1. Ray Tahir Mushtaq and Asif Iqbal and Yanen Wang and Aqib Mashood Khan and Muhammad S. {Abu Bakar} (2023) Parametric optimization of 3D printing process hybridized with laser-polished PETG polymer. Polymer Testing 125: 108129 https://doi.org/https://doi.org/10.1016/j.polymertesting.2023.108129, The potential of Fused Filament Fabrication (FFF) technology to produce very complex geometrics has a profound impact on the physical world. Practitioners in the industry are developing predictive methods for assessing key parameters and responses of engineering materials. Enterprises are trying to solve problems such as Flexural Strength (FS), Average Surface Roughness (Ra), Tensile Strength (TS), sustainability including energy efficacy, and time management problems. This study's objective is divided into two main parts. The First part includes the parametric investigations of Polyethylene Terephthalate Glycol (PETG) polymer to optimize the mechanical properties, Ra, and sustainability, including Print Time (T) and Printer Energy consumption (E) of samples using the FFF technique. In second part, samples were printed at optimized parametric values and further post-processed to enhance the responses such as TS, FS, Ra, and Laser Scan Time (Tl) using CO2 laser polishing treatment. FFF with PETG, a biodegradable material, is commonly used in various fields, despite the traditionally significant surface roughness of FFF products. In pre-processing, by optimizing 3D printed parameters, FS of 69.9 MPa, lowest Ra of 6.8 μm, TS of 45.1 MPa, lowest T of 53 min and E of 0.20 kWh were attained. In post-processing, the Ra decreased by more than 58.38% due to laser polishing (6.8 μm –2.81 μm). The TS increased by 8.89%, from 45.1 MPa to 49.11 MPa. SEM investigation revealed that the gaps in the material lowered the FS by 5.09%, from 69.9 MPa to 66.34 MPa and Tl for 0.22 min. The fracture morphologies were studied as a means of learning more about the reinforcing process in the future. These outcomes prove that laser scanning can improve and alter the surface of an FFF product., CO laser polishing, 3D printing, Mechanical strength, Surface roughness, Laser scan time, Energy efficiency, https://www.sciencedirect.com/science/article/pii/S014294182300209X, 0142-9418
2. Ray Tahir Mushtaq and Yanen Wang and Aqib Mashood Khan and Mudassar Rehman and Xinpei Li and Shubham Sharma (2023) A post-processing laser polishing method to improve process performance of 3D printed new Industrial Nylon-6 polymer. Journal of Manufacturing Processes 101: 546-560 https://doi.org/https://doi.org/10.1016/j.jmapro.2023.06.019, In this paper, the laser polishing method has been used to alleviate the asperities on the printed surface. The proposed laser polishing method is specifically used to solve industrial problems such as improving mechanical properties (flexural strength and tensile strength), surface roughness, and eco-friendly polishing of 3D-printed novel nylon-6 polymers. A series of experimentations were conducted using response surface methodology to see how changing the laser parameters affected the surface finish and the mechanical qualities of the test materials. Mechanical properties and scanning electron microscope-based surface analysis were compared with the pre-polishing workpiece. After multi-optimization, optimal laser scanning parameters resulted in a 20.2 % reduction in surface roughness, 8.27 % increment in flexural strength, and a 1.45 % increase in tensile strength. The optimum laser scanning time for samples was 0.23 min, and the energy consumption was 1.58E −05 kWh for one experiment. These findings prove that the relatively new post-processing laser polishing method improves the mechanical and surface properties of a 3D-printed nylon-6 polymer material. This new method is highly useful in the 3D printing industry to provide sustainable products., Additive manufacturing, Laser scanning, Mechanical properties, Surface roughness, Eco-friendly polishing, https://www.sciencedirect.com/science/article/pii/S1526612523006291, 1526-6125
3. Ray Tahir Mushtaq and Yanen Wang and Mudassar Rehman and Aqib Mashood Khan and Chengwei Bao and Shubham Sharma and Sayed M. Eldin and Mohamed Abbas (2023) Investigation of the mechanical properties, surface quality, and energy efficiency of a fused filament fabrication for PA6. REVIEWS ON ADVANCED MATERIALS SCIENCE 62(1): 20220332 https://doi.org/doi:10.1515/rams-2022-0332, 2023-09-26, https://doi.org/10.1515/rams-2022-0332
4. Mushtaq, Ray Tahir and Iqbal, Asif and Wang, Yanen and Rehman, Mudassar and Petra, Mohd Iskandar (2023) Investigation and Optimization of Effects of 3D Printer Process Parameters on Performance Parameters. Materials 16(9) https://doi.org/10.3390/ma16093392, Professionals in industries are making progress in creating predictive techniques for evaluating critical characteristics and reactions of engineered materials. The objective of this investigation is to determine the optimal settings for a 3D printer made of acrylonitrile butadiene styrene (ABS) in terms of its conflicting responses (flexural strength (FS), tensile strength (TS), average surface roughness (Ra), print time (T), and energy consumption (E)). Layer thickness (LT), printing speed (PS), and infill density (ID) are all quantifiable characteristics that were chosen. For the experimental methods of the prediction models, twenty samples were created using a full central composite design (CCD). The models were verified by proving that the experimental results were consistent with the predictions using validation trial tests, and the significance of the performance parameters was confirmed using analysis of variance (ANOVA). The most crucial element in obtaining the desired Ra and T was LT, whereas ID was the most crucial in attaining the desired mechanical characteristics. Numerical multi-objective optimization was used to achieve the following parameters: LT = 0.27 mm, ID = 84 percent, and PS = 51.1 mm/s; FS = 58.01 MPa; TS = 35.8 MPa; lowest Ra = 8.01 m; lowest T = 58 min; and E = 0.21 kwh. Manufacturers and practitioners may profit from using the produced numerically optimized model to forecast the necessary surface quality for different aspects before undertaking trials., 1996-1944, 37176273, https://www.mdpi.com/1996-1944/16/9/3392, 3392
5. Ashutosh Kumar Gupta and Sunny Chakroborty and Swarup Kumar Ghosh and Subhas Ganguly (2023) A machine learning model for multi-class classification of quenched and partitioned steel microstructure type by the k-nearest neighbor algorithm. Computational Materials Science 228: 112321 https://doi.org/https://doi.org/10.1016/j.commatsci.2023.112321, The paper proposed a machine learning model for multiclass classification of quenched and partitioned (Q &P) steel microstructure type. In this work, we implemented the k-nearest neighbor (k-NN) algorithm to train the classifier. A Q &P steel microstructure-type database has been compiled from the previous research data comprising the information of 348 steel samples. The feature space was described by the steel composition, lower critical temperature (Ac1), upper critical temperature (Ac3), martensitic-start temperature (Ms), etc., and the Q &P heat treatment parameters. At the same time, the target or dependent variable was recorded as the microstructure type, for example, martensite-retained austenite {M, RA}, martensite-bainite-retained austenite {M, B, RA} etc. The proposed classifier could achieve an overall performance of 97.7% and 77.7%, measured as f1-Score in the training and testing dataset, respectively. The martensite-retained austenite {M, RA} type was found to be the most confusing class. The model explored the effect of compositional parameters and heat treatment variables on the evolution of microstructure. The re-engineering through model study for targeted martensite-retained austenite microstructure type has depicted a steel composition and heat treatment window, which has been validated by experimental development of steel microstructure. The optical and SEM micrographs, along with hardness, strongly corroborated the model analysis from a re-engineering perspective., Quenched and partitioned steels, Machine learning, Steel microstructure, Multiclass classification, -nearest neighbor classifier, https://www.sciencedirect.com/science/article/pii/S0927025623003154, 0927-0256