Industrial Robot Selection Research Articles

A Knowledge-Based Industrial Robot Selection System for Manufacturing Industries

Nowadays, robots can be used for different applications and environment. The capabilities and specification of the robot will determine its usage. The large number of the robot in the market has made the selection of the suitable robot becoming difficult. This paper presents the development of knowledge-based system for robot selection using expert system shell. The aim of the work is to select the suitable industry robot for manufacturing industry. The selection criteria are based upon application, payload, maximum reach, number of axes, repeatability and mounting position. The system consists of several modules such as knowledge acquisition module, inference engine module and user interface module. The development system helps the industrial engineers to select the most appropriate robot for an application.

A comparative study of preference dominance-based approaches for selection of industrial robots

A B S T R A C T A R T I C L E I N F O In the modern era of highly mechanized technologies, manufacturing organizations are now extensively using different kinds of industrial robots for performing complicated and perilous tasks with superior levels of accuracy. The major role of robotic technology within manufacturing organizations is to amalgamate design, manufacturing and management planning activities into a flexible system for improving production lines with minimum manufacturing cost involvement. However, the pre-implementation, implementation, and post-implementation phases of robotic technologies are the foremost issues associated with the selection and rationalization of robotic investments, which is based on a thorough review and exploration of various alternative robots and their mutually conflicting performance measures. Evaluating alternative robots in the presence of multiple conflicting attributes often makes the selection task very complex. This paper focuses on the application feasibilities of two preference dominance-based multi-attribute decision-making (MADM) approaches, namely evaluation of mixed data (EVAMIX) and extended preference ranking organization method for enrichment evaluation II (EXPROM2) whilst selecting the best alternative robots within given manufacturing environments. Using these two methods, a list of all the feasible alternatives from the best to the worst suitable robot is obtained by taking into account different robot selection attributes. The ranking performances of these methods are also compared with those of the past researchers, using four performance tests.

Selection of industrial robots using the Polygons area method

Article history: Received January 22 2014 Received in Revised Format June 6 2014 Accepted June 12 2014 Available online June 17 2014 Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decisionmaking criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman’s rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem. © 2014 Growing Science Ltd. All rights reserved

Robot selection based on grey‐MULTIMOORA approach

PurposeThe purpose of this paper is to report an efficient decision‐support system for industrial robot selection. It seeks to analyze potential robot selection attributes with a relatively new MCDM approach which employs grey set theory coupled with MULTIMOORA method.Design/methodology/approachUse of interval‐valued grey numbers (IVGN) adapted from grey theory has been explored to tackle subjective evaluation information collected from an expert group; finally MULTIMOORA (multi‐objective optimization by ratio analysis) method has been applied in order to aggregate individual criterion/attribute scores into an equivalent evaluation index towards evaluating feasible ranking order of candidate alternative robots.FindingsAn empirical study has also been shown here for better understanding of the said selection‐module; effectively applicable to any other decision‐making scenarios.Originality/valueThis method is computationally very simple, easily comprehensible, and robust which can simultaneously consider numerous subjective attributes. Grey MULTIMOORA ranking is expected to provide a good guidance to the managers of an organization to select the feasible robot. It will also provide a good insight to the robot manufacturer so that it can improve its product or introduce a new product to satisfy customer needs.

Robot selection using a fuzzy regression-based decision-making approach

Industrial robots, which enable manufacturing firms to produce high-quality products in a cost-effective manner, are important components of advanced manufacturing technologies. The performance of industrial robots is determined by multiple and conflicting criteria that have to be simultaneously considered in a robust selection study. In this study, a decision model based on fuzzy linear regression is presented for industrial robot selection. Fuzzy linear regression provides an alternative approach to statistical regression for modelling situations where the relationships are vague or the data set cannot satisfy the assumptions of statistical regression. The results obtained by employing fuzzy linear regression are compared with those of earlier studies applying different analytical methods to a previously reported robot selection problem.

Selection of industrial robots using compromise ranking method

Many advanced manufacturers are now extensively using robots to perform repetitious, difficult and hazardous tasks with precision. Selection of industrial robots to suit a particular application and production environment from among a large number of alternatives available in the market is a difficult task in real-time manufacturing environment. This has become more and more complicated due to increase complexity, advanced features and facilities that are continuously being incorporated into the robots by different vendors. The decision maker needs to select the most appropriate industrial robot to achieve the desired performance with minimum cost and specific application ability. This paper mainly focuses on solving the industrial robot selection problems using VIse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method, which has already become a quite popular multi-criteria decision-making (MCDM) tool. It evaluates and ranks the alternative candidate robots, while proposing a compromise solution to the robot selection problem. Two real-time examples are illustrated to demonstrate and validate the effectiveness and applicability of VIKOR method, which also prove the computational simplicity of this method.

A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection

Article history: Received 1 April 2011 Received in revised form May, 08, 2011 Accepted 08 May 2011 Available online 10 May 2011 Industrial robots are mainly employed to perform repetitive and hazardous production jobs, multi-shift operations etc. to reduce the delivery time, improve the work environment, lower the production cost and even increase the product range to fulfill the customers’ needs. When a choice is to be made from among several alternative robots for a given industrial application, it is necessary to compare their performance characteristics in a decisive way. As the industrial robot selection problem involves multiple conflicting criteria and a finite set of candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively used to solve such type of problem. In this paper, ten most popular MCDM methods are considered and their relative performance are compared with respect to the rankings of the alternative robots as engaged in some industrial pick-n-place operation. It is observed that all these methods give almost the same rankings of the alternative robots, although the performance of WPM, TOPSIS and GRA methods are slightly better than the others. It can be concluded that for a given industrial robot selection problem, more attention is to be paid on the proper selection of the relevant criteria and alternatives, not on choosing the most appropriate MCDM method to be employed. © 2011 Growing Science Ltd. All rights reserved

Industrial robot selection using a novel decision making method considering objective and subjective preferences

Robots with vastly different capabilities and specifications are available for a wide range of applications. Selection of a robot from among the large number available in the market to suit a particular application and production environment has become a difficult task. Many methods for robot selection have been developed to date. Keeping in view the research works on industrial robot selection, a subjective and objective integrated multiple attribute decision making method is proposed in this paper for the purpose of robot selection. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Three examples are presented to illustrate the potential of the proposed method.

Selection of industrial robot using interval-valued trapezoidal fuzzy numbers set combined with VIKOR method

Recently industrial robots are being immensely applied in almost every manufacturing or production industries for improvement of quality as well as productivity. Depending on the nature of the job to be performed, appropriate robot selection has become an important as well as challenging task for an automated manufacturing cell. Several criteria attributes are assumed to be responsible towards performance of a particular robot. Hence, a strong multi–attribute decision support model is indeed required to facilitate this evaluation and selection process. To address this issue, present work explores the concept of interval–valued trapezoidal fuzzy numbers set integrated with VIKOR method to help such a decision–making problem.

Selection of industrial robots using compromise ranking and outranking methods

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is ‘VIsekriterijumsko KOmpromisno Rangiranje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.

Selection, identification and comparison of industrial robots using digraph and matrix methods

In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative industrial robots. A robot selection index is proposed that evaluates and ranks robots for a given industrial application. The index is obtained from a robot selection attributes function, obtained from the robot selection attributes digraph. The digraph is developed considering robot selection attributes and their relative importance for the application considered. A step by step procedure for evaluation of robot selection index is suggested. Coefficients of similarity and dissimilarity and the identification sets are also proposed. These are obtained from the robot selection attributes function and are useful for easy storage and retrieval of the data. Two examples are included to illustrate the approach.

Integrating AHP with QFD for robot selection under requirement perspective

Selection of a robot is an important task, as improper selection may adversely affect a firm's production by reducing the quality of the product, thereby reducing productivity as well as profitability. To effectively select a robot for a specified job, several factors have to be considered. The objective of this paper is to explain how, using a combined AHP/QFD model, the authors are able to determine if the deployment of robots in industry helped in performance enhancement from requirement perspective. Incorporating a simple and novel cost factor measure in the proposed integrated AHP/QFD model aids justification of the implementation of a robotic system in a manufacturing firm from an economic point of view also. The proposed integrated approach also identifies technical requirements followed by customer requirements. In this paper, an integrated model combining AHP and QFD has been delineated for the industrial robot selection problem. Seven technical requirement factors have been considered for the case study.

Dimensional analysis for investment selection in industrial robots

Investment evaluation and selection of industrial robots is a complex multicriteria decision-making task. This paper considers the possibility of applying to the robot selection problem a known mathematical method based on Dimensional Analysis (DA) theory. It is shown how it is possible to obtain an easy, efficient and robust decision-making support system which overcomes all attribute dimension problems. In particular, the robustness of the DA approach when compared with judgements made by a group of experts concerning different factors and weights, is evaluated. DA low sensitivity is compared with a weightedpoint decision method found in the literature.

Evaluating and selecting investments in industrial robots

This paper proposes an alternative methodology for the selection of industrial robots using data envelopment analysis (DEA). It aims at the identification, in a cost/benefit perspective, of the optimal robot, by measuring, for each robot, the relative efficiency through the resolution of linear programming problems. The methodology adopted is based on a sequential dual use of DEA with restricted weights. This approach increases the discriminatory power of standard DEA and makes it possible to achieve a better balancing of robot performances. Further benefits refer to the possibility of extending the use of this approach to various multi-attribute decision-making problems where each performance may depend on a number of factors. An empirical application of the methodology, using data from 12 robot manufacturers, confirms the applicability of revised DEA to advanced manufacturing technology selection, and reinforces its use as a tactical/operational tool in the area of production/operations. In order to evaluate the overall balancing of robot performance indicators, a sensitivity analysis (with variable weight restrictions) is also carried out. The comparison of the results with those obtained by applying cross-efficiency, another DEA-based methodology (Baker and Talluri 1997 Computers and Industrial Engineering , 32 , 101- 108), is also addressed and discussed. Finally, the dual model of DEA has helped to provide a useful economical and technological analysis of the inefficient robots.

Fuzzy clustering procedure for evaluation and selection of industrial robots

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased, with many now offering a wide range of robots. A potential user is faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem using fuzzy cluster analysis. Unlike most other models for robot selection, this model considers the fact that a robot's performance, as specified by the manufacturer, is often unobtainable in reality. Robots selected by the proposed model become candidates for factory testing to verify manufacturer's specifications. The proposed model is tested on a real data set, and an example is presented.

Fuzzy clustering procedure for evaluation and selection of industrial robots : Moutaz Khouja, David E. Booth, pp. 244–251

Fuzzy clustering procedure for evaluation and selection of industrial robots : Moutaz Khouja, David E. Booth, pp. 244–251

THE INDUSTRIAL ROBOTS SELECTION PROBLEM: LITERATURE REVIEW AND DIRECTIONS FOR FUTURE RESEARCH

Abstract Research on the industrial robot selection problem (RSP) has received increased attention in the past decade. In this paper, RSP models are reviewed and classified. The strengths and weaknesses of the different approaches to the RSP are summarized. A tabular framework is used to summarize the reviewed RSP models. For quick and easy reference, the table categorizes the models by application, solution approach, robot attributes considered, and selection criteria. We suggest directions for future research

A Robust Multivariate Statistical Procedure for Evaluation and Selection of Industrial Robots

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased, with many of these firms now offering a wide range of robots. A potential user is thus faced with many options in both performance and cost. Proposes a decision model for the robot selection problem using both a robustified Mahalanobis distance analysis, i.e. a multivariate distance measure, and principal‐components analysis. Unlike most other models for robot selection, this model takes into consideration the fact that a robot′s performance, as specified by the manufacturer, is often unobtainable in reality. The robots selected by the proposed model become candidates for factory testing to verify manufacturers′ specifications. Tests the proposed model on a real data set and presents an example.

Development of an expert system for industrial robot selection

The number and types of industrial robots available on the market make the selection process an increasingly difficult one for potential robot users. This paper describes an expert system designed for the selection of industrial robots. The system takes into account functional, organizational as well as economical criteria in the selection process. It is user friendly and runs on the VAX/11-750 central processor.

Robot Configuration and Servicing Strategy for a Given Manufacturing Cell Layout

This paper starts from the fact that for a successful designing it is necessary to analyse and check in advance the characteristics of industrial robots (IR) of different configurations, in order to find the one that corresponds to the technological purpose. Research work refers to the investigations of influence of different industrial robots configurations to the servicing machine-tool coefficient. Algorithm has been developed for the analysis of coeficients of a servicing the industrial robots of various configurations. By configuration it is meant here its structure and parameters. The objective of this analysis is that the criterion for the service, among other combined ones, helps in selection of industrial robots and their configuration. Servicing criteria also apply to the selection of a flexible manufacturing cell bots.