Using the Soar Cognitive Architecture to Remove Screws from Different Laptop Models

This paper investigates an approach that uses the cognitive architecture Soar to improve the performance of an automated robotic system, which uses a combination of vision and force sensing to remove screws from laptop cases. Soar\u27s long-term memory module, semantic memory, was used to remember pieces of information regarding laptop models and screw holes. The system was trained with multiple laptop models and the method in which Soar was used to facilitate the removal of screws was varied to determine the best performance of the system. In all the cases, Soar could determine the correct laptop model and in what orientation it was placed in the system. Soar was also used to remember what circle locations that were explored contained screws and what circles did not. Remembering the locations of the holes decreased a trial time by over 60%. The system performed the best when the number of training trials used to explore circle locations was limited, as this decreased the total trial time by over 10% for most of the laptop models and orientations. Note to Practitioners - Although the amount of discarded electronic waste in the world is rapidly increasing, efficient methods that can handle this in an automated non-destructive fashion have not been developed. Screws are a common fastener used on electronic products, such as laptops, and must be removed during nondestructive methods. In this paper, we focus on using the cognitive architecture Soar to facilitate the disassembly sequence of removing these screws from the back of laptops. Soar is able to differentiate between different models of laptops and store the locations of screws for these models leading to an improvement of the disassembly time when the same laptop model is used. Currently, this paper only uses one of Soar\u27s long-term memory modules (semantic memory) and a screwdriver tool. However, this paper can be extended to use multiple tools by using different features available in Soar such as other long-term memory modules and substates

Digital Technologies as Enablers of Component Reuse : Value Chain Perspectives in Construction & Manufacturing

Our planet is experiencing climate emergency due to the overconsumption of natural resources and ever-increasing carbon footprint. The construction and manufacturing industries are by far the biggest contributors to this grim situation. Hence, it is of paramount importance that the current economic model in those industries shifts from conventional linear to circular. Among the different circular economy (CE) approaches, adopting the component reuse practices is more imperative; because, after reduce, reuse is considered to be the least resource and energy intensive CE principle. With regard to transformation of the construction and manufacturing industries towards component reuse, digitalization could play a major enabling role. However, how the digital technologies such as BIM, digital twin, IoT (sensors and RFIDs), and robots could facilitate the component reuse practices is still an underexplored field of study. Additionally, the studies thus far in this direction lack the integrative approach both from multi-technology and multi-stakeholder perspectives. Therefore, the objective of this research is to investigate the perspectives of value chain actors, in construction and manufacturing, on how the digital technologies can advance component reuse practices. To address the research objective, this study employs qualitative research methodology and therein, multiple case study method. For the selection of most relevant cases, purposive sampling strategy was used. As a result, ten cases were selected, out of which, six are from the construction industry and the remaining four belong to manufacturing industry. To garner the primary data from those cases, semi-structured elite interviews were carried out. Subsequently, the data analysis process proceeded from within-case analysis to cross-case analysis. Finally, the findings from construction industry were juxtaposed to the findings from manufacturing industry, in order to examine the similarities and differences in how the digital technologies can advance component reuse practices in each industry. The findings of this study suggest that both the construction and manufacturing industries are becoming more perceptive to the need circular economy transformation. They recognize that the digital technologies are de facto the cornerstones in their efforts to adopt component reuse practices. The results demonstrate that collectively the BIM and IoT in construction, similar to digital twin and IoT in manufacturing, enables several component reuse practices- namely, DfDR, predictive maintenance, logistics & inventory management, quality & lifecycle assessment, and component disassembly planning. In addition, a few digital technology-enabled reuse practices were identified, that are peculiar to each industry. Robots, for instance, were recognized for the potential to partially automate some repetitive processes in construction industry, but that was not the case in manufacturing. Nevertheless, this study indicate that, for the technologies to be optimal in their enabling role, their current technological capabilities need to be developed further in the future. This study enriches the literature stream in circular economy and digitalization both in terms research methodology and findings. By taking a broader and integrative stance and through comparative study of two industries, this study validates several previous findings and also pro-poses novel findings of its own. To the practitioners the findings will provide comprehensive in-sights that may be useful in their efforts to adopt or foster digitalization in component reuse context. Finally, this study identifies a few directions for future research that may result in promising outcomes

Robotic disassembly of waste electrical and electronic equipment

Waste electrical and electronic equipment (WEEE) is the world’s fastest growing form of waste. Inappropriate disposal of WEEE causes damage to ecosystems and local communities due to hazardous materials and toxic chemicals present in electronic products. High value metals in small quantities are dissipated and embodied energy from manufacturing are lost in shredding and crushing treatments of WEEE. On the other hand, manual disassembly is costly and presents safety concerns for human workers. Therefore, robotic disassembly is an ideal approach to addressing the treatment of WEEE. Despite extensive research in the field, large variations and uncertainties in product structures, models, and conditions is a major limitation to the implementation of automation and robotics in the waste industry. The ability of a robotic disassembly system to learn new product structures and reason about existing knowledge of product structure is vital to addressing this challenge. This thesis explores robotic disassembly for WEEE by building upon an existing research disassembly rig for LCD monitors and expanding it to address other product families. The updated disassembly system utilizes a modular framework consisting of a Cognition module, Perception module, and Operation module, in order to address the uncertainties present in end-of-life (EoL) products. A novel disassembly ontology is designed and developed with an upper and lower ontology structure to represent generic disassembly knowledge and product-family-specific knowledge respectively. Furthermore, a Learning framework enables automated expansion of the ontology using past disassembly experiences and user-demonstration. These presented methodologies form the main function of the Cognition module, which aids the Perception module and instructs the Operation module. The disassembly ontology and Learning framework are verified independently from the rest of the system prior to being integrated and validated with real disassembly runs of LCD monitors and keyboards. As such, the disassembly system’s ability to address both known and unknown EoL product types, as well as learn new product types, is demonstrated