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The investment in innovative technology demonstrates the automotive market’s explosive interest in artificial intelligence development. By 2025, Tractica projects that the market for automotive hardware, software, and services will reach nearly $27 billion. Data Science, Machine Learning, Artificial Neural Networks, and Text Mining are all technologies that, while still in their infancy in the online marketing and financial worlds, have a lot to offer manufacturing in general, and the automotive industry in particular.
AI has applications throughout the automotive lifecycle, from design and development to testing and production to marketing. The data generated by the numerous sensors embedded in vehicles today, as well as data extracted from production lines and compiled from customer feedback, are extremely valuable sources of information. Their analysis and interpretation provide equally effective levers for enhancing design, testing, and maintenance, as well as for gaining a better understanding of user needs and expectations.
The research into the development of smart vehicle technology is concentrated on the issue of environmental perception, which includes infrastructure, other vehicles, pedestrians, and any other object that could be considered an impediment to a car. Radar, sensors, cameras, weather conditions, roadworks, and other extraordinary events: the machine must be capable of recognizing and evaluating all external influences on the vehicle’s trajectory in order to make real-time corrections to the driving control system.
Here are six ways in which AI will improve the auto manufacturing sector:
- Less equipment failure.
If a machine on an automotive assembly line fails unexpectedly, the resulting costs can be catastrophic. Employees who are idle are unable to meet their production quotas. A factory’s entire operation can be thrown into disarray. AI-based algorithms can ingest massive amounts of data from vibration sensors and other sources, identify anomalies, diagnose the problem, and forecast the likelihood or imminence of a breakdown.
More productive employees through robot-human collaboration.
Computer vision advances are accelerating the development of collaborative, context-aware robots. Increased computing power and improved algorithms will enable the development of flexible, general-purpose robots that can work alongside humans while requiring less configuration to respond to changes in their environment. For instance, Rethink Robotics is developing collaborative robots that can be programmed by a human instructor simply by grasping the robot’s arm and guiding it through desired movements such as gripping and releasing objects.
- Fewer quality problems.
Human workers perform quality control tasks such as inspecting painted car bodies. This procedure is prone to errors and is relatively inefficient. However, even automated methods can fail due to the large number of variables present in the test environment. If the lighting is inadequate or if the product is mounted slightly off-center for inspection, the current method may generate false positives. By contrast, AI-assisted visual quality control can filter out these issues and focus exclusively on defects. The AI system is constantly improving its analysis in response to feedback. AI-powered hardware can visually inspect and provide superior quality control on a variety of products, including machined parts, painted car bodies, and textured metal surfaces.
- Leaner supply chains.
Although precise prediction is crucial for closeness between supply and demand, a huge amount of data, such as stocking high volumes of low-volume long-duty products and just-in-time production, eliminating inventory pads, overwhelmed conventional forecasting and refuelling systems. AI systems can address these issues by utilizing machine learning to generate more precise demand forecasts.
Smarter project management.
It can be challenging to track R&D progress or determine when to terminate a project in order to reallocate resources to more promising R&D initiatives. As a result, zombie projects with unclear status and milestones tend to linger, wasting money, increasing total R&D costs, lengthening the time required to market worthwhile projects, and causing widespread frustration. AI-based methodologies can help prioritise R&D projects and boost performance within individual projects, freeing up budgets and increasing overall efficiency.
- Improved business support functions.
Finance, human resources, and information technology are resource-intensive but critical to a business’s success. Demand for digitization is being driven by cost constraints and flexibility requirements. AI has a high potential for automating previously supported by computer systems tasks such as information technology or finance. For instance, on an IT service desk, codified problem-solving strategies and knowledge (such as server configuration) can be fed into an AI system, which can automatically combine disparate pieces of knowledge to create a custom problem-resolution process.
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