Evaluation of Collaborative Robot Sustainable Integration in Manufacturing Assembly by Using Process Time Savings

Abstract

Collaborative robots are enablers of flexibility in the current dynamic and uncertain manufacturing environment. Decision making on its implementation requires technical feasibility, involving productivity and workforce implications that should be faced in an integrated perspective in processes where many components of different materials are assembled in products of increasing diversity and complexity. This study introduces two new parametric models for collaborative robotics, formulated in order to evaluate the differential cost of assembly (economic dimension) and the differential income from taxes that supports short-term workforce displacement (social dimension) in cobot implementation. Updated techno-economical parameters are selected for assessing feasibility ranges of application in different production scenarios. Next, the influence curves of productivity gain for a feasible implementation of cobot establish thresholds for decision making under both criteria. The results show the need for productivity gains that are significantly lower in high-wage scenarios than in low-wage scenarios; however, in a joint approach, breakeven productivity gain is always higher for the social dimension threshold than for the economic requirement of cost-effective manufacturing, with a higher gap in low-wage cases. The detailed analysis of a real case study of cobot implementation for assembly demonstrates the practical application of models and potential for future research.

Collaborative robots in manufacturing and assembly systems: literature review and future research agenda

“…The development of digital representation of the workers by collecting real time data regarding workers' status, well-being, health and safety parameters (including postures and fatigue as previously discussed) will be strategic to support the operation manager decision making in the near future. Digital ergonomics tools, biosensors and wearable sensors coupled with ML techniques will be also strategic in this context to design a digital twin for human workers and create effective and sustainable collaborative working environments (Calvo & Gil, 2022;Lin & Lukodono, 2021;Berti et al, 2022). As it can be seen in Appendix Table 6, currently, a few studies investigated a sustainable collaborative system and all of them assert that more studies required to fully developed this issue, also some suggestion provided by these studies (Calvo & Gil, 2022;Gualtieri et al, 2022).…”

“…Digital ergonomics tools, biosensors and wearable sensors coupled with ML techniques will be also strategic in this context to design a digital twin for human workers and create effective and sustainable collaborative working environments (Calvo & Gil, 2022;Lin & Lukodono, 2021;Berti et al, 2022). As it can be seen in Appendix Table 6, currently, a few studies investigated a sustainable collaborative system and all of them assert that more studies required to fully developed this issue, also some suggestion provided by these studies (Calvo & Gil, 2022;Gualtieri et al, 2022). As a consequence, the integration of key concepts from the human factors engineering discipline will be strategic to assess cobots use and benefits in the context of Industry 4.0 (Neumann et al, 2021).…”

Task allocation and planning for product disassembly with human–robot collaboration

“…As we grapple with the urgent need to transition to a more circular economy, HRC in disassembly can play a crucial role in waste reduction, resource recovery, and extended product lifecycles. However, it is important that the economic benefits of this technology do not undermine social equity [220]. Strategies need to be developed that simultaneously foster technological progress and socio-economic responsibility.…”

Collaborative approaches in sustainable and resilient manufacturing

“… • Improve working conditions & reduce health problems : - Human-robot collaboration (Renteria & Mozos, 2019 ; Gualtieri et al, 2020 ; Ansari et al 2020 ; Lv et al, 2021 ; Poschmann et al, 2021 ). • Support short-term workforce displacement : - Collaborative robotics & social impact (Calvo & Gil, 2022 ). • Reduce energy & resources consumption : - Shared factories (Li & Jiang, 2021 ).…”