Monitoring and characterization of abnormal process conditions in resistance spot welding

Resistance spot welding (RSW) is extensively used for sheet metal joining of body-in-white (BIW) structure in the automobile industry. Key parameters, such as welding current, electrode force and welding time, are involved in the RSW process. Appropriate welding parameters are vital for producing good welds; otherwise, undersized weld and expulsion are likely to be caused. For a specific type of sheet metal, an acceptable nugget is produced when an appropriate combination of welding parameters is used. However, undersized welds and expulsion are still commonly seen in the plant environment, where some abnormal process conditions could account for the production of the poor quality welds. Understanding the influence of abnormal process conditions on spot weld quality and other RSW related issues is crucial. A range of online signals, strongly related to the nugget development history, have attracted keen interest from the research community. Recent monitoring systems established the applied dynamic resistance (DR) signal, and good prediction of nugget diameter was made based on signal values. However, the DR curves with abnormal process conditions did not agree well with those under normal condition, making them less useful in detecting abnormal process conditions. More importantly, none of the existing monitoring systems have taken these abnormal process conditions into account. In addition, electrode degradation is one of the most important issues in the plant environment. Two major electrode degradation mechanisms, softening and intermetallic compound (IMC) formation, are strongly related to the characteristics of welding parameters and sheet metals. Electrode misalignment creates a very distinct temperature history of the electrode tip face, and is believed to affect the electrode degradation mechanism. Though previous studies have shown that electrode misalignment can shorten electrode life, the detailed mechanism is still not understood. In this study, an online-monitoring system based on DR curve was first established via a random forest (RF) model. The samples included individual welds on the tensile shear test sample and welds on the same sheet, considering the airgap and shunting effect. It was found that the RF model achieved a high classification accuracy between good and poor welds. However, the DR signals were affected by the shunting distance, and they displayed opposite trends against individual welds made without any shunting effect. Furthermore, a suitable online signal, electrode displacement (ED), was proposed for monitoring abnormal process conditions such as shunting, air gap and close edged welds. Related to the thermal expansion of sheet metal, ED showed good consistency of profile features and actual nugget diameters between abnormal and normal welds. Next, the influence of electrode misalignment on electrode degradation of galvannealed steel was qualitatively and quantitatively investigated. A much-reduced electrode life was found under the angular misalignment of 5°. Pitting and electrode softening were accelerated on the misaligned electrodes. δ Fe-Zn phase from the galvannealed layer that extends electrodes was found non-uniformly distributed on the worn electrode. Furthermore, electron backscatter diffraction (EBSD) analysis was implemented on the worn electrode, showing marked reduction in grain diameter and aspect ratio. The grain deformation capacity was estimated by the distribution of the Taylor factor, where the portion of pore grain was substantially weakened in the recrystallized region compared to the base metal region

State of the Art of Laser Hardening and Cladding

In this paper an overview is given about laser surface modification processes, which are developed especially with the aim of hardness improvement for an enhanced fatigue and wear behaviour. The processes can be divided into such with and without filler material and in solid-state and melting processes. Actual work on shock hardening, transformation hardening, remelting, alloying and cladding is reviewed, where the main focus was on scientific work from the 21st century

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Technique for Measurement of Weld Resistance for AC Resistance Spot Welding via Instantaneous Phasor Measurement

The resistance measurement in the resistance spot welding (RSW), is an ongoing research topic. The high current flow during the welding process induces an electromagnetic field in the wires which are attached to the electrodes to measure tip voltage. This results an additional voltage drop which is proportional to the derivative of current. Also the presence of silicon controlled rectifier (SCR) in the welding power supply generates harmonics in both supply voltage and current. These issues together complicate the methods for resistance estimation. A set of simultaneous linear equations is derived for the on-line measurement of dynamic resistance and induced voltage constant by using the dynamic circuit analysis of weld setup. This can be solved to determine the weld resistance using instantaneous phasors measurements for the 1st, 3rd and 5th harmonics of current and measured voltage signals. The instantaneous phasor measurements for these desired harmonics are obtained by employing the following proposed method. In this thesis, a new method for the measurement of instantaneous phasor is proposed for the narrow band signals. The proposed algorithm is based on the internal model principle (IMP) defined for the cancellation of a sinusoidal disturbance signal. The IMP has two states, exhibiting the properties of being sinusoidal and orthogonal. The instantaneous values of IMP states are defined as real and imaginary components of a complex signal at each time instant. The instantaneous measurements of envelope and phase of a sinusoidal signal are determined from instantaneous values of complex signal by using arithmetic properties of complex numbers. In case of signal comprising of sum of sinusoids of different frequencies, the approach for obtaining instantaneous phasor for each sinusoidal component is presented by connecting multiple internal models in the parallel and open-loop configuration. The instantaneous phasor measurement of fundamental frequency signal is not only advantageous in detecting faults like short circuiting, harmonic distortion and frequency variations but it can also be applied to protect power system from these faults. In this work, the applicability of the proposed instantaneous phasor measurement algorithm is analyzed for scenarios of power disturbances due to the the harmonic distortion and decaying DC offset. The results are discussed and compared with few existing methods

Control and Power Supply for Resistance Spot Welding (RSW)

In the automobile industry, Resistance Spot Welding (RSW) is widely used for its low cost, high speed, simple mechanism and applicability for automation. RSW has become the predominant means of auto body assembly, resulting in two to six thousands spot welds performed on each manufactured car. In the North American automobile industry there are approximately 100 billion spot welds, which are done every year. RSW is the joining of two or more metal parts together in a localized area by resistive heating and pressure. Small Scale RSW (SSRSW) is commonly used for medical devices and electronic components, because the welded parts are thinner and smaller compared to common RSW applications, such as automotive applications. According to a study of Edison Welding Institute, 20% of the welding quality issues are the weld schedule or power supply related. Therefore, to contribute to weld quality improvement, the study of different weld schedules or power supplies and control schemes needs to be improved by doing further studies in this area. Thus a novel power supply, which can provide a testing bench for these studies, was designed and developed in 2005 by L. J. Brown and J. Lin. This research study will focus on studying and improving weld power supplies, weld schedules and control modes. One of the goals for this research is to improve the consistency of weld nugget size and strength by using different control parameters, which will be weighted geometric averages of voltage and current. These control parameters are fed back to a Proportional Integral Derivative (PID) controller that is designed to control the Direct Current (DC) power supply for the RSW to come up with the best control parameters that will improve the consistency of the RSW spot welds. Another goal for this research is it to further develop the existing DC power supply that was designed for SSRSW by L. J. Brown, to include tip voltage measurements, and Large Scale Resistance Spot Welding (LSRSW). This goal will lead to build additional weld modules to construct a 6000A welder in the future

Machine Learning Methods for Product Quality Monitoring in Electric Resistance Welding

Elektrisches Widerstandsschweißen (Englisch: Electric Resistance Welding, ERW) ist eine Gruppe von vollautomatisierten Fertigungsprozessen, bei denen metallische Werkstoffe durch Wärme verbunden werden, die von elektrischem Strom und Widerstand erzeugt wird. Eine genaue Qualitätsüberwachung von ERW kann oft nur teilweise mit destruktiven Methoden durchgeführt werden. Es besteht ein großes industrielles und wirtschaftliches Potenzial, datengetriebene Ansätze für die Qualitätsüberwachung in ERW zu entwickeln, um die Wartungskosten zu senken und die Qualitätskontrolle zu verbessern. Datengetriebene Ansätze wie maschinelles Lernen (ML) haben aufgrund der enormen Menge verfügbarer Daten, die von Technologien der Industrie 4.0 bereitgestellt werden, viel Aufmerksamkeit auf sich gezogen. Datengetriebene Ansätze ermöglichen eine zerstörungsfreie, umfassende und präzise Qualitätsüberwachung, wenn eine bestimmte Menge präziser Daten verfügbar ist. Dies kann eine umfassende Online-Qualitätsüberwachung ermöglichen, die ansonsten mit herkömmlichen empirischen Methoden äußerst schwierig ist. Es gibt jedoch noch viele Herausforderungen bei der Adoption solcher Ansätze in der Fertigungsindustrie. Zu diesen Herausforderungen gehören: effiziente Datensammlung, die dasWissen von erforderlichen Datenmengen und relevanten Sensoren für erfolgreiches maschinelles Lernen verlangt; das anspruchsvolle Verstehen von komplexen Prozessen und facettenreichen Daten; eine geschickte Selektion geeigneter ML-Methoden und die Integration von Domänenwissen für die prädiktive Qualitätsüberwachung mit inhomogenen Datenstrukturen, usw. Bestehende ML-Lösungen für ERW liefern keine systematische Vorgehensweise für die Methodenauswahl. Jeder Prozess der ML-Entwicklung erfordert ein umfassendes Prozess- und Datenverständnis und ist auf ein bestimmtes Szenario zugeschnitten, das schwer zu verallgemeinern ist. Es existieren semantische Lösungen für das Prozess- und Datenverständnis und Datenmanagement. Diese betrachten die Datenanalyse als eine isolierte Phase. Sie liefern keine Systemlösungen für das Prozess- und Datenverständnis, die Datenaufbereitung und die ML-Verbesserung, die konfigurierbare und verallgemeinerbare Lösungen für maschinelles Lernen ermöglichen. Diese Arbeit versucht, die obengenannten Herausforderungen zu adressieren, indem ein Framework für maschinelles Lernen für ERW vorgeschlagen wird, und demonstriert fünf industrielle Anwendungsfälle, die das Framework anwenden und validieren. Das Framework überprüft die Fragen und Datenspezifitäten, schlägt eine simulationsunterstützte Datenerfassung vor und erörtert Methoden des maschinellen Lernens, die in zwei Gruppen unterteilt sind: Feature Engineering und Feature Learning. Das Framework basiert auf semantischen Technologien, die eine standardisierte Prozess- und Datenbeschreibung, eine Ontologie-bewusste Datenaufbereitung sowie halbautomatisierte und Nutzer-konfigurierbare ML-Lösungen ermöglichen. Diese Arbeit demonstriert außerdem die Übertragbarkeit des Frameworks auf einen hochpräzisen Laserprozess. Diese Arbeit ist ein Beginn des Wegs zur intelligenten Fertigung von ERW, der mit dem Trend der vierten industriellen Revolution korrespondiert

MHD-RLC discharge model and the efficiency characteristics of plasma synthetic jet actuator

Major factors affecting efficiency of plasma synthetic jet actuator (PSJA) are analyzed based on a new discharge model in the present paper. The model couples the magnetohydrodynamics (MHD) equations with the resistor–inductor–capacitor (RLC) equations, and is able to resolve the time-dependent voltage fall on the sheath region and arc region, which is critical in analyzing energy loss in the heating process. This model is integrated into the commercial CFD software by a two-equation method. Results show that in a typical capacitive discharge at microsecond scale, the maximum energy loss is the sheath energy loss, which accounts for nearly half of the discharge energy, while the radiation loss is less than 5%. The discharge time is an important parameter for the PSJA efficiency. A short discharge time less than 1 μs will effectively reduce the sheath energy loss, while a longer discharge time will decrease the thermodynamic efficiency

Production-oriented design of electric traction drives with hairpin winding

In recent years, the manufacturing of stators by hairpin technology has proven its ability to fulfill the requirements on quality, productivity and robustness of traction drive applications in automotive industry. However, the uncertainty and necessity of rapid product development despite fuzzy target systems still cause that processes, machines and equipment – as well as the electric design – are often in an imperfect prototype stage at the start of production ramp-up. Due to the complex interdependencies between the stator components in combination with a high sensitivity of the overall process reliability to minor adjustments of stator design features, possible production-related weaknesses in design are often recognized first in the prototype stage of the production system. In order to reduce the economic risk resulting from these volatile technological conditions, production-oriented design based on numerical simulation methods can be applied from the beginning of product development. Therefore, several techniques for numerical process modeling are presented in this paper as possibilities to consider manufacturing constraints in an early stage of product development. For this purpose, the influence of wire dimensions on the forming process of hairpin coils is investigated using the example of rotary bending as well as the twisting process of a full stator by finite element simulations. Furthermore, a numerical approach to investigate the influence of heat input during laser welding of hairpin coils on the required stripping length is introduced