Work-in-Progress—A Generalizable Virtual Reality Training and Intelligent Tutor for Additive Manufacturing

Mogessie, Michael; Wolf, Sandra DeVincent; Barbosa, Matheus; Jones, Nicholas; McLaren, Bruce M.

doi:10.23919/ilrn47897.2020.9155119

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Duffy saw that repeated CNC training can lower workloads, but study participants still struggled between choosing "safe" versus "efficient" operations (Duffy 1999). Lastly, Mogessie makes the case for training modes which can become modularized and generalized, such that training can be redesigned and repurposed for multiple metal additive manufacturing machines as needed (Mogessie et al 2020). Hybrid manufacturing has an extensive future work space; however, the authors see a human factors element to the process that remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Operator 4.0 for Hybrid Manufacturing

Fillingim

Feldhausen

2023

Proc. Des. Soc.

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Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

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Section: Introductionmentioning

confidence: 99%