Chrome Layer Thickness Modelling in a Hard Chromium Plating Process Using a Hybrid PSO/ RBF–SVM–Based Model

The purpose of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. The principal feature of chromium plating is its endurance in the face of the wear and corrosion. This industrial process has a vast range of applications in many different areas. In the performance of this process, some difficulties can be found. Some of the most common are melt deposition, milky white chromium deposition, rough or sandy chromium deposition and lack of toughness of the layer or wear and lack of thickness of the layer deposited. This study builds a novel nonparametric method relied on the statistical machine learning that employs a hybrid support vector machines (SVMs) model for the hard chromium layer thickness forecast. The SVM hyperparameters optimization was made with the help of the Particle Swarm Optimizer (PSO). The outcomes indicate that PSO/SVM–based model together with radial basis function (RBF) kernel has permitted to foretell the thickness of the chromium layer created in this industrial process satisfactorily. Thus, two kinds of outcomes have been obtained: firstly, this model permits to determine the ranking of relevance of the seven independent input variables investigated in this industrial process. Finally, the high achievement and lack of complexity of the model indicate that the PSO/SVM method is very interesting compared to other conventional foretelling techniques, since a coefficient of determination of 0.9952 is acquired

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Parametric Optimization of Taper Cutting Process using Wire Electrical Discharge Machining (WEDM)

Significant technological advancement of wire electrical discharge machining (WEDM) process has been observed in recent times in order to meet the requirements of various manufacturing fields especially in the production of parts with complex geometry in precision die industry. Taper cutting is an important application of WEDM process aiming at generating complex parts with tapered profiles. Wire deformation and breakage are more pronounced in taper cutting as compared with straight cutting resulting in adverse effect on desired taper angle and surface integrity. The reasons for associated problems may be attributed to certain stiffness of the wire. However, controlling the process parameters can somewhat reduce these problems. Extensive literature review reveals that effect of process parameters on various performance measures in taper cutting using WEDM is also not adequately addressed. Hence, study on effect of process parameters on performance measures using various advanced metals and metal matrix composites (MMC) has become the predominant research area in this field. In this context, the present work attempts to experimentally investigate the machining performance of various alloys, super alloys and metal matrix composite during taper cutting using WEDM process. The effect of process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension on various performance measures such as angular error, surface roughness, cutting rate and white layer thickness are studied using Taguchi’s analysis. The functional relationship between the input parameters and performance measures has been developed by using non-linear regression analysis. Simultaneous optimization of the performance measures has been carried out using latest nature inspired algorithms such as multi-objective particle swarm optimization (MOPSO) and bat algorithm. Although MOPSO develops a set of non-dominated solutions, the best ranked solution is identified from a large number of solutions through application of maximum deviation method rather than resorting to human judgement. Deep cryogenic treatment of both wire and work material has been carried out to enhance the machining efficiency of the low conductive work material like Inconel 718. Finally, artificial intelligent models are proposed to predict the various performance measures prior to machining. The study offers useful insight into controlling the parameters to improve the machining efficiency

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy

Book of abstracts of the 14th International Symposium of Croatian Metallurgical Society - SHMD \u272020, Materials and metallurgy held in Šibenik, Croatia, June 21-26, 2020. Abstracts are organized in four sections: Materials - section A; Process metallurgy - Section B; Plastic processing - Section C and Metallurgy and related topics - Section D

The 1981 NASA/ASEE Summer Faculty Fellowship Program: Research reports

Research reports related to spacecraft industry technological advances, requirements, and applications were considered. Some of the topic areas addressed were: (1) Fabrication, evaluation, and use of high performance composites and ceramics, (2) antenna designs, (3) electronics and microcomputer applications and mathematical modeling and programming techniques, (4) design, fabrication, and failure detection methods for structural materials, components, and total systems, and (5) chemical studies of bindary organic mixtures and polymer synthesis. Space environment parameters were also discussed

Computational Design of Compositionally Graded Alloys

In this work, a new computational methodology is presented for the design of compositionally graded alloys. Compositionally graded alloys are a class of functionally graded materials, or materials which exhibit spatially varying properties. While the introduction of additive manufacturing has accelerated interest in these materials, there are many challenges that impede their development like the formation of deleterious phases and material compositions that are incompatible with manufacturing processes. Previous design methods have attempted to design gradients that avoid these issues, but such methods have been limited to the analysis and interpretation of two-dimensional diagrams and are therefore hindered by the limits of human visualization and ideation. The proposed methodology is made possible by the novel formulation of gradient design as a path planning problem. This formulation allows the use of path planning algorithms to optimize gradient paths in composition space. Such algorithms can optimize gradients with any number of constituent elements to meet specified design requirements or objectives. To make the gradient design problem tractable for such algorithms, surrogate modeling techniques are employed to represent design constraints and objectives. Constraints, like deleterious phase formation, can be predicted by CALPHAD software and then modeled by a machine learning classifier. Similarly, regression models can be trained to evaluate cost functions in an efficient manner. Several unique problem formulations are demonstrated to showcase the advantages of the methodology in gradient design. Among these are constraints to avoid deleterious phase regions and other regions of the state space with poor predicted manufacturability. Common cost functions in the path planning community, like path length and obstacle clearance, are shown to be useful for some problems, while including constraint violation as penalty term is demonstrated to satisfy constraints that might otherwise be unachievable. Lastly, a novel cost function is proposed to design gradients with monotonic properties, which can achieve nearly any bounded property distribution on a gradient part. All proposed problem formulations are demonstrated in the design of authentic compositionally graded alloys and experiments are used to validate predicted results

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems

Friction Force Microscopy of Deep Drawing Made Surfaces

Aim of this paper is to contribute to micro-tribology understanding and friction in micro-scale interpretation in case of metal beverage production, particularly the deep drawing process of cans. In order to bridging the gap between engineering and trial-and-error principles, an experimental AFM-based micro-tribological approach is adopted. For that purpose, the can’s surfaces are imaged with atomic force microscopy (AFM) and the frictional force signal is measured with frictional force microscopy (FFM). In both techniques, the sample surface is scanned with a stylus attached to a cantilever. Vertical motion of the cantilever is recorded in AFM and horizontal motion is recorded in FFM. The presented work evaluates friction over a micro-scale on various samples gathered from cylindrical, bottom and round parts of cans, made of same the material but with different deep drawing process parameters. The main idea is to link the experimental observation with the manufacturing process. Results presented here can advance the knowledge in order to comprehend the tribological phenomena at the contact scales, too small for conventional tribology

Towards a Conceptual Design of an Intelligent Material Transport Based on Machine Learning and Axiomatic Design Theory

Reliable and efficient material transport is one of the basic requirements that affect productivity in sheet metal industry. This paper presents a methodology for conceptual design of intelligent material transport using mobile robot, based on axiomatic design theory, graph theory and artificial intelligence. Developed control algorithm was implemented and tested on the mobile robot system Khepera II within the laboratory model of manufacturing environment. Matlab© software package was used for manufacturing process simulation, implementation of search algorithms and neural network training. Experimental results clearly show that intelligent mobile robot can learn and predict optimal material transport flows thanks to the use of artificial neural networks. Achieved positioning error of mobile robot indicates that conceptual design approach can be used for material transport and handling tasks in intelligent manufacturing systems