Комаров Артём о мониторинге процесса контактной сварки (eng)
Комаров Артём о мониторинге процесса контактной сварки (eng)

Комаров Артём о мониторинге процесса контактной сварки (eng)

Artem Komarov noted that process consistency is a common problem in resistance welding. A few problems can go wrong, all caused by differences in one or more of these areas: equipment performance, material properties, and process settings.

To find the culprit, start by recording the welding process parameters using the Weld Monitor and examining the results. An experienced, well-trained operator can detect welding problems and make educated guesses about their causes, but without data from a welding monitor, this operator is largely blind, Artem Komarov said.

— Which process parameters can be measured with the welding monitor?

The resistance welding monitor can measure the electric current during welding, the voltage between the electrodes, the force on the electrode, and the movement of the electrode.

Base models output a numeric aggregated value (minimum or maximum) for one or more parameters. More advanced monitors can capture and analyze the entire waveform at high resolution for each parameter. Signals provide much more useful information about the dynamic welding process than aggregated values. For example, the signals may indicate sparking caused by misfitting, loose welder hardware, and inconsistent timing of mechanical force application and power delivery.

— What do these parameters say about the weld?

Resistance welding is achieved by applying heat and pressure over time. Heating is provided by passing current through the electrodes and the welded part. Pressure is generated by compressing the weld between two electrodes with a given force. The time profile, or the rate at which current and pressure are applied, can affect the result of a weld.

Temperature and pressure cannot be measured directly. But they are a consequence of the current passing through the parts, the voltage on the electrodes and the force applied by the welding head. Other electrical parameters such as power and resistance can be derived from these measurements. In addition, the welding process causes a slight movement of the electrodes, which can indicate whether the weld went as expected.

A change in any of these parameters can indicate problems in the welding process, such as misalignment, dirt and debris, material, and coating changes, Komarov Artem explained.

— Can the weld monitor identify a good or bad weld?

The monitor, with its result values and waveforms, does not indicate whether the weld is good or bad. However, he can compare the most recent weld with a known good weld. If the parameters are similar, they are passed as good; if they are out of range, they are marked as different. In manufacturing environments, it is often assumed that «otherwise» welding is poor, and the part will be marked for rework or scrapped.

Мониторинг контактной сварки, Комаров Артём

To determine the range of allowable values or limits, the user will conduct a series of experiments using various welding equipment settings that will affect the quality of the weld. Signals are recorded and analyzed with welding quality results. At the end of the study, the upper and lower limits of the parameters can be set so that the range includes good welds and excludes bad welds. When setting the optimal limits, one should avoid too many false positives, that is, welds whose readings are within acceptable values, but do not meet the requirements for welding quality. Finding the ideal parameter limits is time-consuming, but results in process settings that allow a good weld to be identified within the statistical limits. As far as critical welds are concerned, this is the actual procedure to be followed for items such as protective components or medical devices.

— What else can welding monitors be used for?

In addition to quality assurance, welding monitors are used to:

— Weld monitor screen display.

— Determining the correct settings during process development. The stability of the settings can be compared to optimize the process.

— Troubleshooting maintenance. The waveform recorded by the welding monitor can indicate problems with material properties and changes in process settings and alert users to equipment malfunctions.

— Equipment certification. The Weld Monitor can check that the equipment is working properly. If you have programmed welding with 2000 amps for 10 ms, you must be sure that the power source is supplying the correct power.

— Data storage. When the Weld Monitor is connected to a production control system, the weld values collected by it for a particular manufactured part can be saved.

— Where is weld monitor data stored and how can it be used?

The data can be stored locally in the process monitor or on a network server, locally or in the cloud. Modern high-quality monitors are equipped with software that stores the recorded values on a database server. This database can be accessed and used with analytical statistical process control (SPC) software and overall equipment efficiency (OEE) programs.

Production managers, process engineers and operators can use the stored data to make informed decisions about the manufacturing process that affect product quality, productivity, and cost.

— Can this data be used for artificial intelligence or machine learning algorithms?

Indeed, it is possible. Artificial intelligence (AI) and machine learning (ML) are applied in the field of welding to create reasonable adaptive limits and predict welding quality.

AI/ML algorithms track trends in recorded data that might not be visible with standard chart analysis. In addition, AI/ML tools can generate many data parameters based on process signals that can be used to determine the fine details of the welding process. While the sheer volume of data can overwhelm a wide variety of SPC software tools, the data can be easily handled with modern ML algorithms running on inexpensive computers.

AI/ML programs go beyond simple momentary limitations. They change the limits during the welding process, considering external fluctuations that occur naturally over time, such as ambient temperature and electrode changes. These smart adaptive limits provide better performance and better quality.

Maybe AI/ML can finally answer the question, “Was that last weld good or bad?” and provide tools for predicting the quality of welding, Artem Komarov said.