Explainability of Quality Issues in Manufacturing: a Semantic Based Approach

Abstract

This paper presents an approach using stream reasoning for detecting manufacturing quality losses. To semantically detect quality issues situations, an ontology-based context for manufacturing is introduced. Moreover, as heterogeneous data streams have to be integrated, a combination of existing models using stream processing and ofline reasoning can be used. This combination allows continuous processing of data and the use of expert knowledge to detect anomalies and provide explanations to operators and stakeholders. An illustrative case study about quality assurance succeeded in detecting anomalies and proposing an explanation.

Keywords

• eol>Quality Assurance and Industry 4
• 0 (Quality 4
• 0)
• Ontology
• Explainability
• Quality issues detection

1. Introduction

2. Quality issues detection and explanation

Quality assurance in manufacturing companies is an essential process for ensuring that products meet standards. It contributes to customer satisfaction and reduces the costs of defects. To tackle quality issues, we propose to adapt the approach presented in [ 3 ]. The idea is to detect situations that may lead to quality losses by observing products and tracking abnormal sensor values. Expert knowledge will allow the identification and assessment of the root cause that led to a detected abnormal situation.

The framework proposed in Figure 1 is based on [ 3 ]. Three modules are used to detect and explain quality issue situations reasoning over an ontology: Translation, Temporal Relations and Cause Determination. This reasoning is performed in real time (stream reasoning) or ofline (classical reasoning over an ontology).

To detect quality issues, an ontology based on the Context Ontology described in [ 4 ] is used. This ontology is composed of three core ontologies (Sensor Ontology, Time Ontology, Location Ontology and Situation Ontology) and three domain ontologies (Resource Ontology and Process Ontology). Therefore, we propose to extend the Situation Ontology with a Quality Assurance Ontology. As the Situation Ontology concerns only situations on machines, we want to extend it with quality issues situation detection on products. The Quality Assurance Ontology is based on TOVE Traceability Ontology [ 5 ] which provides representation to identify and trace a quality problem. As TOVE is a core ontology, we mainly use the idea of traceability between products and activities. This allows to link products to the machine that produced or modified them when and where the default occurred.

The Translation module is responsible for collecting sensor data and converting it to RDF streams thanks to Stream Generators. This component performs a semantic enrichment of raw data using the concepts and relations defined in the ontology. One Stream Generator is used per sensor.

The Temporal Relations module is used to explore the data. RDF streams are continuously queried into the Stream Reasoner using the ontology to contextualize the streams. The queries represent diferent quality issue situations to detect. For this component, we used RSP4J [ 6 ] to query the streaming data. The data from the detected situations is then formatted and put into the ontology.

The Cause Determination module identifies the root cause and explains the problem. The Reasoner is used over the ontology to check the consistency of the ontology and then, infer new knowledge. The cause of the quality problem can then be determined using diferent classifiers as proposed in [ 7 ].

3. Illustrative case study

In this section, we present an illustrative case study created to test our framework. We consider a manufacturing production line named PL1 composed of two machines M1 and M2 which produce products named P1, P2 and P3. The production line is equipped with sensors that observe machine and product properties. The sensors collect data on properties in Table 1. Constraints ranging from 1 to 8 relate to machines and the other ones relate to products.

Set of constraints C ID Properties Restriction Device ID Properties Restriction Device 1 Motor temp. > 500°C M1mt1 7 Roller speed > 600 mpm M2R1 2 Punching speed > 2000 times/min M1p1 8 Cooling temp. > 25°C M2T1 3 Punching pressure > 8 MPa M1p1 9 Porosity > 10 pu Pr 4 Environment temp. < 25°C PL1 10 Porosity > 40 pu Pr 5 Roller temp. > 1300°C M2R1 11 Roughness > 0.3 m Pr 6 Roller pressure > 450 bar M2R1 12 Flatness > 27 I-Units Pr

Set of quality situations Sit. Constraint(T) Description 1 4 P1 water spot 2 12, 2, 3 P1 punching defects 3 11, 12, 2, 3 P1 punching defects 3 9, 4, 5, 6 P2 uneven deformations 4 9, 10, 4, 5, 6, 7 P3 surface defects

Abnormal situations that could lead to machine failures are defined from expert knowledge and expressed as a set of constraints in Table 2. Quality issues situations are also defined from expert knowledge and expressed as a set of constraints in Table 3. They describe quality issues detected on products and the associated machine and product constraints.

Data streams are then created by the Stream Generator and continuous queries are performed on them. To do this, the Stream Reasoner is used with an ontology containing information on the production line. Once a situation is detected, is it added to the ontology. An of-line reasoning is done to check the consistency of the ontology. Another Reasoner is then used to provide an explanation thanks to the information contained in the ontology which allows to link defects found with expert information and technical documentation.

4. Conclusion and future work

A semantic approach to quality loss explanation in the manufacturing industry is presented. Data streams are processed with stream reasoning allowing real-time situation detection. A context ontology is used to help detect quality issues by enriching the information contained in the streams. In future work, explanations of abnormal situations and their causes will be adapted according to the end user.

Acknowledgments References

This work was supported by the French National Research Agency [grant number ANR-22-CE92-0007].

References

[1] A. V. Carvalho , D. V. Enrique , A. Chouchene , F. Charrua-Santos , Quality 4 .0: An Overview , Procedia Computer Science 181 ( 2021 ) 341 - 346 .

[2] T. V. Andrianandrianina Johanesa , L. Equeter , S. A. Mahmoudi , Survey on ai applications for product quality control and predictive maintenance in industry 4.0 , Electronics 13 ( 2024 ).

[3] F. Giustozzi , J. Saunier , C. Zanni-Merk , Abnormal Situations Interpretation in Industry 4.0 using Stream Reasoning , Procedia Computer Science 159 ( 2019 ) 620 - 629 .

[4] F. Giustozzi , J. Saunier , C. Zanni-Merk , Context Modeling for Industry 4.0: an Ontology-Based Proposal , Procedia Computer Science 126 ( 2018 ) 675 - 684 .

[5] H. Kim , M. Fox , M. Grüninger , An Ontology for Quality Management - Enabling Quality Problem Identification and Tracing , Bt Technology Journal - BT TECHNOL J 17 ( 1999 ) 131 - 140 .

[6] R. Tommasini , P. Bonte , F. Ongenae , E. D. Valle , RSP4J: An API for RDF Stream Processing , in: Extended Semantic Web Conference, 2021 .

[7] M. Bellucci , N. Delestre , N. Malandain , C. Zanni-Merk , Ontologies to build a predictive architecture to classify and explain , in: DeepOntoNLP Workshop @ESWC 2022, Hersonissos, Greece, 2022 .