Manufacturing & Industrials
Use Cases
Newsfor Manufacturing & Industrials
Accelerate product discovery based on modelling of components
Accelerate product discovery based on modelling of components - potentially millions of input options can be modelled to ascertain the most promising.
Analyse social media data to evaluate architectural or planning impact of new developments
Analyse social, traffic data to measure potential impact of new building developments. Modeled outcomes might indicate hazards (e.g. fire evacuation risks) or design features for consideration (e.g. siting of facilities etc)
Automate driving with self-driving vehicles
Self-driving vehicles are a poster child for AI. In fact there are multiple levels of risk, autonomy and functionality that this encompasses. There will likely be many linked network vehciles or protected environment deployments before we see fully autonomous vehicles deployed in a non-test environment on city streets.
Automate key tasks to support human driver
Driving assistant - example tasks might be auto-parking or autopilot features on highways. Both enable the driver to sit back but require continued monitoring of the situation.
Collate and evaluate site data for architectural planning
Capture, collate and visualise multiple sources of data to build projected site layouts for architectural purposes. Data sources will include geo-relevant public data sources, private data sets and potentially sensor data.
Confirm animal identity through eye scans
HIgh value animals can need identifying at critical moments - for example to ensure that the right horse has been entered in a race or has been provided to stud. Individual eye scans are kept on record and then a portable scanner can be used to ensure that the right aninal is present.
Construct detailed map of farm characteristics based on aerial image capture
Construct detailed map of farm characteristics based on aerial video or image capture. This can be of assistance for a series of tasks ranging from planning to valuation to deployment of automated farming tools.
Deploy robots to do physical tasks in the agricultural process
Deploy robots to do physical tasks in the agricultural process, for example planting, watering and harvesting. Continuing the culture of automating agriculture started in the 18th Century.
Detect defects and quality issues during production using visual and other data
Detect defects and quality issues during production using visual and other data. This process will potentially be impacted by unexpected issues - e.g. a change in the quality of the lighting on a production line.
Detect suspicious nautical vessel activity indicating overfishing or smuggling
Using image and location tracking data to identify suspicious behaviour patterns by nautical vessels that might indicate, for example, over-fishing. This would also include multi-vessel activity to predict load transfer. Similar technological approaches might be used to identify other nautical malfeasance such as smuggling.
Determine root causes for quality issues originating outside of manufacturing eg in the supply chain
Determine root causes for quality issues originating prior to the manufacturing process. This might include supply sources or logistic process issues. Close human analyst oversight recommended.
Digitise analogue meter reading through computer vision
Updating and replacing legacy analogue meters with digital meters can be an expensive and complicated process. If instead IoT cameras are positioned to capture images of the read-outs and translate those to digital read-outs then the information can be captured automatically. This enables greater speed, frequency and consistency of data capture, along with reduced risk and cost from human checking - potentially enabling multiple other use cases.
Discover anomalies across fleet of vehicle sensor data to identify potential risks
Discover anomalies across fleet of vehicle sensor data to identify potential failure risks. This may enable companies to pre-empt expensive and embarassing recalls, often driven by negative PR.
Enable self-learning option generation in CAD software
Support 3D CAD (Computer Aided Design) software - e.g. Autodesk Dreamcatcher - to build options in to the design process. Environmental, traffic data used to help analyse potential outcomes. Architects / design staff can make educated decisions with visualised data-rich options.
Enhance search process for new molecular structures
Machine learning can be used to speed up the product research and development phase of the chemical industry.
Examine ground composition to reduce required volume of exploratory drilling for samples
Interpolate data on ground composition to reduce the required exploratory drilling samples. This increases speed to market and reduces the potential workload.
Identify and navigate roads and obstructions in real time for autonomous driving
Identify and navigate roads and obstructions in real-time for autonomous driving. This requires a mapping of both relatively static elements - such as the road layout - but also dynamic threat assessment.
Identify and validate a molecule to target with a drug compound for agricultural use
During the initial phase of drug research and development, the target for a new drug treatment must first be chosen. The target molecule will be what the drug compound interacts with to get the intended outcome, often the treatment of a disease. Researchers use deep neural networks to predict molecular level interactions to treat a condition or improve particular functionality
Identify design problems in pre-production to reduce ramp-up time to maximum output
Identify design problems in pre-production to reduce ramp-up time to maximum output. Modelling potential scenarios will help establish capacity planning constraints,
Identify issues driving low product yield in manufacturing
Identify root causes for low product yield (for example input, tool or machine specific issues) in manufacturing. This analytical work will likely require working closely with human analysts.
Identify plant type through image analysis
Using images to identify plant type will enable wider access to up to date agricultural knowledge - whether requiRed remotely (for example to track disease spread) or locally (to support better farming, gardening or rambling).
Improving construction process quality by detecting error
Construction work is very reliant on the quality of both staff and site management, roles which may be harder to deliver in hard to access or inhospitable locations. Machine vision can be used to ensure that quality standards are being met and errors minimised and to ensure a rapid feedback loop to avoid potential cost (and engineering safety) issues.
Manage agricultural production yield by resource optimisation
Increasing production yields through optimising system-long inputs from team, machine, supplier and customer requirements through machine learning.
Mimic creature or animal behaviour with drones or robotics
Mimicing animal behaviour in drones or robots has several potential uses: camouflage (for example in a security situtation), research or media support (allowing humans to better interact with creatures in their natural environment) or replicating (or even replacing) tasks currently performed by natural creatures (pollination for example).
Modify driver experience based on emotion tracking through facial recognition system
Modify driver experience based on emotion tracking through facial recognition system. This will for example lead to different music being played, lighting set up and driving advice offered.