In recent days, many interacted shape products have been developed by manufacturing industries for different applications in various fields such as defense, aerospace, and space centers. In manufacturing, 30% of time consumption is due to assembly operation compared with the remaining processes in manufacturing. It is very difficult to get optimal sequence because assembly sequence planning is a multimodel optimization problem. As the number of parts in the assembly increases, the possible number of sequences increases exponentially therefore obtaining the optimal assembly sequence becomes more difficult and time consuming. There exist many mathematical algorithms to obtain optimal assembly sequences. But, recent studies state that they perform poorly when it comes to multiobjective optimal assembly sequence. In recent years, researchers have developed several soft computing-based algorithms for solving assembly sequence problems. In this paper, assembly subset detection method has been introduced. The proposed method is applied for the first time to solve assembly sequence problems. This method eliminates those assembly sets that have more directional changes and require more energy. The method is compared with other algorithms, namely, genetic algorithm (GA), enhanced GA, ant colony optimization (ACO), memetic algorithm, imperialistic harmonic search algorithm, and flower pollination algorithm (FPA), and is found to be successful in achieving the optimal assembly sequence for an industrial product with smaller number of iterations. Note to Practitioners —This paper is motivated by the redesign of helicopter cowling of a Canadian aircraft company using concepts of design for assembly. Though we could reduce the number of parts using advanced composite materials and manufacturing processes, obtaining a feasible assembly for the new assembly structure required a lot of computation time. Hence, the researchers studied the existing literature on assembly sequence generation methods and their limitations, and came up with efficient automated optimal sequence generation method.

An Optimal Robotic Assembly Sequence Planning by Assembly Subsets Detection Method Using Teaching Learning-Based Optimization Algorithm

Assembly sequence planning is one of the multi-model optimization problems, in which more than one objective function has to be optimized at a time to obtain the quality assembly sequence. Moreover...

Optimal robotic assembly sequence planning using stability graph through stable assembly subset identification

Optimal assembly sequence (OAS) is always an interesting aspect for an industrial engineer to minimize assembly time and cost, which gives number of assembly levels and sequence of assembly operations. An assembly sequence with more number of parallel possible stable sub-assemblies significantly reduces the overall assembly time for large scale products. Finding such optimal sequences from huge set of all assembly sequences (SAAS) is challenging due to involvement of multiple assembly feasibility validation criteria namely; assembly coherence, geometric feasibility, part stability and mechanical feasibility. In this paper, an efficient computational method is proposed to generate optimal assembly sequences. The method considers extended assembly stability relations to identify stable sub-assemblies for parallel execution. The method is proven in generating optimal solutions for any given product effectively. The method is well discussed and compared with prominent computational methods with suitable product illustrations.

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Optimal Assembly Sequence generation through computational approach

https://research-information.bris.ac.uk/ws/files/202418125/Vikas_paper_Final_version_David_1_.pdf

Suicide by hanging is a priority for suicide prevention: method specific suicide in India (2001-2014).

Cyber-physical assembly system-based optimization for robotic assembly sequence planning

Article history: Received October 2 2016 Received in Revised Format October 28 2016 Accepted December 2 2016 Available online December 2 2016 An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS) in combination with the Genetic Algorithm (GA) to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1) Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2) Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques. © 2017 Growing Science Ltd. All rights reserved

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Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences

The modern additive manufacturing process enables the fabrication of innovative structural designs of lattice structures with enhanced mechanical properties. Characterizing the designability of lattice geometries and the corresponding mechanical performance, particularly the compressive strength is vital to commercializing lattice structures for lightweight components. In this paper, a Novel hybrid type of lattice structure was developed inspired by the overlapping pattern of scales on dermal layers of the species like fish and circular patterns observed from the bamboo tree structure. This research designed the lattice cell with 20%,30%,40%, and 50% overlapping areas based on the proposed circular area. The unit cell wall thickness varies between 0.4 mm and 0.6 mm. The designed structures with a constant volume of 40x40x40 mm, varied circular diameters, overlapping areas, and unit wall thickness are modeled in Fusion 360 software. 3D-printed lattice structures were fabricated using the vat polymerization type three-dimensional printing machine using the principle of stereolithography (SLA) technique. Then, the 3D-printed specimens are tested for quasi-static compressive response in a universal testing machine (UTM) by following ASTM standards. The test results are evaluated and compared with the simulation results. Finally, the best lattice structure is selected for energy absorption application in the aerospace and defense sectors. 

An experimental and numerical investigation on the performance of novel hybrid bio-inspired 3D printed lattice structures for stiffness and energy absorption applications

In recent years due to heavy stress and mental depression, in many parts of the world people commit suicides and lose their lives, leaving their family and relatives. From all around the world, approximately 17% of the suicide cases that are registered from the year 1987 to 2017 are from India. Among those, hanging to death from a ceiling fan is one of the major methods of suicide. In this paper, a new safety design is proposed for ceiling fans to prevent suicidal cases. Initially, stress analysis has been done on the general existing model fans to evaluate the force exerted by its own weight of the fan on the down rod. Depending on the results, the modification to the ceiling fan will improve the design from any mishap. In this new design, the down road is replaced with a spring that connects it to the ceiling of the house. The spring is designed such that, the rigidity of the spring fails when it encounters more weight than the designed safe load. Due to rigidity failure of the spring, the spring elongates and the fan lowers down, thus preventing the person from death by hanging. The Computer-Aided Design (CAD) model of the ceiling fan is made in SolidWorks and the analysis of down rod is done in ANSYS. However, the analysis of spring assembly is done in SolidWorks.

A New Safety Design of the Ceiling Fan to Avoid Suicidal Cases

Welding has been done by taking mild steel as work piece material in shielded metal arc welding (SMAW) process. Material thickness and current were considered as input parameters. Joining of metals has been completed in two ways. In first process double pass welding has been done in which 1st pass was in reverse polarity and the second pass was in straight polarity and in the 2nd process both the passes of welding were completed with straight polarity. The comparison of microstructure and impact toughness has been investigated. It was found that more amount of heat was generated in the 2nd process as compared to the 1st process. Therefore, growths of grains in heat affected zone occurred and maximum growth occurred in 2nd than 1st process. Impact strength increased with decrease in current value and increase in material thickness respectively. The impact strength values for 1st process welding were relatively less as compared to 2nd process.

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Variation of Microstructural and Mechanical Properties With Respect to Polarity in Shielded Metal Arc Welding of Mild Steel

This study presents the underactuated design of a tendon-driven bioinspired robotic gripper for the stable grasping. In this study, kinematic modeling of the bioinspired underactuated robotic gripper has been proposed and optimal design variables have been found out using multi-objective evolutionary algorithms. The obtained anthropometric data of the human hand is taken as the dimension range of the input design variables in the design optimization problem. In this study, the kinematic model of the proposed gripper is obtained, and the structural multi-objective optimization problem is formulated in the static condition of the gripper. Three fitness functions have developed from the contact forces, and some geometric constraints are considered for solving the proposed multi-objective optimization problem using multi-objective grey wolf optimization algorithm.

Balamurali Gunji

Papers

Hybridized genetic-immune based strategy to obtain optimal feasible assembly sequences

An experimental and numerical investigation on the performance of novel hybrid bio-inspired 3D printed lattice structures for stiffness and energy absorption applications

A New Safety Design of the Ceiling Fan to Avoid Suicidal Cases

Variation of Microstructural and Mechanical Properties With Respect to Polarity in Shielded Metal Arc Welding of Mild Steel

Multi-Objective Design Optimization of a Bioinspired Underactuated Robotic Gripper Using Multi-Objective Grey Wolf Optimizer