7 research outputs found
Applications of Artificial Intelligence in Additive Manufacturing
After the recent launch of home-based personal 3D printers as well as government funding and company investments in advancing manufacturing initiatives, additive manufacturing has rapidly come to the forefront of discussion and become a more approachable lucrative career of particular interest to the younger generation. It is essential to identify the long-term competitive advantages and how to teach, inspire, and create a resolute community of supporters, learners, and new leaders in this important industry progression.Applications of Artificial Intelligence in Additive Manufacturing provides instruction on how to use artificial intelligence to produce additively manufactured parts. It discusses an overview of the field, the strategic blending of artificial intelligence and additive manufacturing, and features case studies on the various emerging technologies. Covering topics such as artificial intelligence models, experimental investigations, and online detections, this book is an essential resource for enΠΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ Adobe AcrobatΠΠΎΡΠ»Π΅ Π½Π΅Π΄Π°Π²Π½Π΅Π³ΠΎ Π·Π°ΠΏΡΡΠΊΠ° Π΄ΠΎΠΌΠ°ΡΠ½ΠΈΡ
ΠΏΠ΅ΡΡΠΎΠ½Π°Π»ΡΠ½ΡΡ
3D-ΠΏΡΠΈΠ½ΡΠ΅ΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠ½Π°Π½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΠΉ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΡ
ΠΈΠ½ΠΈΡΠΈΠ°ΡΠΈΠ² Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ΅ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²ΠΎ Π±ΡΡΡΡΠΎ Π²ΡΡΠ»ΠΎ Π½Π° ΠΏΠ΅ΡΠ΅Π΄Π½ΠΈΠΉ ΠΏΠ»Π°Π½ Π΄ΠΈΡΠΊΡΡΡΠΈΠΉ ΠΈ ΡΡΠ°Π»ΠΎ Π±ΠΎΠ»Π΅Π΅ Π΄ΠΎΡΡΡΠΏΠ½ΠΎΠΉ ΠΏΡΠΈΠ±ΡΠ»ΡΠ½ΠΎΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠΈΠ΅ΠΉ, ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΡΡΠ΅ΠΉ ΠΎΡΠΎΠ±ΡΠΉ ΠΈΠ½ΡΠ΅ΡΠ΅Ρ Π΄Π»Ρ ΠΌΠΎΠ»ΠΎΠ΄ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ. ΠΠ°ΠΆΠ½ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΠΈΡΡ Π΄ΠΎΠ»Π³ΠΎΡΡΠΎΡΠ½ΡΠ΅ ΠΊΠΎΠ½ΠΊΡΡΠ΅Π½ΡΠ½ΡΠ΅ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π° ΠΈ ΡΠΎ, ΠΊΠ°ΠΊ ΠΎΠ±ΡΡΠ°ΡΡ, Π²Π΄ΠΎΡ
Π½ΠΎΠ²Π»ΡΡΡ ΠΈ ΡΠΎΠ·Π΄Π°Π²Π°ΡΡ ΡΠ΅ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΠΎΠΎΠ±ΡΠ΅ΡΡΠ²ΠΎ ΡΡΠΎΡΠΎΠ½Π½ΠΈΠΊΠΎΠ², ΡΡΠ°ΡΠΈΡ
ΡΡ ΠΈ Π½ΠΎΠ²ΡΡ
Π»ΠΈΠ΄Π΅ΡΠΎΠ² Π² ΡΡΠΎΠΌ Π²Π°ΠΆΠ½ΠΎΠΌ ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΠΎΡΡΠ°ΡΠ»ΠΈ. ΠΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° Π² Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠΌ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π΅ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΈΠ½ΡΡΡΡΠΊΡΠΈΡ ΠΎ ΡΠΎΠΌ, ΠΊΠ°ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡ Π΄Π»Ρ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° Π΄Π΅ΡΠ°Π»Π΅ΠΉ Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π°. Π ΠΊΠ½ΠΈΠ³Π΅ ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΡΡΡ ΠΎΠ±Π·ΠΎΡ ΠΎΠ±Π»Π°ΡΡΠΈ, ΡΡΡΠ°ΡΠ΅Π³ΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠΎΡΠ΅ΡΠ°Π½ΠΈΠ΅ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° ΠΈ Π°Π΄Π΄ΠΈΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π°, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΡΡ ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
Π½ΠΎΠ²ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. ΠΡΠ° ΠΊΠ½ΠΈΠ³Π°, ΠΎΡ
Π²Π°ΡΡΠ²Π°ΡΡΠ°Ρ ΡΠ°ΠΊΠΈΠ΅ ΡΠ΅ΠΌΡ, ΠΊΠ°ΠΊ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°, ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΠΎΠ½Π»Π°ΠΉΠ½-ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΡ, ΡΠ²Π»ΡΠ΅ΡΡΡ Π²
Sliding wear behaviour of salt bath nitrided 316LN austenitic stainless steel
The wear behaviour of AISI 316LN austenitic stainless steel has been studied by varying the duration of nitriding. AISI 316LN steel was nitrided using salt bath nitriding which consist of a mixture of 70:30 ratio of alkaline cynates and carbonates. Nitriding was carried out for two time durations namely 60Β min and 100Β min. Pin-on-Disc wear test were carried out for both the untreated and treated sample at room temperature. The wear parameters were changed to understand the wear mechanism of AISI 316LN steel along with the nitrided sample by varying the sliding distance for 250Β m, 500Β m and 1000Β m. Due to the presence of different wear mechanism the rate of wear varies as a function of sliding distance. For untreated sample the wear mechanism was dominant with adhesion, abrasion and plastic deformation. An optical micrograph, X-ray diffraction analysis, wear morphology, hardness measurement and surface roughness were carried out. Due to the presence of compound layer as a result of nitriding AISI 316LN austenitic stainless steel, the mechanism of wear was restricted to abrasive wear. Untreated specimens have more significant wear loss when compared to treated specimen as work hardening and an increase in hardness of wear track resulted in material pull out. Stability of the compound layer is achieved as a result of longer duration of nitriding. Abrasive wear is resisted to a greater extend due the availability of compound layer. AISI 316LN steel which was nitrided for 100Β min exhibited a more stable compound layer when compared to the same nitrided for 60Β min
Wear Characterization of Laser Cladded Ti-Nb-Ta Alloy for Biomedical Applications
Additive manufacturing (AM) has started to unfold diverse fields of applications by providing unique solutions to manufacturing. Laser cladding is one of the prominent AM technologies that can be used to fulfill the needs of custom implants. In this study, the wear resistance of the laser cladded titanium alloy, Ti-17Nb-6Ta, has been evaluated under varied loads in Ringerβs solution. Microstructural evaluation of the alloy was performed by SEM and EDX, followed by phase analysis through XRD. The wear testing and analysis have been carried out with a tribometer under varied loads of 10, 15, and 20 N while keeping other parameters constant. Abrasion was observed to be the predominant mechanism majorly responsible for the wearing of the alloy at the interface. The average wear rate and coefficient of friction values were 0.016 mm3/Nm and 0.22, respectively. The observed values indicated that the developed alloy exhibited excellent wear resistance, which is deemed an essential property for developing biomedical materials for human body implants such as artificial hip and knee joints
Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge
Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance
Meta-Heuristic Technique-Based Parametric Optimization for Electrochemical Machining of Monel 400 Alloys to Investigate the Material Removal Rate and the Sludge
Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance
Tribological Performance and Rheological Properties of Engine Oil with Graphene Nano-Additives
Nanoparticles dispersed in lubricants are being studied for their ability to reduce friction and wear. This paper examines SAE 5W-30 oil enhanced with dispersed graphene nanoplates for tribological and rheological properties. Graphene nanoplate (GNs) concentration effects on the rheological and tribological properties of 5W-30 base oil (0.03, 0.06, 0.09, 0.12, and 0.15 wt percent) were tested. Under various loads, a four-ball testing model was used to conduct a tribological analysis (200, 400, 600, and 800 N). Kinematic viscosity is calculated, and base oil and nanofluid-added 5W30 lubricant are compared for thermal conductivity and flashpoint. Wear scar and coefficient of friction improved by 15% and 33% with nano-additives. When related to the base oil, the flashpoint, thermal conductivity, kinematic viscosity, and pour point all increased, by 25.4%, 77.4%, 29.9%, and 35.4%, respectively. The addition of GNs improved the properties of 5W30 engine oil
Optimization of Process Parameters for Turning Hastelloy X under Different Machining Environments Using Evolutionary Algorithms: A Comparative Study
In this research work, the machinability of turning Hastelloy X with a PVD Ti-Al-N coated insert tool in dry, wet, and cryogenic machining environments is investigated. The machinability indices namely cutting force (CF), surface roughness (SR), and cutting temperature (CT) are studied for the different set of input process parameters such as cutting speed, feed rate, and machining environment, through the experiments conducted as per L27 orthogonal array. Minitab 17 is used to create quadratic Multiple Linear Regression Models (MLRM) based on the association between turning parameters and machineability indices. The Moth-Flame Optimization (MFO) algorithm is proposed in this work to identify the optimal set of turning parameters through the MLRM models, in view of minimizing the machinability indices. Three case studies by considering individual machinability indices, a combination of dual indices, and a combination of all three indices, are performed. The suggested MFO algorithmβs effectiveness is evaluated in comparison to the findings of Genetic, Grass-Hooper, Grey-Wolf, and Particle Swarm Optimization algorithms. From the results, it is identified that the MFO algorithm outperformed the others. In addition, a confirmation experiment is conducted to verify the results of the MFO algorithmβs optimal combination of turning parameters