Profile Monitoring and Fault Diagnosis via Sensor Fusion for Ultrasonic Welding

2016 ◽  
Author(s):  
Weihong Guo ◽  
Jionghua (Judy) Jin ◽  
S. Jack Hu
Keyword(s):  
Intelligent System ◽  
Real Data ◽  
Profile Data ◽  
Ultrasonic Metal Welding ◽  
Operational Quality ◽  
Vector Projection ◽  
Sensor Signals ◽  
Rich Information ◽  
Metal Welding ◽  
And Control

Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, quick diagnosis of fault root causes, and intelligent system design and control. This paper develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The developed method is demonstrated with both simulated and real data from ultrasonic metal welding.

scholarly journals Profile Monitoring and Fault Diagnosis Via Sensor Fusion for Ultrasonic Welding

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Weihong (Grace) Guo ◽  
Jionghua (Judy) Jin ◽  
S. Jack Hu
Keyword(s):  
Real Data ◽  
Ultrasonic Welding ◽  
Profile Data ◽  
Ultrasonic Metal Welding ◽  
Monitoring And Diagnosis ◽  
Operational Quality ◽  
Vector Projection ◽  
Sensor Signals ◽  
Rich Information ◽  
Metal Welding

Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, and quick diagnosis of fault root causes. This paper develops a method for effective monitoring and diagnosis of multisensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multistream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus, preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The developed method is demonstrated with both simulated and real data from ultrasonic metal welding.

scholarly journals 3D finite element model of dynamic material behaviors for multilayer ultrasonic metal welding

2021 ◽  
Vol 62 ◽  
pp. 302-312
Author(s):  
Ninggang Shen ◽  
Avik Samanta ◽  
Wayne W. Cai ◽  
Teresa Rinker ◽  
Blair Carlson ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
3D Finite Element ◽  
Ultrasonic Metal Welding ◽  
3D Finite Element Model ◽  
Material Behaviors ◽  
Metal Welding

Using a Response Time–Based Expected A Posteriori Estimator to Control for Differential Speededness in Computerized Adaptive Test

2021 ◽  
pp. 014662162110146
Author(s):  
Justin L. Kern ◽  
Edison Choe
Keyword(s):  
Response Times ◽  
Real Data ◽  
Joint Estimation ◽  
Item Selection ◽  
A Posteriori ◽  
Computerized Adaptive Test ◽  
Maximum Information ◽  
Adaptive Test ◽  
Estimation And Control ◽  
And Control

This study investigates using response times (RTs) with item responses in a computerized adaptive test (CAT) setting to enhance item selection and ability estimation and control for differential speededness. Using van der Linden’s hierarchical framework, an extended procedure for joint estimation of ability and speed parameters for use in CAT is developed following van der Linden; this is called the joint expected a posteriori estimator (J-EAP). It is shown that the J-EAP estimate of ability and speededness outperforms the standard maximum likelihood estimator (MLE) of ability and speededness in terms of correlation, root mean square error, and bias. It is further shown that under the maximum information per time unit item selection method (MICT)—a method which uses estimates for ability and speededness directly—using the J-EAP further reduces average examinee time spent and variability in test times between examinees above the resulting gains of this selection algorithm with the MLE while maintaining estimation efficiency. Simulated test results are further corroborated with test parameters derived from a real data example.

scholarly journals A Distributed and Collaborative Intelligent System for Medical Diagnosis

2013 ◽  
Vol 2 (2) ◽  
pp. 1-16
Author(s):  
Naoufel Khayati ◽  
Wided Lejouad-Chaari
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Intelligent System ◽  
Distributed Environment ◽  
Web Based ◽  
Knowledge Based ◽  
Technological Advances ◽  
And Control ◽  
Control Complex

In this paper, we present a distributed collaborative system assisting physicians in diagnosis when processing medical images. This is a Web-based solution since the different participants and resources are on various sites. It is collaborative because these participants (physicians, radiologists, knowledgebasesdesigners, program developers for medical image processing, etc.) can work collaboratively to enhance the quality of programs and then the quality of the diagnosis results. It is intelligent since it is a knowledge-based system including, but not only, a knowledge base, an inference engine said supervision engine and ontologies. The current work deals with the osteoporosis detection in bone radiographies. We rely on program supervision techniques that aim to automatically plan and control complex software usage. Our main contribution is to allow physicians, who are not experts in computing, to benefit from technological advances made by experts in image processing, and then to efficiently use various osteoporosis detection programs in a distributed environment.

scholarly journals Combining Capability Indices and Control Charts in the Process and Analytical Method Control Strategy

2020 ◽  
Author(s):  
Alexis Oliva ◽  
Matías Llabrés
Keyword(s):  
Control Strategy ◽  
Analytical Method ◽  
Control Charts ◽  
Process Capability ◽  
Real Data ◽  
Process Capability Indices ◽  
Specification Limits ◽  
Capability Indices ◽  
And Control ◽  
Control Limits

Different control charts in combination with the process capability indices, Cp, Cpm and Cpk, as part of the control strategy, were evaluated, since both are key elements in determining whether the method or process is reliable for its purpose. All these aspects were analyzed using real data from unitary processes and analytical methods. The traditional x-chart and moving range chart confirmed both analytical method and process are in control and stable and therefore, the process capability indices can be computed. We applied different criteria to establish the specification limits (i.e., analyst/customer requirements) for fixed method or process performance (i.e., process or method requirements). The unitary process does not satisfy the minimum capability requirements for Cp and Cpk indices when the specification limit and control limits are equal in breath. Therefore, the process needs to be revised; especially, a greater control in the process variation is necessary. For the analytical method, the Cpm and Cpk indices were computed. The obtained results were similar in both cases. For example, if the specification limits are set at ±3% of the target value, the method is considered “satisfactory” (1.22<Cpm<1.50) and no further stringent precision control is required.

Process Monitoring in Ultrasonic Metal Welding of Lithium Batteries by Power Signals

2021 ◽  
pp. 1-22
Author(s):  
Xinhua Shi ◽  
Lin Li ◽  
Suiran Yu ◽  
Lingxiang Yun
Keyword(s):  
Electric Power ◽  
Process Monitoring ◽  
Lithium Batteries ◽  
Welding Process ◽  
Ultrasonic Metal Welding ◽  
Isometric Transformation ◽  
Welding Defects ◽  
Different Types ◽  
Metal Welding ◽  
Welding Processes

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.

Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries

2015 ◽  
Author(s):  
Chenhui Shao ◽  
Tae Hyung Kim ◽  
S. Jack Hu ◽  
Jionghua (Judy) Jin ◽  
Jeffrey A. Abell ◽  
...  
Keyword(s):  
Tool Wear ◽  
Lithium Ion ◽  
Production Costs ◽  
Tool Wear Monitoring ◽  
Surface Geometry ◽  
Ultrasonic Metal Welding ◽  
Tool Condition ◽  
Wear Monitoring ◽  
Tool Replacement ◽  
Metal Welding

This paper presents a tool wear monitoring framework for ultrasonic metal welding which has been used for lithium-ion battery manufacturing. Tool wear has a significant impact on joining quality. In addition, tool replacement, including horns and anvils, constitutes an important part of production costs. Therefore, a tool condition monitoring (TCM) system is highly desirable for ultrasonic metal welding. However, it is very challenging to develop a TCM system due to the complexity of tool surface geometry and a lack of thorough understanding on the wear mechanism. Here, we first characterize tool wear progression by comparing surface measurements obtained at different stages of tool wear, and then develop a tool condition classification algorithm to identify the state of wear. The developed algorithm is validated using tool measurement data from a battery plant.

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