1. Introduction

Because color has a vital function for the production of polycarbonate pigments, materials need to be extruded with good dispersion properties and uniform particle sizes. In order to yield plastic with a commercial sable color, adding pigments to it is usually required. However, a great challenge is likely to be faced to attain the required color from the first attempt. Several variables affect the color properties of polymers compounding during their extrusion steps, including temperature, screw speed, feed rate, residence time, and screw configuration. Several researchers investigated the effects of such process variables on yielded color during polymers compounding [1- 2]. Being constituted of chemical species, it is likely that the pigments will take part in chemical reactions depending on process conditions. Thus, the correct selection of the right variables is vital to attain the color requirements.  Furthermore, the time-temperature relationship can also affect the polymer characteristics. The required pigments dispersion and good uniformity can be attained by decreasing the viscosity of the resin and increasing the mixing time [3].

Spectrophotometers can serve as important measures to control the quality, quantify color, and numerically compare variations in colors [3].

Allowable tolerance limits in particular terms of dL*, da*, db* or dE* are usually chosed by the client; however, for the polycarbonate grade-3 under this study, limits were equal or less ≤ 1.0 for dE or ≤ 0.6 for dL*, da*, db*  [4]. The deviation in L*, a*, b* is represented as “dE*, where

Instead of using absolute values of color, color differences concerning target values regarding dL*, da* and db* are used. The total change in color, dE* is used to represent the color difference in the CIELAB color space [5].

Design of Experiments (DOE) is a planned approach that allows an experimenter to plan the experiments and determine cause-and-effect relationships. DOE is extensively used in numerous areas of science because it reduces the number of experiment that need to be performed.

For optimal dispersion of these pigments, the optimization of extrusion process parameters is required. Researchers designed experiments to evaluate the effect of process parameters on colour properties of a compounded polycarbonate grade. A regression model was generated. Several factors  were  contributed to color mismatch. Such factors need to be studied to understand their effect on output colour [6 ,7, 8].

Many experimental designs have been recognized as useful techniques to optimize process variables. A modified general factorial DOE has been employed for investigating the effect of changes in compounding process variables on gloss and surface appearance of a PVC sheet. [9] .Different types of RSM designs are available, including a factorial design, central composite design (CCD), Box-Behnken design, and D-optimal design. [10] The execution of a DOE involving the Box-Behnken design (BBD) has been reported to determine a relationship between processing parameters and viscosity variation for a wood-plastic compound [11].

The BBD, being a combined array design, requires fewer runs than Taguchi’s crossed array designs and allows estimation for significant interactions. [12] It is the most efficient design in terms of runs and requires only three levels of each factor in order to generate a quadratic model. [12,13] To estimate curvature, other designs require either five levels of each factor such as in a central composite design (CCD) or even more experimental runs such as in a three level factorial design.

Analysis-of-variance (ANOVA) is essential to validate the significance and fitness of the model; it explains, whether the developed quadratic model is meaningful. It investigated the bearing of process parameters and interaction of these parameters. The robustness of RSM designs is ensured by considering the propagation of error (POE). POEs, a measure of the standard deviation of the transmitted variability in the output response, are caused by fluctuations in significant controllable process variables during experimentation assuming uncontrollable factors (noise) to be zero. [14].

In this study, an experimental investigation of the processing parameters was conducted using DOE. DOE was utilized to determine the optimum number of experiments to be run so that sufficient data was available for analysis. The designs were prepared for three processing parameters: temperature, speed, and feed rate. The effect of the processing parameters on output response parameters was studied. Experimentation for various grades was carried out to observe the effect of controlled variation of different processing parameters on the colour attributes of compounded plastics. The results were analyzed to determine an optimum set of processing parameters in order to ensure minimum wastages and timely delivery of orders.

Statistical Design of Experiments can be used to study the color responses to variation in these processing parameters with the help of methods such as the Response Surface Methodology (RSM). In this approach the first step is to properly design experiments in order to evaluate model parameters efficiently after performing experiments. Second step is to develop a second order polynomial for the responses [14].

Where y is the predicted response, b₀ is a constant, b_i is the ith linear coefficient, b_ii is the ith quadratic coefficient, b_ij is the ith interaction coefficient, x_i is the independent variable, k is number of factors and ε is error. Coefficients of the model predicted through regression of the obtained experimental data. Details of parameter estimations for the model done by these authors are reported elsewhere [13]. RSM is a collection of statistical and mathematical techniques useful for developing, improving and optimizing process. The Three-level full factorial design is one of the most powerful and efficient experimental design among other response surface designs (central composite, Doehlert matrix, and Box Behnken designs). The ultimate aim of the present study is to employ the 3 level full factorial design to optimize the processing parameters to have a minimum deviation in color properties (dE* < 0.8)

Focus was extended to study the variations of independent of processing parameters. Parameters were usedare such as temp, speed, and feed rate were used to affect the dependent responses for consistent output color (L*,a*,b*,dE*). The procedure of controlling the variations of two Processing parameters and keeping the third parameter constant (general trends), focused on the variation of the optimal federates parameters to achieve a minimum desirable deviation dE* of 0.3.

Design optimization of the two procedures to precisely determine cause and effect relationships. Both designs yielded models that were statistically significant and optimal color were found.

2. Experimental Set up

Experimentation was carried out for the investigated material at Industrial Plant. A blend of two polycarbonate resins was used along with four different pigments, the color formulation of these grades in parts per hundred (PPH) is presented in Table 1. The melt flow index (MFI) for Resin 1 was 25 gm/10min, and that of Resin 2 was 6.5 gm/10min.

Table 1: Color formulation used for investigated grade

3. Design of Experiments

The Design of Experiments (DOE) containing the 27 experimental runs was used to implement a Three-Level Full Factorial Design and a DOE containing 9 experimental runs was used to implement variations of the processing parameters while other parameters are constant (General Trends) are shown in Table 2  and Table 3 respectively .

Table 2: Design level in actual and coded unit

3.1.  3 Level Full Factorial Design

The processing was carried out on a twin- screw extruder of 25.5 mm diameter, with ratios of L/D=37 and D_o/D_i=1.55. The materials were extruded in an intermeshing , ZSK26- Coperion -Germany ,27 kW twin co rotating screw extruder (TSE).  The three process parameters, the temperature of the heating zones, feed rate to the extruder, and the screw speed were considered in the experimental design, and the levels used are shown in Table 2.  Parameters were varied on 27 different treatments with additional five center points, the total of runs are (32 treatments) for 3 level full factorial response method to study their effects on color. The additional five centre points were added to estimate the experimental error and for the detection of nonlinearity in the responses [13].

Extruded melt was quenched in cold water, air dried and then pelletized. Using injection molding, the pellets from each run were molded into three rectangular chips (3x2x0.1”) after which their values (CIE L*, a*, b*) were investigated by utilizing a spectrophotometer (CE 7000A, X rite- Inc. USA). Target color output for these values were L*= 70.04, a*= 3.41, and b*= 18.09. Statistical analysis of data was performed using The Design Expert Software (Version 8, Stat-Ease Inc. USA) to quantify and relate the effects of variables at a confidence interval of 95%. In order to attain zero deviation from target color, numerical optimization of the data was performed.

3.2. Variations of the Processing Parameters (General Trends)

Because color is directly related to the process parameters involved, herein, we performed a control study to investigate the effects of operating variables (temperature, speed, and feed rate) on color. Three processing parameters were controlled individually at three different stages, while fixing all other parameters (GT). Based on our observed strong correlations between the processing variables and the color generated, we conclude the following recommenations: Flow rate was 20 kg/hr, 25 kg/hr, and 30 kg/hr, at a speed of 750 rpm and a temperature of 255 °C [15-17] .

 The selected processing temperatures were 230°C, 255°C and 280°C with a speed and flow rate fixed at the middle values (750 rpm and 25 kg/hr, respectively). A similar procedure was followed for both speed and flow rate. The selected speeds were 700, 750 and 800 rpm and the selected flow rates were 20, 25, and 30 kg/hr. The following tables show the experimental processing conditions. The general trends (GT) experimental design is shown in Table 3.

Assuming that the aforementioned variables were utilized, in this work we suggest optimized process parmeters to attain plastic grade color consistency.

Table 3: Processing Parameters Variables

4. Results

Using analysis of variance (ANOVA), correlations between variables were investigated and processing parameters were optimized to generate resulting color properties. The Design Expert software was used to the effects of the operating process parmeters on  dL*, da*, db*. We performed sequential F-tests, by utilizing an initial linear model, modified by subsequently adding suitable linear or quadratic terms [20]. The F-statistic was considered for each type of model, and the highest order model with significant terms was chosen. Analysis of all tristimulus values was based on the same process. Based on the F statistics £ 0.05 and probability values £ 0.1, only significant terms were added. Our ANOVA sequential model sum of squares results for dL*, da*, db* are given in Table 4.

The highest order model with significant terms (Prob > F is less than 0.05) are the 2F models, and are applicable to describe the dL*, da* and db* responses. R² values (Table 4) provide confirmation and suggest that a variability of 78% in dL*, da* and db* is decreased to 74 %. The remainng variability is unexplained and can be ascribed to noise.  The “Predicted R-Squared” and the “Adjacent R-Squared” are in rational agreement. A signal to noise ratio larger than 4 is usually desirable since it suggests that the model can be utilized for navigation into the design space, and can be quantified by “Adequate Precision”. The “Lack of the fit” test was utilized to compare between the residual and the pure error, and resembled a p-value > 0.05, which is insignificant.

Table 4: ANOVA results for dL*, da* and db* response

To generate predicted responses for the dL*, da* db*, several models based on linear regression were produced as given in Table 5. The effects of temperature, speed, and feed rate operating variables and their interactions are represented by the polynomial equations. The effect of these variables on the responses correlate with the coefficient values. The differnce between predicted and actual values is in the 0.2 limit suggesting a strong agreement between these 2 values.

RSM curves were used to investigate interactions and optimize process parameters variables. The contour graph (Figure 1a) illustrates the first order relation between temperature and speed, at the feed rate  of 24.71kg/hr  for dL*, and resembles that various temperature and speed combinations can satisfy the required objective.  Moreover, the contour graph (Figure 1b) illustrates the relation between temperature and the feed rate of 741.2 rpm. Finally, the contour graph (Figure 1c) illustrates the relation between speed and feed rate at temperature of 245.2 ^oC. The global optimal value (dL* = 0.0) is realized at 245.2 ^oC, 741.2 rpm and 24.71 kg/hr as demonstrated in the graphs. The maximum and minmum dL* values at 95% confidence interval are both equal to 0.11.

Table 5:  Regression Model for dL*, da* and db*

Table 6: dL*, db* and da* Actual and Predicted Values

Figure 2 shows the interaction between speed and feed rate for da* at 245.2 ^oC, resembling the first order relationship and suggest that several feed rate and speed combinations can satisfy the objective. The optimum value of da* = 0.15 is realized at 741.2 rpm and 24.7 kg/hr. The maximum and minimum allowable values at 95% confidence interval for da* are 0.23 and 0.07 respectively.

Figure 1: (a)  Interaction between temp and speed at 24.7 kg/hr for dL* (b) Temp and feed rate at 741.2 rpm for dL* (c) Speed and feed rate at 245.2 ^oC for dL*.

Figure. 2: Speed and feed rate interaction at  temperature of 245.2. ^oC for da *

Figure 3: Speed and feed rate contour plot for d b* at 245.2 ^oC

Figure 4: Desirability Graph at temperature of 245.2 ^oC

(A= Temp; B= Speed, C= Feed rate)

The interaction between speed and feed rate for db* at 245.2 ^oC is shown in Figure 3.  The linear behavior includes only speed and feed rate as significant model terms as compared to dL* and da*. Interestingly, at lower values of speed and feed rate around 20 kg/hr and as well as at higher values of speed and feed rate around 24 kg/hr, db* approaches zero, while temperature stays at 245.2 ^oC. At the global optimum, the predicted value of db* is equal to -0.19 and the allowable maximum and minimum values at 95% confidence interval are -0.05 and -0.33 respectively.

An important effect on the color responses was exerted by temperature (A), speed (B) and feed rate (C).  The optimal settings of the three parameters regarding all responses were realised by utilizing a decision making method based on multiple criteria, and a total desirability function “d” [19]. A desirability function is quantifies the quality and presents a convenient responses comparison method to select the optimal settings; Figure 4 shows a 3D plot of the global desirability D, maintaining a feed rate of 24.7 kg/hr, and indicates that the maximum combined desirability of 77% is attained at 245.2^oC, 741.2 rpm, and 24.7 kg/hr.

5. Effect of variation feed rate on color values

A plot of feed rate variation from 20 kg/hr to 30 kg/hr with a fixed speed at 750 kg/hr and a fixed temperature at 255 °C is shown in Figure 5; The difference in color decreases initially as the feed rate increases up to 30 kg/hr.[20-22]

A response contours plot of speed versus feed rate at a temperature of 245.2 ^oC is shown in Figure 6, and resembles the presence of region that is feasible to achieve target values. The plot shows a region between the da*=0.30 and db*=0.20 contours which illustrates the temperature and speed operation conditions at which the mean responses (dL*, da*, db* and SME) target are met at a fixed feed rate of 24.44 kg/hr.

The results suggest that the optimal tristimulus values of dL* = 0.0, da * = 0.15, db* = – 0.19 are attained at 245.2 ^oC, 741.2 rpm, and 22.7 kg/hr. The total minimum deviation in tristimulus values (using equation 1) is equal to 0.25, which is reasonably acceptable as compared with the maximum allowable deviation (dE* = 0.8).

Figure 5: Effect of the variation of feed rate at fixed a fixed temperature of 255 °C, and a fixed speed of 750 rpm.

Figure. 6: Speed and feed rate overlay plot at a temperature of 245.2 ^oC.

Figure 7 :  Rectangular color  chip (3x2x0.1”)

Using the lab injection molding, the extruded pellets were molded into a rectangular chip (3x2x0.1”) after which the color values (CIE L*, a*, b*) were examined by using a spectrophotometer (CE 7000A, X rite- Inc. USA). The image of the specimen is shown in Figure 7.

Figure 8: Scanning Electron Microscopy images at feed rates of (a) 20 kg/hr, (b) 25 kg/hr, and (c) 30 kg/hr, at a fixed temperature of 255 °C, and a fixed speed of 750 rpm.

Scanning Electron Microscopy (SEM) images were probed using a Joel 5500 LV at a 20 kV voltage, at different feed rates, and are shown in Figure 8. The images illustrate that primary particles and agglomerates were present in the compounded grades. The images show that larger agglomerated pigments could be observed at a lower feed rate as compared with higher feed rates [20-22].

Conclusions

An experimental design approach which relied on three levels full-factorial surface method was utilized to study the effects of operational process variables, and suggested appropriate predictive models for dL*, da*, and db*. We used a regression model to calculate tristimulus values and validated our work by confiming that a good agreement exists with experimental results. At target values of 245.2 ^oC, 741.2 rpm, and 24.7 kg/hr, tristimulus values were close, with a minimum total deviation (dE*) of 0.25 at a 95% confidence interval. An experimental design approach, which relied on the interaction between tristimulus color values and processing parameters showed that the minimum color difference throughout the experiment was  equal to 0.34 for dE*. As the feed rates increased from 20 kg/hr to 30 kg/hr, the color difference values (dE*) substantially decreased. Finally, an optimum set of processing parameters for the grades of the polycarbonate can be yielded through the utilization of the optimized parameters, and hence reduce colors mismatch so that wastages can be reduced.

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30. Mariutsi Alexandra Osorio-Sanabria, Astrid Jaime, Tamara Alcantara-Concepcion, Piedad Barreto, "Open Access Research Trends in Higher Education: A Literature Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 499–511, 2021. doi: 10.25046/aj060257
31. Khalil Ibrahim Mohammad Abuzanouneh, Khalil Hamdi Ateyeh Al-Shqeerat, "Development and Improvement of Web Services Selections using Immigrants Scheme of Multi-Objective Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 412–422, 2021. doi: 10.25046/aj060248
32. Musa Sulaiman Jibia, Abdussamad Umar Jibia, "Fetal Electrocardiogram Extraction using Moth Flame Optimization (MFO)-Based Adaptive Filter", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 303–312, 2021. doi: 10.25046/aj060235
33. Nora Bouhaddour, Abdelkrim Moufakkir, Sara Belarouf, Abderrahim Samaouali, Hanane Sghiouri El Idrissi, Abdellah Elbouzidi, Salah El Alami, "Study of Thermo-Physical Characteristics and Drying of Araucaria Wood from the City of El Jadida, Morocco", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 274–278, 2021. doi: 10.25046/aj060231
34. Amany Khalil, Osama Tolba, Sherif Ezzeldin, "Design Optimization of Open Office Building Form for Thermal Energy Performance using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 254–261, 2021. doi: 10.25046/aj060228
35. Abdelaziz Lberni, Malika Alami Marktani, Abdelaziz Ahaitouf, Ali Ahaitouf, "Multi-Objective Design of Current Conveyor using Optimization Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 154–160, 2021. doi: 10.25046/aj060218
36. Sk. Md. Masudul Ahsan, Aminul Islam, "Visual Saliency Detection using Seam and Color Cues", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 139–153, 2021. doi: 10.25046/aj060217
37. Md. Ashfaqul Islam, Maisha Hasnin, Nayeem Iftakhar, Md. Mushfiqur Rahman, "Super Resolution Based Underwater Image Enhancement by Illumination Adjustment and Color Correction with Fusion Technique", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 36–42, 2021. doi: 10.25046/aj060205
38. Bryan Huaytalla, Diego Humari, Guillermo Kemper, "An algorithm for Peruvian counterfeit Banknote Detection based on Digital Image Processing and SVM", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1171–1178, 2021. doi: 10.25046/aj0601132
39. Arwa A. Al Shamsi, Sherief Abdallah, "Text Mining Techniques for Sentiment Analysis of Arabic Dialects: Literature Review", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1012–1023, 2021. doi: 10.25046/aj0601112
40. Subash Pokharel, Aleksandar Dimitrovski, "Ferromagnetic Core Reactor Modeling and Design Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 810–818, 2021. doi: 10.25046/aj060190
41. Reem Bayari, Ameur Bensefia, "Text Mining Techniques for Cyberbullying Detection: State of the Art", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 783–790, 2021. doi: 10.25046/aj060187
42. Jesus Aguila-Leon, Cristian Chiñas-Palacios, Carlos Vargas-Salgado, Elias Hurtado-Perez, Edith Xio Mara Garcia, "Particle Swarm Optimization, Genetic Algorithm and Grey Wolf Optimizer Algorithms Performance Comparative for a DC-DC Boost Converter PID Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 619–625, 2021. doi: 10.25046/aj060167
43. Hendro Arieyanto, Andry Chowanda, "Classification of Wing Chun Basic Hand Movement using Virtual Reality for Wing Chun Training Simulation System", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 250–256, 2021. doi: 10.25046/aj060128
44. Broderick Crawford, Ricardo Soto, Gino Astorga, José Lemus-Romani, Sanjay Misra, Mauricio Castillo, Felipe Cisternas-Caneo, Diego Tapia, Marcelo Becerra-Rozas, "Balancing Exploration-Exploitation in the Set Covering Problem Resolution with a Self-adaptive Intelligent Water Drops Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 134–145, 2021. doi: 10.25046/aj060115
45. Olayinka Oluwaseun Oluwasina, Surjyakanta Rana, Sreekantha Babu Jonnalagadda, Bice Susan Martincigh, "Synthesis and Characterization of Graphene Oxide Under Different Conditions, and a Preliminary Study on its Efficacy to Adsorb Cu\(^{2+}\)", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 10–16, 2021. doi: 10.25046/aj060102
46. Yaswanthkumar S K, Keerthana M, Vishnu Prasath M S, "A Machine Vision Approach for Underwater Remote Operated Vehicle to Detect Drowning Humans", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1734–1740, 2020. doi: 10.25046/aj0506207
47. Hoang Xuan Thinh, Pham Van Dong, Tran Ve Quoc, "A Study on the Tool Wear in Milling Process of the Gleason Spiral Bevel Gear", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1402–1407, 2020. doi: 10.25046/aj0506169
48. Gehad Ali Alsayed, Zahraa Ismail, Sameh O. Abdellatif, "Investigating the Optical Behavior of Single/Multi-Dimensional Photonic Crystal Structures for Photovoltaic Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 951–958, 2020. doi: 10.25046/aj0506113
49. Luisella Balbis, "Optimal Irrigation Strategy using Economic Model Predictive Control", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 781–787, 2020. doi: 10.25046/aj050693
50. Azani Cempaka Sari, Natashia Virnilia, Jasmine Tanti Susanto, Kent Anderson Phiedono, Thea Kevin Hartono, "Chatbot Developments in The Business World", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 627–635, 2020. doi: 10.25046/aj050676
51. Pearl Keitemoge, Daniel Tetteh Narh, "Effective Application of Information System for Purchase Process Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 594–605, 2020. doi: 10.25046/aj050673
52. Jojo Blanza, Lawrence Materum, "Interface for Visualization of Wireless Propagation Multipath Clustering Outcomes", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 544–549, 2020. doi: 10.25046/aj050665
53. Wen-Fei Hsieh, Henry Lin, Vincent Chen, Irene Ou, Yung-Song Lou, "The Probe Mark Discoloration on Bond Pad and Wafer Storage", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 416–422, 2020. doi: 10.25046/aj050650
54. Sergey Alekseevich Serebryansky, Alexander Vladimirovich Barabanov, "To the Question of Multi-Criteria Optimization of Aircraft Components in Order to Optimize its Life Cycle", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 408–415, 2020. doi: 10.25046/aj050649
55. Athraa Ali Kadhem, Noor Izzri Abdul Wahab, Ahmed Abdalla, "The Contribution of Wind Energy Capacity on Generation Systems Adequacy Reliability using Differential Evolution Optimization Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 331–340, 2020. doi: 10.25046/aj050640
56. Simona Kirilova Filipova-Petrakieva, "Applications of the Heuristic Optimization Approach for Determining a Maximum Flow Problem Based on the Graphs’ Theory", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 175–184, 2020. doi: 10.25046/aj050621
57. Khalid Ait Hadi, Rafik Lasri, Abdellatif El Abderrahmani, "Inferring Topics within Social Networking Big Data, Towards an Alternative for Socio-Political Measurement", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 155–159, 2020. doi: 10.25046/aj050618
58. Ihsan Mizher Baht, Petre Marian Nicolae, Ileana Diana, Nameer Baht, "Analysis of Green Building Effect on Micro grid Based on Potential Energy Savings and BIM", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 30–35, 2020. doi: 10.25046/aj050604
59. Yanlin Pan, Jia Rui Thong, Pik Kee Tan, Siong Luong Ting, Chang Qing Chen, "Laser Deprocessing Technique and its Application to Physical Failure Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1273–1281, 2020. doi: 10.25046/aj0505153
60. Sherif H. ElGohary, Aya Lithy, Shefaa Khamis, Aya Ali, Aya Alaa el-din, Hager Abd El-Azim, "Interactive Virtual Rehabilitation for Aphasic Arabic-Speaking Patients", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1225–1232, 2020. doi: 10.25046/aj0505148
61. Mounir Amraoui, Rachid Latif, Abdelhafid El Ouardi, Abdelouahed Tajer, "Feature Extractors Evaluation Based V-SLAM for Autonomous Vehicles", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 1137–1146, 2020. doi: 10.25046/aj0505138
62. Adrian Florea, Valentin Fleaca, Simona Daniela Marcu, "Innovative Solution for Parking-Sharing of Private Institutions Using Various Occupancy Tracking Methods", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 808–819, 2020. doi: 10.25046/aj050598
63. Imad El Hajjami, Bachir Benhala, Hamid Bouyghf, "Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 426–433, 2020. doi: 10.25046/aj050553
64. Ladislav Burita, Ales Novak, "ISR Data Processing in Military Operations", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 314–331, 2020. doi: 10.25046/aj050540
65. Vu Nguyen Hoa Hong, Luong Tuan Anh, "Development Trends of Smart Cities in the Future – Potential Security Risks and Responsive Solutions", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 548–556, 2020. doi: 10.25046/aj050465
66. Mohammed Qbadou, Intissar Salhi, Hanaâ El fazazi, Khalifa Mansouri, Michail Manios, Vassilis Kaburlasos, "Human-Robot Multilingual Verbal Communication – The Ontological knowledge and Learning-based Models", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 540–547, 2020. doi: 10.25046/aj050464
67. Roberta Avanzato, Francesco Beritelli, "A CNN-based Differential Image Processing Approach for Rainfall Classification", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 438–444, 2020. doi: 10.25046/aj050452
68. Hana Yousuf, Said Salloum, "Survey Analysis: Enhancing the Security of Vectorization by Using word2vec and CryptDB", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 374–380, 2020. doi: 10.25046/aj050443
69. Shahab Pasha, Jan Lundgren, Christian Ritz, Yuexian Zou, "Distributed Microphone Arrays, Emerging Speech and Audio Signal Processing Platforms: A Review", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 331–343, 2020. doi: 10.25046/aj050439
70. Rand Talib, Alexander Rodrigues, Nabil Nassif, "Energy Recovery Equipment and Control Strategies in Various Climate Regions", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 47–53, 2020. doi: 10.25046/aj050407
71. Pham Van Bach Ngoc, Bui Trung Thanh, "Dynamics Model and Design of SMC-type-PID Control for 4DOF Car Motion Simulator", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 557–562, 2020. doi: 10.25046/aj050369
72. Ayyoub El Berbri, Adil Saadi, Seddik Bri, "Design and Optimization of Dual-Band Branch-Line Coupler with Stepped-Impedance-Stub for 5G Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 355–360, 2020. doi: 10.25046/aj050346
73. Ivan Boban, Alen Doko, Sven Gotovac, "Sentence Retrieval using Stemming and Lemmatization with Different Length of the Queries", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 349–354, 2020. doi: 10.25046/aj050345
74. J. Vijay Fidelis, E. Karthikeyan, "Estimation of Influential Parameter Using Gravitational Search Optimization Algorithm for Soccer", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 340–348, 2020. doi: 10.25046/aj050344
75. Jesuretnam Josemila Baby, James Rose Jeba, "A Hybrid Approach for Intrusion Detection using Integrated K-Means based ANN with PSO Optimization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 317–323, 2020. doi: 10.25046/aj050341
76. Noraziah Adzhar, Yuhani Yusof, Muhammad Azrin Ahmad, "A Review on Autonomous Mobile Robot Path Planning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 236–240, 2020. doi: 10.25046/aj050330
77. Dennis Luqman, Sani Muhamad Isa, "Machine Learning Model to Identify the Optimum Database Query Execution Platform on GPU Assisted Database", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 214–225, 2020. doi: 10.25046/aj050328
78. Sally Almanasra, Ali Alshahrani, "Alternative Real-time Image-Based Smoke Detection Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 123–128, 2020. doi: 10.25046/aj050316
79. Vasiliy Olonichev, Boris Staroverov, Maxim Smirnov, "Dynamic Objects Parameter Estimation Program for ARM Processors Based Adaptive Controllers", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 34–40, 2020. doi: 10.25046/aj050305
80. Aditi Haresh Vyas, Mayuri A. Mehta, "A Comprehensive Survey on Image Modality Based Computerized Dry Eye Disease Detection Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 748–756, 2020. doi: 10.25046/aj050293
81. Ricardo Simões Santos, António João Pina da Costa Feliciano Abreu, Joaquim José Rodrigues Monteiro, "Using Metaheuristics-Based Methods to Provide Sustainable Market Solutions, Suitable to Consumer Needs", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 399–410, 2020. doi: 10.25046/aj050252
82. ?ahin Aydin, Mehmet Nafiz Aydin, "A Sustainable Multi-layered Open Data Processing Model for Agriculture: IoT Based Case Study Using Semantic Web for Hazelnut Fields", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 309–319, 2020. doi: 10.25046/aj050241
83. Hind El Hassani, Nour- Eddine Boutammachte, Sanae El Hassani, "Optimization of Low Temperature Differential Stirling Engine Regenerator Design", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 272–279, 2020. doi: 10.25046/aj050235
84. Johannes Linden, Xutao Wang, Stefan Forsstrom, Tingting Zhang, "Productify News Article Classification Model with Sagemaker", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 13–18, 2020. doi: 10.25046/aj050202
85. Nguyen Tuan Anh, Hoang Thang Binh, Tran The Tran, "Optimization of the Stabilizer Bar by Using Total Scores Method", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 431–435, 2020. doi: 10.25046/aj050155
86. Brian Meneses-Claudio, Witman Alvarado-Diaz, Avid Roman-Gonzalez, "Classification System for the Interpretation of the Braille Alphabet through Image Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 403–407, 2020. doi: 10.25046/aj050151
87. Guillermo Kemper, David Atencia, Ivan Ortega, Roberto Kemper, Alejandro Yabar, "An Algorithm for Automatic Measurement of KI-67 Proliferation Index in Digital Images of Breast Tissue", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 201–211, 2020. doi: 10.25046/aj050126
88. Mohamed Bakry El_Mashade, Haitham Akah, Shimaa Abd El-Monem, "Windowing Accuracy Evaluation for PSLR Enhancement of SAR Image Recovery", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 48–57, 2020. doi: 10.25046/aj050107
89. Temitayo Olayemi Olowu, Mohamadsaleh Jafari, Arif Sarwat, "A Multi-Objective Voltage Optimization Technique in Distribution Feeders with High Photovoltaic Penetration", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 377–385, 2019. doi: 10.25046/aj040648
90. Mohd Razif Idris, Imad Mokhtar Mosrati, "Optimization of the Electrical Discharge Machining of Powdered Metallurgical High-Speed Steel Alloy using Genetic Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 255–262, 2019. doi: 10.25046/aj040632
91. Jalal Benallal, Lekbir Cherif, Mohamed Chentouf, Mohammed Darmi, Rachid Elgouri, Nabil Hmina, "A New Wire Optimization Approach for Power Reduction in Advanced Technology Nodes", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 140–146, 2019. doi: 10.25046/aj040617
92. Youssef Bikrat, Khalid Salmi, Kamal Azghiou, Ahmad Benlghazi, Abdelhamid Benali, and Driss Moussaid, "Intelligent Wireless System for PV Supervision Based on The Raspberry Pi", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 94–98, 2019. doi: 10.25046/aj040611
93. Wilson Babu Musinguzi, Ibrahim Luqman Mpungu, "The Impact of Using Upgraded Biogas on Generator Performance", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 186–192, 2019. doi: 10.25046/aj040524
94. Andrei Panteleev, Valentin Panovskiy, "Application of Open-Source Optimization Library “Extremum” to the Synthesis of Feedback Control of a Satellite", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 23–29, 2019. doi: 10.25046/aj040503
95. Houcine Marouani, Amin Sallem, Mondher Chaoui, Pedro Pereira, Nouri Masmoudi, "Multiple-Optimization based-design of RF Integrated Inductors", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 574–584, 2019. doi: 10.25046/aj040468
96. M. Monica Subashini, Abhinav Deshpande, Ramani Kannan, "Study and Implementation of Various Image De-Noising Methods for Traffic Sign Board Recognition", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 545–560, 2019. doi: 10.25046/aj040466
97. Mohamad Faiz Ahmad, Syed Sahal Nazli Alhady, Ooi Zhu Oon, Wan Amir Fuad Wajdi Othman, Aeizaal Azman Abdul Wahab, Ahmad Afiq Muhammad Zahir, "Embedded Artificial Neural Network FPGA Controlled Cart", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 509–516, 2019. doi: 10.25046/aj040461
98. Jaya V. Gaitonde, Rajesh B. Lohani, "Material, Structural Optimization and Analysis of Visible-Range Back-Illuminated OPFET photodetector", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 485–502, 2019. doi: 10.25046/aj040459
99. Olfa Jedda, Ali Douik, "Optimal Discrete-time Sliding Mode Control for Nonlinear Systems Subject to Input Constraints", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 141–146, 2019. doi: 10.25046/aj040417
100. Fernando Hernández, Roberto Vega, Freddy Tapia, Derlin Morocho, Walter Fuertes, "Early Detection of Alzheimer’s Using Digital Image Processing Through Iridology, An Alternative Method", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 126–137, 2019. doi: 10.25046/aj040317