**6.3 On line defects detection in Gas Metal Arc Welding**

Another fuzzy application used in industrial field is proposed by Naso & Turchiano (Naso & Turchiano, 2005), who propose the development of an intelligent optical sensor for on line defects detection in Gas Metal Arc Welding (GMAW).

GMAW (Li & Zhang, 2001) is a welding process widely used in industrial field (Bingul et al., 2000) which presents many advantages, such as low costs, high metal deposition rate and suitability to automation. Also the process monitoring and defect-detections methods are very important tasks in order to improve the weld quality reducing manufacturing costs.

The electro-optical sensor includes two main modules (sensor and telescope) which are interconnected with optical fibers. This equipment aims at filtering and splitting the measured radiation into four components: infrared (*IR*), ultraviolet (*UV*) and two radiations at visible wavelengths (*VIS1, VIS2*). Photodiodes convert the resulting beams in electrical signals. The wavelength of the two signals belonging to the visible spectrum is set to tolerate the computation of the electronic temperature (*Te*) of the plasma. If *VIS1, VIS2, Te* are interdependent, then only *Te* and *VIS1* are taken into account to eliminate redundancies.

Before the on line classification operation, a signal pre-processing phase is necessary in order to improve the signal quality. The pre-processing phase includes two stages: signal filtering and extraction of the regularity indices. Signal filtering is a fundamental step in this context because a large amount of noise affects the observed signals. The Kalman filter (Brown & Hwang, 1992) has been adopted to this purpose, which provides efficient algorithms for estimating useful parameters in the stochastic environments. Regularity indices are extracted considering several factors: first of all, given a fixed configuration of the welding equipment, the signals associated to successful welding processes have the same behaviour; in contrast, signals observed during defective welds contain particularly features which are easily associable to the occurred defect. Also, it is possible to discover when the quality of the weld decreases, the cause of such downgrading and the type of defect. For the classification task the information describing the behaviour of the observed signal can be synthesized in three independent features:


The extracted features are fed as inputs into a classifier. In order to develop a classifier that directly exploits the experts knowledge a fuzzy system has been chosen. It must be noticed that signals belonging to different welds are different due to the stochastic nature of the phenomena under consideration; also the deviation of one or more indices from their expected behaviour often is due to the occurrence of a defect during the welding process. Fuzzy system, in this context, is the ideal classifier, as it is simple, it can directly use the knowledge of the experts and it can be easily reconfigured when new knowledge is available.

In order to limit the number of membership functions and rules the classification task is developed through two different fuzzy systems operating parallel. The first fuzzy classification system is used to provide a percentile index of acceptability of the weld, while the second fuzzy system detects the simultaneous signal patters directly connected with a specific defect. The first FIS gives a real time estimate of the quality of each weld, also for each time-window the system analyzes the indices through a rule-based method. Firstly a partition of the range of each observed input three fuzzy set is been made basing on the set of reference welds used as training set. The three introduced fuzzy sets are referred to quality: Optimal (OPT), acceptable (ACC) and unacceptable (UNA) and the membership functions have a trapezoidal shape. Finally the fuzzy system uses the welding time (*Time*) as an input to let the classification ignore the first seconds of process when the welding equipment is warming up. To describe the time interval a single linear piecewise increasing membership function regime (*REG*) is introduced. The output is represented by three membership functions as well. In this case the membership function are represented by three singletons as follows:


256 Fuzzy Inference System – Theory and Applications

Another fuzzy application used in industrial field is proposed by Naso & Turchiano (Naso & Turchiano, 2005), who propose the development of an intelligent optical sensor for on line

GMAW (Li & Zhang, 2001) is a welding process widely used in industrial field (Bingul et al., 2000) which presents many advantages, such as low costs, high metal deposition rate and suitability to automation. Also the process monitoring and defect-detections methods are very important tasks in order to improve the weld quality reducing manufacturing costs.

The electro-optical sensor includes two main modules (sensor and telescope) which are interconnected with optical fibers. This equipment aims at filtering and splitting the measured radiation into four components: infrared (*IR*), ultraviolet (*UV*) and two radiations at visible wavelengths (*VIS1, VIS2*). Photodiodes convert the resulting beams in electrical signals. The wavelength of the two signals belonging to the visible spectrum is set to tolerate the computation of the electronic temperature (*Te*) of the plasma. If *VIS1, VIS2, Te* are interdependent, then only *Te* and *VIS1* are taken into account to eliminate redundancies.

Before the on line classification operation, a signal pre-processing phase is necessary in order to improve the signal quality. The pre-processing phase includes two stages: signal filtering and extraction of the regularity indices. Signal filtering is a fundamental step in this context because a large amount of noise affects the observed signals. The Kalman filter (Brown & Hwang, 1992) has been adopted to this purpose, which provides efficient algorithms for estimating useful parameters in the stochastic environments. Regularity indices are extracted considering several factors: first of all, given a fixed configuration of the welding equipment, the signals associated to successful welding processes have the same behaviour; in contrast, signals observed during defective welds contain particularly features which are easily associable to the occurred defect. Also, it is possible to discover when the quality of the weld decreases, the cause of such downgrading and the type of defect. For the classification task the information describing the behaviour of the observed

1. *Normalized Signal Offset* (NSO), which is used to quantify the deviation of the signal

2. *Change of Normalized Signal Offset* (CNSO), which measures the change in signal levels

3. *Residual Signal Noise* (RSN), which represents the remain noise in the signal after the

The extracted features are fed as inputs into a classifier. In order to develop a classifier that directly exploits the experts knowledge a fuzzy system has been chosen. It must be noticed that signals belonging to different welds are different due to the stochastic nature of the phenomena under consideration; also the deviation of one or more indices from their expected behaviour often is due to the occurrence of a defect during the welding process. Fuzzy system, in this context, is the ideal classifier, as it is simple, it can directly use the knowledge of the experts and it can be easily reconfigured when new knowledge

**6.3 On line defects detection in Gas Metal Arc Welding** 

defects detection in Gas Metal Arc Welding (GMAW).

signal can be synthesized in three independent features:

between two consecutive time windows.

Kalman filtering.

is available.

from the expected value belonging to an ideal weld.


Once the membership functions have been defined, a few generic rules are introduced. The first rules refer to obvious conditions; then, each time, another rule is included and the overall classification performance is evaluated in order to adjust membership function and rule weights. The output is so defined as an index of weld quality, in particular 0% (UNA) indicates the occurrence of defect while 100% (OPT) represents an optimal weld, values in the range 0-100% represent intermediate acceptability. Subsequently a threshold of acceptability is introduced in order to convert the fuzzy degree of quality in a binary decision: good or defective weld.

The fuzzy rules used by the quality estimation system can be described as follow:


Fuzzy Inference Systems Applied to Image Classification in the Industrial Field 259

Both Fuzzy Inference Systems are Mandani type and a general scheme of the proposed

The proposed approach has been evaluated with 40 different welding processes, where the 70% of processes are non defective while the remaining 30% present particular defects voluntarily induced or *a posteriori* detected with an appropriate tool. Furthermore, 60% of

In order to demonstrate the effectiveness of the proposed method a comparison with a stochastic approach (Sforza & DeBlasiis, 2002) is provided. It is important consider that stochastic approach is not able to indicate the type of defect and, in order to make the comparison, the fuzzy index of quality must be convert in a binary value, also a threshold is necessary. The obtained results show that the fuzzy classification system correctly classifies

Finally a sensitivity analysis in order to evaluate the robustness of the proposed approach, when membership function, rules and operating condition vary, is carried out. The sensitivity investigation on the several variation of parameters leads to the following conclusion: if a proper pre-processing signal phase and a correct identification of the important features are been carried out, then the proposed fuzzy classification system can be

An interesting industrial application which exploits the advantages of the fuzzy theory is due to (Tong et al., 2003). The authors propose a process control chart which integrate both fuzzy theory and engineering experience in order to monitor the defects on a wafer which

The wafer manufacturing process contains many step, such as alignment, etch and deposition. It is a very complex process; the occurrence of defects on the wafer surface is

Typically Integrated Circuits (IC) manufacturers use c-charts to monitor wafer defects. This technique assumes that wafer defects are randomly and independently distributed so that the number of defects has a Poisson distribution. A limit of this approach is that the real defect clustering infringes this constraint creating a non acceptable occurrences of false alarms. A modified c-chart, introduced in order to solve this problem, is presented by Albin & Friedman (Albin & Friedman, 1991) and it is based on a Neyman Type-A distribution. Unfortunately also this approach presents a considerable limit, as it can monitor only the variation in the number of defects but it is not able to detect variation located within the wafer. The authors demonstrate that applying fuzzy theory in combination with engineering experience it is possible to build a process control chart which is able to monitor the clustered defect and defect clustering simultaneously. The proposed algorithm is

The KLA 2110 wafer inspection system (Castucci et al., 1991) is adopted to obtain the wafer map. This system provides in-line wafer inspection information such as number of defects, size of defects, placement of defects and, finally, type of defects. The number of defects are determined and the cluster index is calculated. Some cluster indices are provide to calculate

all considered welds while the stochastic approach misclassifies 14% of the welds.

approach is shown in Fig.6.

effectively built and tuned.

have been clustered.

illustrated in Fig.7.

**6.4 Detection of wafer defects** 

unavoidable and decreases the wafer yield.

data are used as training set and 40% as validation set.


The second fuzzy system is used to display messages which explain the occurred defect or the anomaly operation in the considered weld. The considered events for this aim are the common ones such as current increase, current decrease, voltage variation, gases assistance decrease, contamination of with materials having different thermal properties and occurrence of hole in the metal. The second FIS is similar to the first one working with analogous membership functions and rules and provides six outputs, one for each considered defect. It is evident that a single defect could be associated to one or more causes.

Fig. 6. Diagram of the proposed approach

Both Fuzzy Inference Systems are Mandani type and a general scheme of the proposed approach is shown in Fig.6.

The proposed approach has been evaluated with 40 different welding processes, where the 70% of processes are non defective while the remaining 30% present particular defects voluntarily induced or *a posteriori* detected with an appropriate tool. Furthermore, 60% of data are used as training set and 40% as validation set.

In order to demonstrate the effectiveness of the proposed method a comparison with a stochastic approach (Sforza & DeBlasiis, 2002) is provided. It is important consider that stochastic approach is not able to indicate the type of defect and, in order to make the comparison, the fuzzy index of quality must be convert in a binary value, also a threshold is necessary. The obtained results show that the fuzzy classification system correctly classifies all considered welds while the stochastic approach misclassifies 14% of the welds.

Finally a sensitivity analysis in order to evaluate the robustness of the proposed approach, when membership function, rules and operating condition vary, is carried out. The sensitivity investigation on the several variation of parameters leads to the following conclusion: if a proper pre-processing signal phase and a correct identification of the important features are been carried out, then the proposed fuzzy classification system can be effectively built and tuned.
