Bibhuti Bhusan Biswal

Bio: Bibhuti Bhusan Biswal is an academic researcher from National Institute of Technology, Rourkela. The author has contributed to research in topics: Robot & Welding. The author has an hindex of 20, co-authored 155 publications receiving 1413 citations. Previous affiliations of Bibhuti Bhusan Biswal include Techno India & National Institute of Technology, Meghalaya.

Advances in weld seam tracking techniques for robotic welding: A review

Abstract: Use of sensors in robotic welding for controlling the weld quality leads to replacement of manual welding operation in dangerous work environment in presence of high temperature and fumes even in small or medium scale enterprises. The seam tracking operation is very essential for extracting weld seam position which can be fed to robot controller for instructing robot along the weld seam path. The seam tracking operation can be executed by different types of sensors having their own merits and demerits. In this paper, different sensors and techniques used for seam tracking task in robotic welding have been discussed in detail. Each sensor has different method or technology of weld seam feature extraction which have been described by different authors in different ways. The chief tasks for seam tracking have been found to be weld starting and end point detection, weld edge detection, joint width measurement and weld path position determination with respect to robot co-ordinate frame. Thus sensors have a very important role in robotic welding for fully automating the system with in process real time monitoring of weld process parameters with the sensor feedback. In further discussion the practical use of different sensors in industries with a comparison of their advantages and disadvantages have been discussed. This Paper presents the role of sensors in robotic welding and a detail study of methodologies of weld seam position and geometry feature extraction by different sensors typically used for weld seam tracking.

A review on assembly sequence generation and its automation

Abstract: Sequence of feasible mechanical assembly operations plays significant role in overall cost optimisation process for manufacturing industry and thus great importance is given to assembly sequence generation from past four decades. Though achieving at least one feasible sequence is focused in the earlier stages of research, the introduction of soft computing techniques attracted the industrial engineers towards cost-effective, optimised assembly sequences to attain economical manufacturing process. The integration of assembly sequence generation methods with computer aided design environment ensures more correctness and flexibility to automate the process. In this paper, a detailed review on various methods, their applications and limitations is presented and well discussed.

A general regression neural network approach for the evaluation of compressive strength of FDM prototypes

Abstract: Soft computing (SC) methods are well known for their remarkable ability of learning from experimental data set to describe nonlinear and interaction effect with great success. Due to complex mechanism and uncertainty of the fused deposition modelling (FDM) process, of late, SC methods are preferred compare to theoretical model (physics-based) for measuring output responses of the FDM process. In the present study, performance modelling of FDM prototype has been carried out using two potential SC methods such as multi-gene genetic programming (MGGP) and general regression neural network (GRNN). The effect of three input factors namely, layer thickness, orientation, raster angle on output compressive strength of the prototype was studied using the two SC models. Data generated from the experimental study are fed into the cluster of MGGP and GRNN for the formulation of mathematical models. Based on the experimental datasets, the proposed SC models predict compressive strength of FDM fabricated prototype in terms of input process parameters. The predictions of compressive strength by these models are evaluated against the data generated in experimental study. Results conclude that the model generated by GRNN has better goodness of fit compare to the MGGP model and thus can be a promising alternative for optimizing the FDM process.

Determination of optimum parameters with multi-performance characteristics in laser drilling—A grey relational analysis approach

Abstract: Laser drilling is increasingly becoming the method of choice for precision drilling for variety of components. However, a number of defects such as spatter, recast, heat-affected zone (HAZ), and taper limit the application. Elimination of these defects is the subject of intense research. This paper presents a grey relational optimization approach for the determination of the optimum process parameters which minimize the HAZ and hole circularity and maximize material removal rate in a Pulsed Nd:YAG laser micro-drilling in high carbon steel within existing resources. The input process parameters considered are pulse width, number of pulses, assist gas (oxygen) flow rate, and its supply pressure. A higher resolution-based L25 orthogonal array has been used for conducting the experiments. The designed experimental results are used in grey relational analysis and the weights of the quality characteristics are determined optimizing the parameters. On the basis of optimization results, it has been found that the optimal parameter level gives a small HAZ, fine hole, and maximum material removal rate. Subsequently, the results are also verified and found appropriate by running confirmation tests.

3D printing trends in building and construction industry: a review

Abstract: Three-dimensional (3D) printing (also known as additive manufacturing) is an advanced manufacturing process that can produce complex shape geometries automatically from a 3D computer-aided design m...

A Comprehensive Review of Swarm Optimization Algorithms

Abstract: Many swarm optimization algorithms have been introduced since the early 60’s, Evolutionary Programming to the most recent, Grey Wolf Optimization. All of these algorithms have demonstrated their potential to solve many optimization problems. This paper provides an in-depth survey of well-known optimization algorithms. Selected algorithms are briefly explained and compared with each other comprehensively through experiments conducted using thirty well-known benchmark functions. Their advantages and disadvantages are also discussed. A number of statistical tests are then carried out to determine the significant performances. The results indicate the overall advantage of Differential Evolution (DE) and is closely followed by Particle Swarm Optimization (PSO), compared with other considered approaches.

Metal Foams A Design Guide

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A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics

Abstract: Fused deposition modeling (FDM) is an additive manufacturing (AM) process that is often used to fabricate geometrically complex shaped prototypes and parts. It is gaining popularity as it reduces cycle time for product development without the need for expensive tools. However, the commercialization of FDM technology in various industrial applications is currently limited due to several shortcomings, such as insufficient mechanical properties, poor surface quality, and low dimensional accuracy. The qualities of FDM-produced products are affected by various process parameters, for example, layer thickness, build orientation, raster width, or print speed. The setting of process parameters and their range depends on the section of FDM machines. Filament materials, nozzle dimensions, and the type of machine determine the range of various parameters. The optimum setting of parameters is deemed to improve the qualities of three-dimensional (3D) printed parts and may reduce post-production work. This paper intensively reviews state-of-the-art literature on the influence of parameters on part qualities and the existing work on process parameter optimization. Additionally, the shortcomings of existing works are identified, challenges and opportunities to work in this field are evaluated, and directions for future research in this field are suggested.