1,720,989 research outputs found
Statistical modelling and optimization of nanosecond Nd:YAG Q-switched laser scarfing of carbon fiber reinforced polymer
No full textThis paper explores the effects of laser scarfing on a CFRP tape consisting of unidirectional carbon fiber with a layup of [(45/0/-45/90)4] sym with a total laminate dimension of 5.952 mm. The laser system used in the experimental investigations consisted of a nanosecond Nd: YAG laser source with an average maximum power of 20 W in a pulsed mode. The material was a with a total laminate of 5.952 mm. The laser process aims to investigate the surface modification by laser ablation. In more details, the effects of scanning speed (550 - 650 mm/s) and frequency (23 - 27 kHz) for three different scanning strategies on the scarfing depth, specimen dimensions (in x and y direction) and surface conditions were studied by full factorial experimental plan. By scarfing, the fibers were either oxidized, partially stripped, stripped and excessively stripped. After the statistical analysis, the best results in terms of surface conditions were achieved with the x-parallel laser scanning strategy, where the presence of excessively stripped fibers was not noticed. The mixed-hatching (MH) mode was the strategy that produced the worst results for the scarfing depth, which can negatively affect the ablation rate, even if it turns out to be the most stable strategy. The best results in terms of scarfing depth and x-y dimensions were achieved with MH mode, and with a scanning speed of 600 mm/s and a frequency of 27 kHz
A methodology for multi-object optimization of laser/MIG hybrid welding process
No full textIn this article a methodology for multi-object optimization of laser/MIG hybrid welding of Al-Mg alloy is proposed. For modeling and analyzing the experimental responses, the response surface methodology (RSM) and the multiple response prediction (MRP) approaches were coupled. The laser power and the process speed were the input parameters, while heat affected zone, the porosity and the lack of penetration sizes the experimental outputs. Then, the optimized solution was implemented in a validated FEM model, for quicker numerical experiments. Finally, the methodology was tested with an experiment. The presented methodology can be applied to optimize weld quality in various welding processes
Numerical and experimental investigation of probeless friction stir spot welding of a multilayer aluminium alloy compound
No full textThis paper presents the experimental and numerical results of the influence of dwell time (30, 60, 90 s), down force (2450, 4900, 7350 N), and rotational speed (1000, 1500, 2000 RPM) on microstructure, microhardness, and material flow during solid-state welding. Probeless friction stir spot welding (P-FSSW) with a flat tool shoulder was assisted to form a metal compound of AA2024, AA6082, and AA5754 aluminium plates with different thicknesses in lap configuration. The analysis of the experimental data and numerical results showed that down force had a significant influence on the material flow and the quality of the welds. High friction energy, and subsequent intensive material flow, promoted the vortexes formation, which improve metals mixing and grain refinements
Integrated IoT-based production, deep learning, and Business Intelligence approaches for organic food production
No full textThe organic food processing industry grapples with several complex challenges, such as ensuring the ingredients' authenticity, reducing resource consumption, and maintaining consistent product quality despite fluctuating demand and the supply seasonal nature. Previous methodologies often lacked integration of real-time data and advanced predictive analytics, leading to inefficiencies and increased waste. This study proposes a novel framework that combines IoT sensor networks, deep learning algorithms, and business intelligence to optimize production processes in organic tomato processing. By employing a Long Short-Term Memory (LSTM) model, the framework effectively predicts sales, manages raw material procurement and enhances logistics based on real-time data inputs. Findings indicate a 25 % improvement in productivity and a 20 % reduction in waste during production, alongside a 30 % increase in profitability attributed to informed pricing strategies and enhanced supplier quality management. The integration of predictive analytics not only aligns production with consumer demand but also supports sustainable practices by minimizing overproduction and waste. This work addresses the critical intersection of technology and sustainability in food production, ultimately contributing to a more resilient and efficient organic food supply chain. Keywords: Organic food, data mining, deep learning, Business Intelligenc
Grey Relational Analysis vs. Response Surface Methodology for the Prediction of the Best Joint Strength in Hybrid Welding of TWIP/DP Steels
No full textAdvanced High Strength Steels (AHSSs) have been developed to offer high strength and formability for automotive and aerospace applications. This work proposes a methodology to improve the mechanical performances of welded TWIP/Dual Phase steels by a hybrid laser/MAG process. The Response Surface Methodology (RSM) and the Grey Relational Analysis (GRA) have been used to predict the tensile strength and elongation at fracture of the TWIP/DP joints and compared each other. A grey factorial plan has been used to perform the experimental welding tests. From the regression analysis, the determination coefficient is higher than 86%, indicating a high correlation between the experimental and predicted values. From the optimization analysis, the best combination of process parameters is 2.25 kW and 3.3 m/min for the statistical analysis, while 2 kW and 3 m/min for the grey analysis, which lead to quite similar UTS and maximum strain values. The RSM and GRA methodology are both suitable for predicting responses in the case of gray systems
Multi-objective optimization of laser milling of 5754 aluminum alloy
No full textLaser milling is a new, very flexible process for micro-fabrication, suitable for machining difficult-to-machine materials, like ceramics, dielectrics, carbide and hardened steel with good productivity and surface. Optimal selection of process parameters is highly critical for successful material removal and achieving high surface quality. It is crucial for Laser Milling to enhance the productivity of the process in terms of maximization of the material removal rate (MRR), calculated as the ratio between the volume of removed material and the process time, saving at the same time a good surface quality, and to correlate this index to the ablation depth and to surface roughness. In contrast, laser ablation suffers from the usual incompatibility of high ablation depths and good surface quality. The objective of this paper was to demonstrate that the careful laser choice and process optimization can result in a satisfactory compromise for both. This goal was achieved with a simultaneous statistical analysis of ablation depth, material removal rate and surface roughness. Moreover, a multi-objective statistical optimization was performed for improving machining productivity and surface quality. The dependence of the ablation depth, MRR and surface roughness on the laser fluence was also analyzed. All experimental tests were conducted on the 5754 aluminum alloy using a nanosecond Nd:YAG laser with a wavelength of 1064 nm
Study on properties and microstructure of laser beam butt welded joints of Al-Si coated USIBOR® 1500 steel
No full textThe aim of the work is to analyze the laser welding quality of USIBOR®1500-AS, without the coating removing. Through metallographic analysis, the martensitic phase was found both in the Fused Zone and in the Heat Affected Zone. Within the Fused Zone was, also, found a certain amount of Al-Si coating, which essentially depended on the welding speed. That coating modified the molten pool solidification mechanism, but did not affect the welds characteristics, in terms of defects and hardness. The welding speed did not affect the hardness values, while strongly influenced the Fused Zone and Heat Affected Zone widths
Proprietà e performance di materiali metallici ottenuti mediante Selective Laser Melting
No full text
Inline Image Vision Technique for Tires Industry 4.0: Quality and Defect Monitoring in Tires Assembly
No full textThis paper analyzes an image vision methodology suitable for the quality monitoring of the tires assembly process. The proposed architecture integrates a camera system with a laser having the function to control the exact tires position on the platform. The image vision technique provides an automatic tires classification and important information about possible defects by analyzing the three-dimensional (3D) tire features. The proposed model is suitable for Industry 4.0 implementing internet of things -IoT- devices controlling inline the production processes. The proposed results have been developed within the framework of an research industry project
- …
