Adaptive SVM-based real-time quality assessment for primer-sealer dispensing process of sunroof assembly line
Introduction
Recent manufacturing companies have faced challenges to manage the manufacturing complexity due to an increasing requirement of product variety from the customers [1]. Thus, the automated system is an alternative to enhance productivity and improve cost efficiency in the global, complex competition in many firms. In addition, with an advance in sensor technologies and computer capabilities, the real-time, data-driven tools have been adapted to examine products’ quality in many industrial cases [2]. Several industries adopt sensor and camera technologies in their process lines to improve safety conditions for operators and making the process more reliable [2], [3], [4], [5], [6]. An aid of real-time data acquisition also enables an application of machine learning algorithms to accurately assess quality of manufacturing process. Based on the concept of supervised learning, the machine learning techniques are operated by building a model from a training set of input observations in order to make data-driven predictions or decisions expressed as outputs [7], [8], [9]. Some of these algorithms are artificial neural network, support vector machine (SVM), and Bayesian statistic.
Machine learning techniques have been lately implemented in the quality inspection to predict and reduce errors in the classification results based on a variety of data analysis methods (e.g., see [10], [11]). The SVM algorithm, in particular, has been shown to be an effective computational classification method widely used in many engineering applications (e.g., [11], [12]). Researchers have also proposed to improve the SVM algorithm through an analysis of optimal parameters for ensuring the applicability of the method (e.g., [13], [14]). Although previous studies with regard to SVM applications have been proposed in industries, an implementation in a real manufacturing study with long-term production data is scarcely reported due partly to the sensitivity of manufacturing and operational issues. A feedback (adaptive and self-updating) process with a large amount of historical quality data is important to ensure the effectiveness of the SVM algorithms over time, which enables the real-time improvement of SVM-based quality inspection [15], [16].
In this study, we propose the adaptive SVM (ASVM) with quality manager's judgement. The real-time quality assessment using ASVM is implemented in a production line on a daily basis so that the proposed algorithm will systematically repeat the SVM-training loop when the system judgement of the manufacturing process is found to be inconsistent with human/expert judgement. This closed-loop process enhances the system's accuracy over time and ensures reliability in the long run. The proposed system is then verified and validated by evaluating the quality of the primer-sealer dispensing process of an automotive assembly line. The primer-sealer dispensing process is a process that a frame and a glass are attached though the heating process in an automotive company. Thus, an analysis of the thermal image data based on the infrared thermography (IRT) is needed and further conducted to assess the quality information from the heating process that cannot be easily assessed by a human operator. The thermal image data is collected, pre-processed, and analyzed using image processing algorithms in our study. In particular, the key novelty of this study is emphasized in three folds: Frist, we pre-process and integrate the IRT data for machine learning-based analysis using ASVM to assess the quality information related to temperature profile of the automotive manufacturing process. Second, the adaptive process in the ASVM-based quality classification is an important part of the proposed system found to effectively enhance the system's accuracy and reliability over time. Third, the actual implementation is illustrated, in which the part quality is classified as either defective or good to be displayed real time on the kiosk personal computer (PC), and is adopted as a part of the company quality inspection system.
The remaining sections of this paper are organized as follows. We introduce the relevant studies in Section 2. Next, we discuss the real-time quality assessment using the ASVM algorithm in Section 3. Then, the case study in an automotive industry using primer-sealer dispensing process and thermal image data analysis is illustrated in Section 4. Finally, we present our research conclusions and outline future research directions in Section 5.
Section snippets
Literature review
Machine learning and computational statistics (e.g., statistical process control (SPC) are closely related fields from methodological principles to theoretical tools, in which the former evolves from the study of pattern recognition and computational learning theory in artificial intelligence and expert system. In particular, machine learning approach explores the construction of algorithms that can learn from predictions on data by building a model from a training set of input observations to
Problem statement
Generally, product quality has been maintained by skilled operators on the basis of their experience and intuition [41]. However, not only are the manual inspection system slow, but also ineffective. In fact, some research has stated that human inspectors typically find about 80% of the defects [42]. Meanwhile, with the advances in the sensing technology, several manufacturers have shifted from manual to automated quality inspection. Ironically, good number of studies argues that fully
Case study and application
This section discusses the ASVM-based quality assessment using a real case study data from the primer-sealer dispensing process. We first introduce the sunroof assembly process for an automotive requiring the primer-sealer dispensing process. Next, we discuss image data preparation. Then, we compare our ASVM-based results with the results obtained from an expert judgment alone. Finally, we discuss the key findings from the case study.
Conclusions and future research
We propose the ASVM-based quality assessment using thermal image data in the primer-sealer dispensing process in this study. The proposed quality assessment system incorporating human/expert judgement is implemented and applied to enhance the system accuracy over time in the long run. The proposed system consists of the following key operations. First, a number of thermal cameras with their respective locations and timing were determined based on the product and process being examined. Then,
Acknowledgments
This research was supported by the 5th regional S/W convergence business though the Ulsan Business Support Center and the National IT Industry Promotion Agency (NIPA) funded by The Ministry of Science, ICT, Future Planning (MSIP), and Ulsan metropolitan city, Republic of Korea. The authors would like to also thank the three anonymous reviewers for comments, which greatly improve the quality of the manuscript and its presentation.
References (57)
- et al.
Automatic defect identification using thermal image analysis for online weld quality monitoring
J Mater Process Technol
(2012) - et al.
Infrared thermography for condition monitoring – a review
Infrared Phys Technol
(2013) An application of one-class support vector machines in content-based image retrieval
Expert Syst Appl
(2007)- et al.
Automated diagnosis of sewer pipe defects based on machine learning approaches
Expert Syst Appl
(2008) - et al.
Active incremental support vector machine for oil and gas pipeline defects prediction system using long range ultrasonic transducers
Ultrasonics
(2014) - et al.
Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs)
Int J Mach Tools Manuf
(2001) - et al.
Fault diagnosis using support vector machine with an application in sheet metal stamping operations
Mech Syst Sig Process
(2004) - et al.
Support vector machine in machine condition monitoring and fault diagnosis
Mech Syst Signal Process
(2007) - et al.
Evaluation of simple performance measures for tuning SVM hyperparameters
Neurocomputing
(2003) - et al.
Multi-objective optimization analysis for part-to-Printer assignment in a network of 3D fused deposition modeling
J Manuf Syst
(2017)
Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble
Expert Syst Appl
A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM
Expert Syst Appl
Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors
Expert Syst Appl
Method for online quality monitoring of AWJ cutting by infrared thermography
CIRP J Manuf Sci Technol
Data-based process monitoring, process control, and quality improvement: recent developments and applications in steel industry
Comput Chem Eng
Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences
Atmos Environ
Computational modelling of manufacturing choice complexity in a mixed-model assembly line
Int J Prod Res
Recent advances in the use of infrared thermography
Meas Sci Technol
Monitoring of the friction stir welding process by means of thermography
Nondestr Test Eval
Pattern recognition and machine learning (Information science and statistics)
The changing science of machine learning
Mach Learn
Introduction to machine learning
Failure and reliability prediction by support vector machines regression of time series data
Reliab Eng Syst Saf
Design of online monitoring and fault diagnosis system for belt conveyors based on wavelet packet decomposition and support vector machine
Adv Mech Eng
Fault diagnosis of rotating machine by thermography method on support vector machine
J Mech Sci Technol
A survey of active learning algorithms for supervised remote sensing image classification
IEEE J Sel Top Signal Process
Data mining in manufacturing: a review
J Manuf Sci Eng
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