As part of the Smart Factory Expo, a novel ‘Hack and Pitch’ challenge was held, to help spur digital manufacturing innovation.
Run by the UK’s Digital Catapult, the Hack and Pitch challenge presented teams of SMEs and startups with a selection of manufacturing-related problems to solve.
Steven Wood from Digital Catapult said: “What we are trying to achieve is to present to these digital startup companies that manufacturing is a fantastic market for them to look at.
“It’s also a way of showing manufacturers what an amazing talent pool these companies are, particularly in Industry 4.0 where there is going to be an awful lot of change, and a need for skills and capabilities that most manufacturers won’t have.
“But collaboration with these problem solving companies is a really great way for them to work their way in.”
Over the two days of the conference, 12 teams, making up over 30 participants (included 10 SMEs, judges and delegates from the 3 challenge owners) competed to solve three individual manufacturing-related problems.
Each problem was set by one of the sponsoring companies of the event, including one from Thales, BOC and RS COMPONENTS.
During the competition, the teams worked together to solve these problems using their own experiences working with digital systems. Their solutions were then presented through a series of lectures to a panel of judges who determined which solutions were deemed to be the best.
The judging criteria for the final pitch were:
- Potential impact on the industry,
- Level of Ingenuity or innovation and
- Quality of Presentation
At the end of the two day Expo, the teams were narrowed down to a series of finalists who had the best solution to each respective problem. From these three finalists a final winner for the entire Hack and Pitch competition was chosen, and presented with an award.
The challenging problems and the winners are:
Railway asset mapping and data interpretation
Thales owns a number of assets in railways such as train signals, radios, equipment boxes and these assets could be located in open or confined spaces like tunnels.
The company wants to be able to remotely and accurately locate and identify these assets using technologies like GPS and GIS and/or intelligent tagging.
Additionally, Thales is looking to combine information about asset specification and location with other data gathered from visual surveys (i.e. photographs, videos, 3d scans), as well as techniques like LIDAR.
This asset information needs to be accessible to engineering teams in the office, as well as maintenance staff that use hand held devices to identify assets on site.
With this challenge, Thales was looking for companies who are exploring solutions in the following areas: • GPS and GIS • Remote asset identification, asset mapping and asset tagging • Data formatting, integration and visualisation • Creating real-time site views.
Category winner for Thales is Techmodal. The participants were David Evans and Liam Nallen.
Use of sensors for machine monitoring
BOC’s teams monitor a diverse range of machinery used in the gas production process. While most BOC plants are monitored through a Remote Operating Centre, many of the machines have limited sensors or gauges.
For those that do, the inspection process is manual without real-time visibility of variables like vibrations, temperatures (process air, water) and pressures (process air).
BOC would like to more effectively track its machines, especially compressors, to be able to respond promptly to alerting signs and ultimately avoid failures.
Core to the challenge is that some of these compressors are old (i.e. Demag VK125 compressor from the 1970s). Installation of sensors on an old machine may mean extensive work modifying bearing housings and field cabling, something that BOC’s wants to avoid.
Through this challenge, BOC was looking to connect with companies who are exploring solutions in the following areas: • Use of sensors to monitor machinery. • Companies with IoT solutions applied to manufacturing. • Smart signalling. • Smart instrumentation. • Machine Management Technologies
Category winner for BOC is Asset Handling Ltd. The participant was Steve Edgson.
Image recognition technologies that enable product identification for customers and report inventory availability
RS COMPONENTS has over 500,000 products ranging from electronic components, connectors, cables, mechanical products and tools, and IT equipment.
These products are sold to diverse companies. Replacing any of these parts is currently a manual process.
RS COMPONENTS wants to offer a solution to their customers by exploring image recognition technologies that can enable faster identification of products and integrate them with available inventory.
Additionally, the company was interested to hear about machine learning techniques and tools that can increase the accuracy of image recognition.
Through this challenge, RS Components was looking to connect with companies who are exploring solutions in the following areas: • Image processing and recognition with application in manufacturing. • Convolutional neural networks. • 3-D object recognition. • Image restoration. • Machine learning applied to image recognition
Category winner for RS COMPONENTS is COSMONiO LTD. The participants were Dr Ioannis Katramados and Dr Laurens Hogeweg.
COSMONiO was also judged the overall Hack & Pitch champion by the judges apart from winning the RS COMPONENTS challenge.
