications

References 27 publications

Optimization of Scanning Parameters in Coordinate Metrology Using Grey Relational Analysis and Fuzzy Logic

Optimization of Scanning Parameters in Coordinate Metrology Using Grey Relational Analysis and Fuzzy Logic

Weed Density Detection Method Based on Absolute Feature Corner Points in Field

Design and development of vibratory cultivator using optimization algorithms

Contact Info

Product

Resources

About