# From Ontologies to Operative Data Models: A Data Model Development Supporting Zero Defect Manufacturing

Claudio Turrin, Federica Acerbi, Antonio Avai, Arnaldo Pagani, Manfredi

Giuseppe Pistone, Angelo Marguglio, Pierluigi Petrali

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Claudio Turrin, Federica Acerbi, Antonio Avai, Arnaldo Pagani, Manfredi Giuseppe Pistone, et al. From Ontologies to Operative Data Models: A Data Model Development Supporting Zero Defect Manufacturing. 19th IFIP International Conference on Product Lifecycle Management (PLM), Jul 2022, Grenoble, France. pp.663-672, 10.1007/978-3-031-25182-5_64. hal-05420902

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# From Ontologies to Operative Data Models:
A Data Model Development Supporting Zero
Defect Manufacturing

Claudio Turrin¹ Ⓓ, Federica Acerbi²(✉) Ⓓ, Antonio Avai³ Ⓓ, Arnaldo Pagani⁴,
Manfredi Giuseppe Pistone⁵, Angelo Marguglio⁵, and Pierluigi Petrali⁴

¹ SAM Software, Via Muller 37, 28921 Verbania, Italy

² Department of Management, Economics and Industrial Engineering, Politecnico di Milano,
via Lambruschini 4/b, 20156 Milan, Italy
Federica.acerbi@polimi.it

³ TTS – Technology Transfer System, Via Francesco d’Ovidio, 3, 20131 Milan, Italy
avai@ttsnetwork.com

⁴ Whirlpool EMEA spa, Via Aldo Moro, 5, 21024 Biandronno, VA, Italy
Arnaldo_Pagani@whirlpool.com

⁵ Engineering Ingegneria Informatica, Piazzale dell'Agricoltura, 24, 00144 Rome, Italy
{ManfrediGiuseppe.Pistone,angelo.marguglio}@eng.it

**Abstract.** Resource consumption is expected to reach 167 Gigatonnes by 2060 doubling the amount accounted in 2017. More than ever, manufacturing companies are asked to rapidly face this issue to limit their negative impacts on the entire society. Factories are supposed to act on their internal operating activities to enhance the quality of both products and processes reducing as much as possible the industrial waste generated. Among all, the zero-defect manufacturing principles represent an opportunity towards this direction, but companies necessitate to rely on their existing assets, both digital and physical, to create value based on that. Therefore, this contribution aims to develop an operating database based on a structured data model enabling to link, and reason over, three main areas (i.e. material, process, function) influencing the final product quality. These areas were identified in an already validated ontology, GRACE, considering them as the key elements to be kept under consideration to evaluate the impacts on product quality facilitating to make zero-defect oriented decisions. Indeed, the developed model, to be useable by a manufacturing company, was aimed to ensure to be data source independent, to embed these four areas, to include all the internal processes (i.e., pre-assembly, assembly) of a factory, and to transform abstracts ideas into operative actions. The data model development procedure is described, and its application was performed in a case study (i.e., an assembly line). This application enabled to validate it highlighting the key requirements for a discrete manufacturing company to embrace zero-defect manufacturing.

**Keywords:** Zero-defect manufacturing · Assembly line · Data model
# 1 Introduction

Resource consumption is increasingly rising, supposing to reach 167 Gigatones by 2060 doubling the amount accounted in 2017 [1]. This problem requires to be rapidly faced by the entire society, and especially by manufacturing companies due to their high resource intensity characteristic [2] which leads also towards an augment of waste and pollution generation [3]. Regarding the waste, the enhancement of process quality can be a great driver towards waste reduction and the concept of “zero defect manufacturing” is gaining momentum representing a cornerstone in this domain [4]. Actually, the “zero defect manufacturing” (ZDM) is easily adoptable in discrete manufacturing companies, advancing the traditional quality management techniques like Six Sigma and Lean [5]. In this context, the ZDM concept relies on a set of inspection activities covering the different parts of the products to be manufactured [4]. Data collection and management represent a fundamental element to monitor product and process quality enhancing the company’s sustainability attitude [6]. Indeed, data mining technique was adopted to investigate and analyse the causes (e.g. the equipment status) affecting the product quality during the production process to strengthen the ZDM benefits obtainable by discrete manufacturing companies [7]. Nevertheless, especially in discrete manufacturing companies, data sources are heterogeneous and complex to be managed in a coordinated way. For this reason, it is required to reduce the complexity and create interoperable systems enabling the exchange of the right data, at the right time to provide useful information to end users based on an appropriate data analysis.

Data collection, analysis and sharing are easily achievable in the contemporary era thanks to the diffusion of the key enabling technologies characterizing the Industry 4.0 (I4.0) paradigm. Among them, in the context of product quality management relying on a ZDM approach, a digital twin has been developed based on the R-MPFQ model (Resource, Material, Production Process, Function, Quality) [8]. This model has been linked to an ontology developed in a European project "inteGration of pProcess and quAlity Control using multi-agent technologies (GRACE)", which was aimed at clarifying the main entities to be kept into account in integrating process and quality control. The main gap observed was the difficulty in translating this standard methodology into concrete actions implementable by a factory by relying on the data gathered from its information systems. In the European Project QU4LITY https://qu4lity-project.eu/, focused on ZDM in the I4.0 era, an operative data model has been developed by relying on the already validated ontological model. This model aims at supporting the decision-making process of both managers and operators facilitating them in performing the proper corrective actions to improve product quality. This contribution will explain how a holistic semantic data model has been developed and how it can be used to monitor the product quality and systematically detect the causes fostering ZDM.

The remainder of the paper is structured as follows. Section 2 presents the theoretical background explaining the MPFQ model in its detail and how it is linked with the concept of zero-defect manufacturing. Section 3 elucidates the data model development and application in a concrete case. Section 4 concludes the contribution by highlighting the main implications to practice and theory underlining the open opportunities based on the limitations of this research.
# 2 Research Context

## 2.1 Quality Management and Zero-Defect Manufacturing

The concept of zero-defect manufacturing is gaining momentum being it considered an opportunity for factories to create more eco-friendly and more efficient production lines with zero defects [9]. This is extremely important, especially for discrete manufacturing companies which are forced to increase the customization level of products ensuring the quality of both products and processes, thus limiting the products defects [10]. Indeed, process control and monitoring may empower product quality reducing defected parts since process degradation and malfunctions can cause defects on both parts and products [11]. Actually, zero-defect manufacturing strategies can be of two types: (i) zero-defect prevention, according to which knowing in depth the production process conditions and the machine state it is possible to prevent a defect, and (ii) zero defect compensation, according to which a downstream change of the process is made to avoid reworking activities [12]. In both cases, information sharing through integrated information and communication solutions may benefit the efficiency improvement by limiting processes and products defects, thus enhancing their quality [13].

## 2.2 MPFQ Ontological Model

As just mentioned, quality management can be highly enhanced by the proper use and sharing of information and knowledge which can be facilitated by the availability in factories of integrated and/or interoperable systems [14]. Nevertheless, interoperability among systems still represents an issue for several manufacturing companies causing semantic misalignment which indirectly is linked to a limited capability to reuse and share knowledge in a factory [15]. To tackle this issue, ontologies have been developed with the final aim to create a common and shared view over knowledge representation that facilitates the communication among distributed entities [16].

In the context of quality management, an ontology (i.e. GRACE ontology) has been developed, that aimed at covering the knowledge about the resources in production lines, the production process, and the testing of the produced products to link this knowledge with the distributed quality controls [17]. Based on this ontology, to furtherly focus the attention on product quality, the MPFQ model has been created, which stands for Material, Process, Functions and Quality, covering the key areas influencing product quality [18]. Recently, it has been added the R of Resources to the MPFQ model, considered as the industrial assets required to produce the products, to differentiate the industrial assets from the other elements included in the model [8].

Although the ontology generated represents a comprehensive picture supporting knowledge sharing for quality management issues, its concrete operationalization is still missing. Therefore, based on this generated and validated scientific knowledge, a relational data model has been elaborated in this contribution to overcome the key limitations emerged so far. This model has been developed by operationalizing the ontology so far created with the final goal to enable the exploitation of data coming from the factory information systems to take informed decisions.
## 3 Data Model Development and Application

The R-MPFQ modelling approach has been used in the QU4LITY project with the goal to model assembly and quality processes by using, as pilot, the Lodz Dryer factory of Whirlpool (WHR). The first main action of the QU4LITY WHR team was to map the WHR Lodz Dryer factory assembly processes and the final product to understand where and how the R-MPFQ model could be generated. Starting from the bill of material of a dryer and the experience of the quality team of the Lodz factory, the dryer was decomposed into six sub-units, and then the relations between components of each sub-unit were identified. The assembly process mapping aimed not only to model the organization (sequential or parallel) of the final product as well as the sub-units assembly processes but also to organize how the processes and related operating resources are harmoniously structured within the assembly sequences and how the “Function” element is generated. This aspect is very important for the QU4LITY project because it represents what a customer mainly perceives from a dryer, either in a positive or negative sense. The outcome of this activity has been the MFQ model of the product itself, which represents the key element on which the entire operational modelling is based.

Figure 1 represents the MFQ model of a dryer where rectangular boxes are the main functional units of a dryer (M), the orange hexagons show some Quality indicators (Q), the light blue hexagons are the external elements and the arrows are the main functions (F) generated by each functional unit. To report an example explaining the functional model, the Quality element “noise” is generated by the air flow, mechanical and hydraulic sub-units of the dryer, while the chassis has the function of stopping or reducing the noise. If “noise” is perceived as a critical aspect for customers, it means the previously mentioned sub-units should be investigated deeper to find out the key elements in terms of material, process or function that allow reducing noise and achieving zero-defect manufacturing. A detailed graph was implemented for each sub-unit of the dryer.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Functional model of a dryer</figcaption></figure>
The next step was the creation of the R-MPFQ model for the dryer and all its sub-units. Figure 2 depicts a small part of the developed model referring to the heat pump sub-unit where the different colours of the boxes represent the four elements of the model. For instance, the compressor (M) is placed on the silent block (M) by the pick and place process (P). The function (F) of the silent block serves to hold the compressor and to reduce the noise (Q) of the compressor that, on the contrary, generates noise (Q) and consumes energy (Q). The pick and place process may impact on the noise if the compressor is not properly assembled on the silent block. All these Quality elements (noise, energy consumption, leakage) are measurable during the assembly process.

<figure><img src="image_4.png" /><figcaption>Fig. 2. MPFQ model of heat pump sub-unit</figcaption></figure>

Along with the creation of this model, it was needed to define the data ontology model able: (i) to represent the R-MPFQ model; (ii) to represent any type of assembly process, pre-assembly, testing, typology of resources, type of operations, etc.; (iii) to turn in an operational object capable of transforming logical abstraction concepts into concatenations of events and data; (iv) to transform the information in a “standard” format. Therefore, to ensure all these issues, the team started to create the relational database from the ontological model of manufacturing developed under the project GRACE [17]. This model was deeply analyzed to check if it was able to represent all the requirements listed before. A revised ontology model was defined integrating new concepts in the initial one, such as “Measure”, “Resource” or “Function”, based on other existing IOF ontologies (link). In parallel, based on this ontology, a relational database operative for the factory was developed. The result was the development of an ontological model represented by the graph in Fig. 3 which has been “transformed” into relational entities [19], representative of reality. This “transformation” means giving each entity an operational value and transforming abstract entities into physical entities clarifying the relations among them based on normalized information.
<figure><img src="image_5.png" /><figcaption>**Fig. 3.** The final ontological data model</figcaption></figure>

Once the relational data structure and its alignment with the ontological model have been finalised, the description of all the parts that compose the assembly process started and, in particular, the following entities were defined and related:

- Journal: it describes the production of a product instance belonging to a production order executed in the assembly line.

- Journal Details: it describes the execution of an operation by a single station.

- Operation: a job executed by one resource.

- Process: a single process composing an operation.

- Resource: an asset that can perform a certain range of processes.

- Function: a feature of the product or the process that can be measured.

The relationships were defined based on the ontological model and this composition of the entities led to the development of two types of representations: the “Static” one, where the system is described by the relations between the entities, and the “Dynamic” one, which enables to correlate and associate the events that occurred during the execution of the single process along a timeline. This correlation is possible thanks to the information about the connection with the production tracking. The product tracking hence is easily linked to process and product measures.

Once the series of tables representing ontological entities were developed and their links were defined, the relational process was then translated into a relational database system. In particular, by introducing the R-MPFQ model fused with Autonomous Quality (AQ) control loops in production, it was addressed unresolved problems in the vertical integration of data management (from data gathering to visualization and decision making), enabling a holistic vision to be achieved. To this end, a set of solutions (see Fig. 4), namely QU4LITY Cloud Solutions, were built to provide a seamless solution to exchange data using the R-MPFQ ontology model, enabling a semantic enriched data exchange
from on-premises data lakes to enable the Cloud Data Storage using a time-based approach: QU4LITY Cloud Bridge and Q-Ontology Enabler. The former, QU4LITY Cloud Bridge, built on top of Node.js and Sequelize (an open-source ORM Object/Relational Mapper), offers a REST API layer to ease the interfaces between R-MPFQ relational database and other processing and visualization components within the pilot, taking care of any data decoding/encoding needs (i.e. IEEE754 data encoding). The latter, the so-called Q-Ontology Enabler, consisting of a set of scripts built with Python, delivers a powerful tool to enable semantic interoperability between legacy data and newly engineered R-MPFQ Ontology with extreme ease. Indeed, thanks to the ontology built, only input data format has to be mapped for the scripts to work.

<figure><img src="image_6.png" /><figcaption>Fig. 4. QU4LITY Cloud Bridge and Q-Ontology Enabler positioning</figcaption></figure>

Subsequently, to support the decision-making on the shopfloor, a dashboard QU4LITY trend cockpit was developed as a Web-based application to query the QU4LITY cloud bridge and report the measurements and the results according to the relations defined with the R-MPFQ model. Thanks to this implementation, it is possible to carry on some R-MPFQ analysis starting from the selection of a Quality indicator, then of a material or a process looking at a certain timeframe as visible in Fig. 5. In the example, the end-user is interested in investigating the energy consumption of the drum. The application, leveraging on the R-MPFQ model and the related database, can show all the materials, functions and processes/resources involved in the energy consumption of the drum.

Then, the end-user can select an item and can analyze the data collected as shown in Fig. 6, where the data about drum concentricity are plotted and the relative CPK (Process Capability Index) is assessed. In the same way, the end-user can retrieve data about processes or functions and find out trends or critical values impacting the Quality of the final product negatively.

The Qu4lity trend cockpit, integrated with the R-MPFQ model, the ontology data model and the operative relational database, allows the autonomous ZDM which entails humans in the loop. Therefore, different stakeholders' profiles such as Quality manager, product development team and industrial engineer can exploit this Quality system to make fast and effective analysis on production and process quality, to correlate consumers' needs with products and processes quality metrics and to take decisions based on data and trends. This advancement was possible thanks to the transformation of the
QUALITY

MPFQ ANALISYS

Parameter selection

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 5. QU4LITY trend cockpit interface</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 6. Data analysis</figcaption></figure>

R-MPFQ model from an abstract model to an operating one as integrated into the onto-
logical model. Usually, for companies, the measurements are associated “vertically” to
specific parameters of specific objects. In an ontological model, the measurements may
be considered as enlarged covering a broader area, thanks to the correlations established
among entities both directly and indirectly. Indeed, the measurement of an element
means to measure a chain of process elements and functional elements that enable to
reach a certain result, which is possible thanks to the new developed model.

Additionally, on one side, the manufacturing sector does not use the functional
model of the product represented as composed by elements of varied nature (mechanical,
hydraulic, electric, etc.). This functional model hence has never been used as a lever, in
the manufacturing world, to understand how good the product production is and thus,
this operational model facilitates the opening towards this direction. On the other side,
also quality management is still limited to only part of the parameters which might be
explored thanks to this model. Indeed, sophisticated data analysis tools such as neural
networks should be used for issues like the monitoring of those parameters enabling to
make the manufacturing company knows what makes the final customer be satisfied or
dissatisfied referring to the production process.
# 4 Conclusions

This contribution enabled to highlight the main challenges that might be faced when an ontological model needs to be concretely adopted by an industrial company and thus, it needs to be transferred into an operational data model highlighting also the key benefits. More specifically, considering that ontologies are created to develop standards aiming at facilitating systems interoperability and thus, knowledge sharing within the factory boundaries, these must be easily adoptable by companies and need to be transferred into their industrial contexts. Therefore, the transformation process, required to generate a data model starting from an ontological model, is deeply described in this contribution with the aim to share the knowledge on how to develop a solution enabling to exploit the data collected from heterogeneous data sources characterizing many manufacturing companies. Such a process required the involvement of a team with different knowledge on products and processes of Lodz Dryer factory, on ontologies definition, and on data models and relational database development. The close collaboration of the team was mandatory to achieve this result.

Last, the final operational data model is under validation by WHP in the Lodz Dryer factory. As highlighted in the application part of this contribution, it should facilitate the analysis of the data collected from the assembly lines with the ultimate goal to take corrective actions on the shop floor by acting directly on those, often hidden, elements influencing customers' perspectives. The first results are very promising and based on these tests, WHP will decide to extend this methodology into other factories of its group. Moreover, an additional user-friendly application is under development with the aim to support operators in their daily activities reaching zero-defect manufacturing.

This research was funded by the European Project QU4LITY (GA. 825030) https://qu4lity-project.eu/.

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