Design Sustainability of Reconfigurable Machines

Abstract

This paper presents the sustainability assessment for reconfigurable machines based on cosine similarity measures and Euclidean distances. The methodology entails the application of four peculiar sustainability indicators (Reconfigurability, Manufacturing, Functionality, and Life Cycle Analysis) that are suitable for reconfigurable machines alongside the traditional sustainability indicators (Environmental, Social, and Economic). An index relating chart approach is proposed for determining the indices of the peculiar and traditional sustainability indicators and their sub-indicators. The chart involves the identification of viable links of the sub-indicators of an indicator and the sub-indicators of other indicators. The viable links are fuzzified using the fuzzy trapezoidal set because of the multi-dimensions and units of the sustainability indicators. The cosine similarity measures of the sustainability indicators were aggregated to estimate the sustainable similarity measures of the reconfigurable machines while the Euclidean distance estimates the distances of the indicators to best and worst sustainable performance in order to identify the sustainability indicators for improvement. Experts' opinions are applied to appraise the availability of sub-indicators in the four reconfigurable machine prototypes (vibrating screen, assembly fixture, bending press machine, and flexible fixture) used as case studies. A sensitivity analysis was carried out to validate the computational process of the methodology. The sensitivity analysis shows that the application of cosine similarity measures is suitable for assessment of sustainability considering the closeness of the similarity measures for the defuzzified values of the trapezoidal fuzzy numbers and the cosine similarity values of the aggregating matrix of the indicators. Also, the findings that can be deduced from the results of the appraisal of the case studies presented in this article shows that high sustainable index and similarity measures can be achieved by creating a balance in the performance of all the sustainable indicators. The results from the assessment also support the fact that improving one sustainability indicator because of its closeness to the worst sustainable performance may cause other indicators not to perform satisfactorily.

Considering Sustainability in Reconfigurable Manufacturing Systems Research – A Literature Review

“…The common aim of the models proposed in these papers is to reduce costs, whereas it is found that if achieving this aim, an economic sustainability is fulfilled. The cost reduction is specified to certain areas, for instance maintenance [26,28,39,43], inventory [38,39], transportation [22,25,35], investment [21,43], manufacturing [19,29,31,38,39], and reconfiguration cost [22,26,29,31,32,35,39]. The latter can be recognized as a fairly exclusive cost for RMS, on the basis that this type of manufacturing system leads to more frequent system-, machine-and tool changes.…”

“…In total, 7 of the reviewed papers included social sustainability in their research. Factors such as employee health and safety [3,17,27,39], training and education [25,27,39], and ethical and legal following [19,27,39] are recurrently discussed in multiple papers as having a connection to RMS. Some authors are describing social sustainability in an imprecise term and focus on the general factors which a production system can lead to, as for instance in Kurniadi & Ryu [38] where the ability to reconfigure a manufacturing system leads to the possibility to match requirements in terms of quantity and products to changing demands.…”

“…For instance, Peukert et al [19] developed a model where social sustainability is included through adding the factor of performing a fair wage assessment. Olabanji & Mpofu [39] on the other hand, developed a novel sustainability assessment model for reconfigurable machines. In their model, a social indicator is taken into consideration where, for instance, operator training, required level of maintenance, patenting and usage regulation, ethical issues/responsibilities are included.…”

A Treatise on Reconnoitering the Suitability of Fuzzy MARCOS for Assessment of Conceptual Designs

“…Considering the fact that the design features that are usually applied as criteria in the decision process are of different dimensions and units, researches have introduced the theory of fuzzy membership functions and rough numbers in the MADM models. The introduction of the fuzzy and rough number theories is to cater for the multifarious units and dimensions of the design features and ensure that the decision process is unprejudiced and there is no allocation of crips value to weights of the design features of different units and dimensions or performance of the design concepts in the decision matrix [17]. Depending on the nature and objectives of the decision process and the complexity of the design features, Triangular Fuzzy Number (TFN) and Trapezoidal Fuzzy Number (TrFN) has been applied as membership functions in different MADM models in order to proffer solution in the decision process of selecting optimal conceptual design [18].…”

Review of life cycle models for enhancing machine tools sustainability: lessons, trends and future directions

“…The examples of machine tools include; lathe, drill press, milling machine, screw machine, etc as depicted in Figure 1 . The machines tools can further be classified into dedicated machine tools ( Landers et al., 2001 ; Mayr et al., 2012 ; Chanal et al., 2007 ; Xu and Newman, 2006 ; de Lacalle and Mentxaka 2008 ; Koren, 2006 ; Valdes et al., 1997 ; Huang et al., 2010 ; Bringmann et al., 2014 ), Flexible machine tools ( Browne et al., 1984 ; Stecke, 1985 ; Mori and Fujishima 2009 ; Kostal &Velisek 2010 ElMaraghy, 2005 ; Patpatiya et al., 2021 ; Cook et al., 2020 ; Oshel et al., 2020 ) and Reconfigurable machine tools ( Ding et al., 2021 ; Xing et al., 2021 ; Kumar et al., 2021 ; Mahmoodjanloo et al., 2020a , Mahmoodjanloo et al., 2020b ; Huang et al., 2020 ; Ming et al., 2020 ; Bruzzone et al., 2020 ; Wang et al., 2020 ; Wang et al., 2018 ; Gadalla and Xue 2018 ; Olabanji and Mpofu 2020 ; Murena et al., 2017 ; Battaïa et al., 2017 ). The classification of machine tools is shown in Figure 1 .…”