Browsing by Author "Prabhu, Vinayak Ashok"

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    Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica
    (BMC (part of Springer Nature), 2020-06-03) Prabhu, Vinayak Ashok; Thomas, Dominic J.; Ledesma-Amaro, Rodrigo; Leeke, Gary A.; Medina, Angel; Verheecke-Vaessen, Carol; Coulon, Frederic; Agrawal, Deepti; Kumar, Vinod
    Background Xylitol is a commercially important chemical with multiple applications in the food and pharmaceutical industries. According to the US Department of Energy, xylitol is one of the top twelve platform chemicals that can be produced from biomass. The chemical method for xylitol synthesis is however, expensive and energy intensive. In contrast, the biological route using microbial cell factories offers a potential cost-effective alternative process. The bioprocess occurs under ambient conditions and makes use of biocatalysts and biomass which can be sourced from renewable carbon originating from a variety of cheap waste feedstocks. Result In this study, biotransformation of xylose to xylitol was investigated using Yarrowia lipolytica, an oleaginous yeast which was firstly grown on a glycerol/glucose for screening of co-substrate, followed by media optimisation in shake flask, scale up in bioreactor and downstream processing of xylitol. A two-step medium optimization was employed using central composite design and artificial neural network coupled with genetic algorithm. The yeast amassed a concentration of 53.2 g/L xylitol using pure glycerol (PG) and xylose with a bioconversion yield of 0.97 g/g. Similar results were obtained when PG was substituted with crude glycerol (CG) from the biodiesel industry (titer: 50.5 g/L; yield: 0.92 g/g). Even when xylose from sugarcane bagasse hydrolysate was used as opposed to pure xylose, a xylitol yield of 0.54 g/g was achieved. Xylitol was successfully crystallized from PG/xylose and CG/xylose fermentation broths with a recovery of 39.5 and 35.3%, respectively. Conclusion To the best of the author’s knowledge, this study demonstrates for the first time the potential of using Y. lipolytica as a microbial cell factory for xylitol synthesis from inexpensive feedstocks. The results obtained are competitive with other xylitol producing organisms.
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    Digitisation of a moving assembly operation using multiple depth imaging sensors
    (Springer, 2015-10-09) Prabhu, Vinayak Ashok; Song, Boyang; Thrower, John; Tiwari, Ashutosh; Webb, Philip
    Several manufacturing operations continue to be manual even in today’s highly automated industry because the complexity of such operations makes them heavily reliant on human skills, intellect and experience. This work aims to aid the automation of one such operation, the wheel loading operation on the trim and final moving assembly line in automotive production. It proposes a new method that uses multiple low-cost depth imaging sensors, commonly used in gaming, to acquire and digitise key shopfloor data associated with the operation, such as motion characteristics of the vehicle body on the moving conveyor line and the angular positions of alignment features of the parts to be assembled, in order to inform an intelligent automation solution. Experiments are conducted to test the performance of the proposed method across various assembly conditions, and the results are validated against an industry standard method using laser tracking. Some disadvantages of the method are discussed, and suggestions for improvements are suggested. The proposed method has the potential to be adopted to enable the automation of a wide range of moving assembly operations in multiple sectors of the manufacturing industry.
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    Digitisation of manual composite layup task knowledge using gaming technology
    (Elsevier, 2016-12-29) Prabhu, Vinayak Ashok; Elkington, Michael; Crowley, Dennis; Tiwari, Ashutosh; Ward, Carwyn
    Increased market demand for composite products and shortage of expert laminators is compelling the composite industry to explore ways to acquire layup skills from experts and transfer them to novices and eventually to machines. There is a lack of holistic methods in literature for capturing composite layup skills especially involving complex moulds. This research aims to develop an informatics-based method, enabled by consumer-grade gaming technology and machine learning, to capture and digitise manufacturing task knowledge from skill-intensive hand layup. The digitisation is underpinned by the proposed human-workpiece interaction theory and implemented to automatically extract and decode key knowledge constituents such as layup strategies, ply manipulation techniques, motion mechanics and problem-solving during hand layup, collectively categorised as layup skills. The significance of this research is its potential to facilitate cost-effective transfer of skills from experts to novices, real-time automated supervision of hand layup and automation of layup tasks in the future.
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    Editorial for the special issue on manufacturing informatics for intelligent automation
    (SAGE Publications (UK and US), 2017-01-01) Tiwari, Ashutosh; Webb, Phil; Prabhu, Vinayak Ashok
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    A framework for digitisation of manual manufacturing task knowledge using gaming interface technology
    (Cranfield University, 2015-10) Prabhu, Vinayak Ashok; Tiwari, Ashutosh
    Intense market competition and the global skill supply crunch are hurting the manufacturing industry, which is heavily dependent on skilled labour. Companies must look for innovative ways to acquire manufacturing skills from their experts and transfer them to novices and eventually to machines to remain competitive. There is a lack of systematic processes in the manufacturing industry and research for cost-effective capture and transfer of human skills. Therefore, the aim of this research is to develop a framework for digitisation of manual manufacturing task knowledge, a major constituent of which is human skill. The proposed digitisation framework is based on the theory of human-workpiece interactions that is developed in this research. The unique aspect of the framework is the use of consumer-grade gaming interface technology to capture and record manual manufacturing tasks in digital form to enable the extraction, decoding and transfer of manufacturing knowledge constituents that are associated with the task. The framework is implemented, tested and refined using 5 case studies, including 1 toy assembly task, 2 real-life-like assembly tasks, 1 simulated assembly task and 1 real-life composite layup task. It is successfully validated based on the outcomes of the case studies and a benchmarking exercise that was conducted to evaluate its performance. This research contributes to knowledge in five main areas, namely, (1) the theory of human-workpiece interactions to decipher human behaviour in manual manufacturing tasks, (2) a cohesive and holistic framework to digitise manual manufacturing task knowledge, especially tacit knowledge such as human action and reaction skills, (3) the use of low-cost gaming interface technology to capture human actions and the effect of those actions on workpieces during a manufacturing task, (4) a new way to use hidden Markov modelling to produce digital skill models to represent human ability to perform complex tasks and (5) extraction and decoding of manufacturing knowledge constituents from the digital skill models.
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    Gesture detection towards real-time ergonomic analysis for intelligent automation assistance
    (Springer, 2016-07-10) Mgbemena, Chika Edith; Oyekan, John; Tiwari, Ashutosh; Xu, Yuchun; Fletcher, Sarah R.; Hutabarat, Windo; Prabhu, Vinayak Ashok
    Manual handling involves transporting of load by hand through lifting or lowering and operators on the manufacturing shop floor are daily faced with constant lifting and lowering operations which leads to Work-Related Musculoskeletal Disorders. The trend in data collection on the Shop floor for ergonomic evaluation during manual handling activities has revealed a gap in gesture detection as gesture triggered data collection could facilitate more accurate ergonomic data capture and analysis. This paper presents an application developed to detect gestures towards triggering real-time human motion data capture on the shop floor for ergonomic evaluations and risk assessment using the Microsoft Kinect. The machine learning technology known as the discrete indicator—precisely the AdaBoost Trigger indicator was employed to train the gestures. Our results show that the Kinect can be trained to detect gestures towards real-time ergonomic analysis and possibly offering intelligent automation assistance during human posture detrimental tasks.
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    Remote Real-Time Collaboration Platform enabled by the Capture, Digitisation and Transfer of Human-Workpiece Interactions
    (Future Technology Press, 2015-12-01) Prabhu, Vinayak Ashok; Oyekan, John; Tiwari, Ashutosh; Advikolanu, Yohann; Burgess, Mark; Mcnally, Rob
    In this highly globalised manufacturing ecosystem, product design and verification activities, production and inspection processes, and technical support services are spread across global supply chains and customer networks. Therefore, a platform for global teams to collaborate with each other in real-time to perform complex tasks is highly desirable. This work investigates the design and development of a remote real-time collaboration platform by using human motion capture technology powered by infrared light based depth imaging sensors borrowed from the gaming industry. The unique functionality of the proposed platform is the sharing of physical contexts during a collaboration session by not only exchanging human actions but also the effects of those actions on the task environment. This enables teams to remotely work on a common task problem at the same time and also get immediate feedback from each other which is vital for collaborative design, inspection and verifications tasks in the factories of the future.
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    Remote real-time collaboration through synchronous exchange of digitised human-workpiece interactions
    (Elsevier, 2016-08-30) Oyekan, John; Prabhu, Vinayak Ashok; Tiwari, Ashutosh; Baskaran, Vinubha; Burgess, Mark; Mcnally, Rob
    In this highly globalised manufacturing ecosystem, product design and verification activities, production and inspection processes, and technical support services are spread across global supply chains and customer networks. Therefore, collaborative infrastructures that enable global teams to collaborate with each other in real-time in performing complex manufacturing-related tasks is highly desirable. This work demonstrates the design and implementation of a remote real-time collaboration platform by using human motion capture technology powered by infrared light based depth imaging sensors and a synchronous data transfer protocol from computer networks. The unique functionality of the proposed platform is the sharing of physical contexts during a collaboration session by not only exchanging human actions but also the effects of those actions on the workpieces and the task environment. Results show that this platform could enable teams to remotely work on a common engineering problem at the same time and also get immediate feedback from each other making it valuable for collaborative design, inspection and verifications tasks in the factories of the future. An additional benefit of the implemented platform is its use of low cost off the shelf equipment thereby making it accessible to SMEs that are connected to larger organisations via complex supply chains.