In the global automotive sector, the adoption of AI-powered quality control has been rapidly evolving in the last few years. Automobile manufacturers like BMW, Volvo and Tesla are increasingly using AI algorithms to identify even minute defects in vehicle components to minimise human error, create consistency and reliability during inspections, enable safer driving experiences, and make driving more comfortable and entertaining. Data indicates that the total number of vehicles equipped with autonomy-enabling hardware are likely to grow from 137,129 units in 2018, to 745,705 units by 2023.
In this blog, we explore the different areas where AI is impacting quality control in global autmotives, and the resultant challenges that these technologies present. We’ll also look at how organisations plan to overcome these, and what we can expect in the future.
5 Areas AI is Creating an Impact in Automotive Quality Control
AI-driven systems are transforming manufacturing processes and improving quality control in a number of ways;
- AI systems are able to automate visual inspection tasks using a combination of AI and computer vision technologies, thus reducing product defects and ensuring greater quality control.
- Using predictive maintenance, AI is able to predict when equipment is due for maintenance, or likely to fail, thus enabling manufacturers to perform maintenance before an event occurs. This reduces downtime and improves operational efficiency.
- AI is able to perform root cause analysis, and identify the source of a defect, thus allowing manufacturers to take corrective action and improve output quality.
- AI is capable of understanding customer purchase patterns and predicting demand. This allows manufacturers to optimise inventory and production schedules.
- AI is able to draw data on manufacturing processes and come up with solutions to optmise production and processes, thus resulting in cost savings, and higher efficiency.
Tesla uses a number of AI powered technologies in its manufacturing process. It uses machine learning to optimise placement of robots to identify and correct defects in the assembly line. It uses predictive maintenance systems to monitor its manufacturing equipment and predict maintenance, when needed. These measures have allowed the company to improve its operational efficiency and reduce downtime.
Supply Chain Optimisation
AI algorithms are reshaping supply chain management by predicting demand, optimising inventory levels, and identifying potential disruptions.
A Merit expert says, “One way it’s happening is manufacturers are using advanced data analysis to predict customer demand and anticipate future trends. This capability is enabling them to fine-tune production schedules, and ensure optimal inventory levels to meet market requirements.”
- AI-driven solutions are able to streamline supply chain processes and mitigate the risks associated with stockouts or overstocking. This is enhancing overall operational efficiency and responsiveness to market fluctuations.
- Automotive manufacturers are able to use AI to refine pricing strategies by accurately predicting and aligning supply with demand. This dynamic approach is able to ensure that pricing models are agile and responsive, contributing to improved profitability and market competitiveness.
Volkswagen, for instance, has been employing AI to analyse vast datasets and make informed decisions about sourcing materials, reducing lead times, and ultimately improving the reliability of the entire supply chain.
In-Car Quality Control
Advanced AI-powered inspection systems are being employed for in-car quality control, detecting even the smallest defects. These systems use advanced machine learning algorithms to analyse images of car parts and identify defects that human inspectors may have missed.
Visual Quality Control (VQC) is one such AI-powered inspection system that is being used in the automotive industry. VQC has proven to be faster than manual inspections because it can analyse images in real-time, without compromising on product quality.
Ford, for example, is using computer vision and machine learning to ensure that every vehicle undergoes rigorous checks. This enhances the final product’s reliability and reduces the likelihood of recalls due to manufacturing defects.
Flaw Detection with Computer Vision
Computer vision technologies are playing a crucial role in flaw detection during manufacturing. Here are examples of some applications;
Quality Assurance: Automotive manufacturers employ computer vision-powered image and video annotation tools to analyse electronic and safety components, to ensure the highest quality standards for vehicles before mass production.
Automated Manufacturing: It is being utilised to minimise human exposure. Cameras are being positioned over production lines to identify defects or deviations in new parts. In the event of a misalignment or surface imperfections, these cameras immediately alert manufacturing personnel, and prompt to take corrective action.
Intelligent Driving Assistance: Computer vision is being harnessed to enhance driver safety, through built-in sensors to detect roads, vehicles, obstacles, and pedestrians. This technology is able to provide real-time warnings to drivers, helping them navigate safely, and avoid potential collisions.
Enhanced Autonomous Cars: When it comes to autonomous vehicles, computer vision is playing a critical role in ensuring safer driving experiences, issuing perceptive warnings, executing 3D mapping, refining LIDAR orientation for comprehensive 360-degree environmental analysis, and automating airbag deployment in response to potential threats.
General Motors, for example, has been utilising computer vision algorithms to analyse visual data from the production line, identifying defects such as paint imperfections or misalignments. This proactive approach has been able to prevent faulty products from reaching consumers, in turn upholding the brand’s reputation for excellence.
AI has been at the forefront of developing hardware components crucial for autonomous vehicles. NVIDIA has developed a Drive platform that integrates AI processors and sensors, enabling vehicles to perceive and respond to their surroundings. This not only ensures the safety of autonomous vehicles but also sets the standard for the reliability of the underlying hardware systems.
With the growing integration of digital technologies in manufacturing, production, and supply chain, the future of AI in quality control looks promising. These advancements not only enhance vehicle quality, but also pave the way for a future secured with improved safety, reduced downtime, and greater operational efficiency across the automotive industry.
Merit’s Expertise in Data Aggregation & Harvesting for the Global Automotive Sector
Merit Data and Technology excels in aggregating and harvesting automotive data using AI, ML, and human expertise. Our capabilities include:
- Crafting end-to-end data pipelines and scalable data warehouses
- Designing compliant governance solutions for seamless integration
- Utilising high-volume, high-velocity data tools for nuanced insights
- Extracting retail product attributes and audience data
- Aggregating industry-specific data points for informed decision-making
Trusted by leading automotive brands, Merit drives innovation and efficiency by delivering refined, actionable insights.
- AI Driving Precision: AI enhances manufacturing precision through automated visual inspection, predictive maintenance, and root cause analysis, reducing defects and improving efficiency.
- Supply Chain Optimisation: AI algorithms predict demand, optimise inventory, and refine pricing, empowering manufacturers like Volkswagen to make informed decisions and enhance supply chain reliability.
- In-Car Quality Leap: Advanced AI-powered inspection systems like Visual Quality Control (VQC) revolutionise in-car quality control, ensuring defect detection with speed and precision, exemplified by Ford’s rigorous checks.
- Computer Vision Revolution: Computer vision technologies play a vital role in flaw detection, from quality assurance in manufacturing to intelligent driving assistance, exemplified by General Motors’ use of algorithms to prevent faulty products from reaching consumers.
- Autonomous Future: AI-driven autonomy-enabling hardware, exemplified by NVIDIA’s Drive platform, sets new standards in ensuring the safety and reliability of autonomous vehicles, reshaping the future of automotive technology.
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