Quality Monitoring – Safety Essential in the Automotive Industry

Many OEMs and Tier 1 Suppliers in Automotive are adopting real-time quality control of spot welds in body construction, as well as the production of automotive parts and components. Preparation for the application of this technology is gaining momentum, from traditional automakers to new entrants alike. To assist in the production ramp of electric vehicles, Heron believes in a data-driven approach to monitoring joint strength.

Often, the safety and long-term quality of a car, as well as how it is maintained over decades is impacted by the joint quality. Heron believes every new assembly system should be equipped with an integrated quality monitoring system to ensure the quality of resistance spot welds. In this article, we will explore the latest trends in quality monitors, and how big data analysis and artificial intelligence (AI) plays a role in the predictive nature of joint quality in large-scale production.

Auto Parts & Components

Welding is a key process of automotive manufacturing. Effective welding contributes greatly to the performance, quality, and safety of the manufactured product. With the help of digital weld monitors (also known as weld checkers or weld testers) , Tier 1 suppliers can analyze one or more of the electrical and/or mechanical parameters that dynamically change during the resistance welding process. These parameters include weld current, voltage drop across the electrodes, work piece expansion and deformation, electrode force, electrode displacement (movement) and more. 

The dataset generated by the real-time quality monitor can be used to identify areas for improvement and optimize processes in the future. For instance, the reference data allows conclusions to be drawn about the quality of the non-destroyed components. With the help of this data, the interval between destructive tests can be increased and the number of test parts reduced. This dataset also serves as the basis for predictive maintenance (PdM), helping manufacturers determine the condition of in-service equipment.

Auto Body-in-White

Preparations for the roll-out of artificial intelligence for quality control at brands from the Volkswagen Group have already begun. At its Neckarsulm site, Audi uses artificial intelligence to analyze around 1.5 million spot welds from 300 vehicles per shift. By way of comparison,  employees at the same plant previously had to monitor the quality of the processes in resistance spot welding manually on a random basis using ultrasound. This technology involves a highly difficult and complicated process of checking more than 5,000 spot welds per vehicle. 

Whether you are manufacturing a light-weight vehicle, pick-up truck, delivery van or school bus, passenger safety on all vehicle segments heavily rely on good quality joints. By collecting data with a real-time digital monitor and relying on AI to make quality assessments, employees can better focus on anomalies and thus control quality in an efficient and targeted manner. This is the factory of the future.

Quality Inspection vs. Quality Monitoring

Whereas “quality inspection” is inherently evaluative in nature, “quality monitoring” is preventative. According to TWI, welding process monitoring and control involves “routine or continuous monitoring of process variables, particularly current, time and force”. One challenge when dealing with irregularities in the production process is compiling all the relevant information about the component in a timely manner. By introducing built-in quality monitors,  all the relevant data is available at a glance, and it is possible to quickly identify problems that are caused directly in the welding process. 

Heron is pursuing an integrated system for comprehensive recording of real-time data. These concepts have been integrated into our products to improve quality monitoring and big data analysis, as seen with our newly launched HRC 650 (for Welding) and HRC 670 (for Riveting and Clinching). 

Heron’s Takeaway 

With technological advancements, welders and technicians will be equipped with an array of powerful tools and techniques that will enable them to work smarter, faster, and with greater precision than ever before. To set up the technical infrastructure, software engineers need to work closely with and train production staff and identify differences in weld settings in order to train the AI model. There are many advantages of adopting an integrated system for comprehensive data recording as opposed to collecting data from a separate weld checker or monitor. This is a worthy investment for the shift to Industry 4.0.