Prediction of formation force during single-point incremental sheet metal forming using artificial intelligence techniques

Integrated Intelligent Method Based on Fuzzy Logic for Optimizing Laser Microfabrication Processing of GnPs-Improved Alumina Nanocomposites

Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al₂O₃ nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al₂O₃ nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.

Multi response optimization of machining parameters for an annealed Monel K 500 alloy in drilling using Machine learning techniques and ANN

Manufacturing companies are focusing on continuous process development to thrive in today’s quality-conscious market. It is particularly relevant to investigate machining processes for advanced materials such as superalloys. Drilling is a major operation that is used in the majority of manufacturing processes. Hence, this research work is focused on investigating the drilling performance of the Monel K500. The output responses under consideration are metal removal rate (MRR), surface roughness, and tool wear. Various contemporary techniques were utilized in this work, namely machine learning methods, artificial neural networks, principal component analysis, and grey relation analysis using uncoated, coated, and HSS (high-speed steel) drills. After annealing, the softened material can be easily machined to increase the MRR and decrease tool wear and surface roughness. The experimental results show that, after annealing, the surface roughness values for HSS drills have been reduced by 23.86%, uncoated drills by 27.29%, and coated drills by 29.27%, respectively. Moreover, tool wear values for HSS drills decreased by 28.51%, uncoated drills by 34.7%, and coated drills by 33.71%, based on the relative error approach. MRR values for HSS drills increased by 20.51 %, uncoated drills by 23.08%, and coated drills by 23.5%, respectively. For PCA (principal component analysis), feed (47%), and for GRA (gray relation analysis), feed (40.1%) will be the significant parameter followed by speed, and both methods have identified the same experimental run values for optimization of cutting parameters. The theoretical values were predicted using machine learning methods, which utilized the Python language using the Google Colab and then validated with experimental values. The predicted values obtained by the decision tree are close to the measured values as compared to support vector regression and K-nearest neighbor based on relative error. The estimated values obtained by the ANN (artificial neural networks) approach, using Easy NN plus software, match well with the actual values, with a slight deviation.

Comparative Analysis of Machine Learning Methods for Predicting Robotized Incremental Metal Sheet Forming Force

This paper proposes a method for extracting information from the parameters of a single point incremental forming (SPIF) process. The measurement of the forming force using this technology helps to avoid failures, identify optimal processes, and to implement routine control. Since forming forces are also dependent on the friction between the tool and the sheet metal, an innovative solution has been proposed to actively control the friction forces by modulating the vibrations that replace the environmentally unfriendly lubrication of contact surfaces. This study focuses on the influence of mechanical properties, process parameters and sheet thickness on the maximum forming force. Artificial Neural Network (ANN) and different machine learning (ML) algorithms have been applied to develop an efficient force prediction model. The predicted forces agreed reasonably well with the experimental results. Assuming that the variability of each input function is characterized by a normal distribution, sampling data were generated. The applicability of the models in an industrial environment is due to their relatively high performance and the ability to balance model bias and variance. The results indicate that ANN and Gaussian process regression (GPR) have been identified as the most efficient methods for developing forming force prediction models.

Complexity-Based Analysis of the Effect of Forming Parameters on the Surface Finish of Workpiece in Single Point Incremental Forming (SPIF)

Nowadays, the manufacturing industry is focused on newer modern manufacturing methods, such as single point incremental forming (SPIF). The popularity of the SPIF process in the manufacturing industry is increasing due to its capability for rapid prototyping, forming complex geometry with simple steps, and customizing products for customers. This study investigates the effect of forming parameters (feed rate and step size) on the surface structure of the aluminum AA6061 sheet. We employ fractal theory to investigate the complexity of deformed surfaces. Accordingly, we study the relationship between the complexity and roughness of the deformed surface. The results show that the complexity and roughness of the deformed surface vary due to the changes in forming parameters. Fractal analysis can be further employed in other manufacturing processes to investigate the relation between the complexity and roughness of processed surfaces.

Emerging Trends in Single Point Incremental Sheet Forming of Lightweight Metals

Lightweight materials, such as titanium alloys, magnesium alloys, and aluminium alloys, are characterised by unusual combinations of high strength, corrosion resistance, and low weight. However, some of the grades of these alloys exhibit poor formability at room temperature, which limits their application in sheet metal-forming processes. Lightweight materials are used extensively in the automobile and aerospace industries, leading to increasing demands for advanced forming technologies. This article presents a brief overview of state-of-the-art methods of incremental sheet forming (ISF) for lightweight materials with a special emphasis on the research published in 2015–2021. First, a review of the incremental forming method is provided. Next, the effect of the process conditions (i.e., forming tool, forming path, forming parameters) on the surface finish of drawpieces, geometric accuracy, and process formability of the sheet metals in conventional ISF and thermally-assisted ISF variants are considered. Special attention is given to a review of the effects of contact conditions between the tool and sheet metal on material deformation. The previous publications related to emerging incremental forming technologies, i.e., laser-assisted ISF, water jet ISF, electrically-assisted ISF and ultrasonic-assisted ISF, are also reviewed. The paper seeks to guide and inspire researchers by identifying the current development trends of the valuable contributions made in the field of SPIF of lightweight metallic materials.

Surface Finish Analysis in Single Point Incremental Sheet Forming of Rib-Stiffened 2024-T3 and 7075-T6 Alclad Aluminium Alloy Panels

The article presents the results of the analysis of the interactions between the single point incremental forming (SPIF) process parameters and the main roughness parameters of stiffened ribs fabricated in Alclad aluminium alloy panels. EN AW-7075-T6 and EN AW-2024-T3 Alclad aluminium alloy sheets were used as the research material. Panels with longitudinal ribs were produced with different values of incremental vertical step size and tool rotational speed. Alclad is formed of high-purity aluminium surface layers metallurgically bonded to aluminium alloy core material. The quality of the surface roughness and unbroken Alclad are key problems in SPIF of Alclad sheets destined for aerospace applications. The interactions between the SPIF process parameters and the main roughness parameters of the stiffened ribs were determined. The influence of forming parameters on average roughness Sa and the 10-point peak–valley surface roughness Sz was determined using artificial neural networks. The greater the value of the incremental vertical step size, the more prominent the ridges found in the inner surface of stiffened ribs, especially in the case of both Alclad aluminium alloy sheets. The predictive models of ANNs for the Sa and the Sz were characterised by performance measures with R² values lying between 0.657 and 0.979. A different character of change in surface roughness was found for sheets covered with and not covered with a soft layer of technically pure aluminium. In the case of Alclad sheets, increasing the value of the incremental vertical step size increases the value of the surface roughness parameters Sa and Sz. In the case of the sheets not covered by Alclad, reduction of the tool rotational speed increases the Sz parameter and decreases the Sa parameter. An obvious increase in the Sz parameter was observed with an increase in the incremental vertical step size.

Al-Samhan A, Ghaleb M, Dabwan A. Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach

The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.