Formal ontologies in manufacturing

Sanfilippo, Emilio M.; Kitamura, Yoshinobuc; Young, Robert I. M.

doi:10.3233/ao-190209

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…Ontology has also been shown to assist in feature and model selection, causal inference, 211 and automated reasoning 208 in research related to other industries. [211][212][213][214] So far, in the biomedical field and pharmaceutical industry, several prominent ontologies have emerged that cover various aspects of product development and manufacturing. For instance, Ontology for biomedical investigations (OBI) can be used to log biological and clinical investigations.…”

Section: Data Contextualization and Metadata Managementmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

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Continuous biopharmaceutical manufacturing is currently a field of intense research due to its potential to make the entire production process more optimal for the modern, ever‐evolving biopharmaceutical market. Compared to traditional batch manufacturing, continuous bioprocessing is more efficient, adjustable, and sustainable and has reduced capital costs. However, despite its clear advantages, continuous bioprocessing is yet to be widely adopted in commercial manufacturing. This article provides an overview of the technological roadblocks for extensive adoptions and points out the recent advances that could help overcome them. In total, three key areas for improvement are identified: Quality by Design (QbD) implementation, integration of upstream and downstream technologies, and data and knowledge management. First, the challenges to QbD implementation are explored. Specifically, process control, process analytical technology (PAT), critical process parameter (CPP) identification, and mathematical models for bioprocess control and design are recognized as crucial for successful QbD realizations. Next, the difficulties of end‐to‐end process integration are examined, with a particular emphasis on downstream processing. Finally, the problem of data and knowledge management and its potential solutions are outlined where ontologies and data standards are pointed out as key drivers of progress.

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Section: Data Contextualization and Metadata Managementmentioning

confidence: 99%

Current challenges and recent advances on the path towards continuous biomanufacturing

Drobnjakovic

Hart²,

Kulvatunyou

et al. 2023

Biotechnology Progress

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“…Even though ISA-95 (IEC/ISO 62264, 2013) is the most used reference standard for the interoperation of manufacturing tools, it is also limited by the lack of semantic consistency in interpretation (Palmer et al, 2018). Sanfilippo et al (2019) have demonstrated the problem of misinterpretations among users of ISA-95, identifying 83 errors in a real-world example and explaining how these could have been avoided by semantical approaches. The necessity of improved standards has been recognized by Panetto et al (2016).…”

Section: Semantic Interoperability Issues In Vertical and Horizontal ...mentioning

confidence: 99%

Ontological Approach to Support the Horizontal and Vertical Information Integration in Smart Manufacturing Systems: An Experimental Case in a Long-Life Packaging Factory

Pereira

Szejka

Canciglieri

2022

Front. Manuf. Technol.

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Digital manufacturing has been challenged by the manufacturing industry to rationalize different ways to connect and exchange information and knowledge across different phases of manufacturing systems. One of the Industry 4.0 pillars is the horizontal and vertical integration with intelligent and self-adaptive systems. For this to be possible, the manufacturing industry applies an extensive range of software tools, such as GRAI, CIMOSA, MO2GO, ARIS, SCADA, MES, ERP, CAD, and CAM. Individually, each one performs its function to support the manufacturing process. However, when these multiple tools operate together using technical standards, some misinterpretation and mistake gaps are identified due to a lack of machine-to-machine (M2M) communication and users’ interpretation. This is recognized as a semantic interoperability problem. Semantic technologies, such as ontologies, have been proven to be a promising way to overcome semantic interoperability obstacles. Based on this context, this study is proposing a conceptual framework based on semantic technologies to create a solution to the horizontal and vertical integration and semantic interoperability obstacle. MANUMATE is the framework proposed, and it consists of three artifacts, 1) reference ontologies, 2) requirements, and 3) application ontology, and two processes, 1) ontology specialization and 2) information application. The MANUMATE framework is applied to two experimental case studies to validate the conceptual solution in two different applications, in the context of a long-life package for the beverages industry. These case studies help elucidate how the application of the framework could improve the information and knowledge exchange by providing a standard way to represent information among different stakeholders in the productive process. A discussion about the results is presented, revealing the benefits and limitations of the solution.

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“…As stated and thoroughly discussed by Ramos [16] and Sanfilippo et al [36], to date, generally accepted manufacturing ontologies are lacking; there is no standard ontology for materials, products, resources, processes, or manufacturing systems. Moreover, ontology languages have different levels of expressivity, making careful process of evaluation for each use case necessary [37].…”

Section: Semantic Web Technologies For Mps According Tomentioning

confidence: 99%

“…Although here we do not claim to address all shortfalls raised by Ramos [16] and Sanfilippo et al [36], we develop an ontology that is compatible with existing ones and that primarily focuses on recommending manufacturing processes for candidate product features and material characteristics under consideration. e suggested ontology-based system contributes to bridging the gap between product engineering, material engineering, and process engineering.…”

Section: Semantic Web Technologies For Mps According Tomentioning

confidence: 99%

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

Mabkhot

Al-Samhan

Hidri

2019

Advances in Materials Science and Engineering

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In nowadays industry 4.0 and changeable manufacturing context, designers and manufacturing engineers struggle to determine appropriate quick, accurate (with flawless quality), and cost-effective processes to design highly customized products to meet customer requirements. To determine manufacturing processes, the matching between product features, material characteristics, and process capabilities needs to be optimized. Finding such an optimized matching is usually referred to as manufacturing process selection (MPS), which is not an easy task because of the infinite combinations of product features, numerous material characteristics, and various manufacturing processes. Although problems associated with MPS have received considerable attention, semantic web technologies are still underexplored and their potential is still uncovered. Almost no previous study has considered combining case-based reasoning (CBR) with ontologies, a famous and powerful semantic web enabler, to achieve MPS. In this study, we developed a decision support system (DSS) for MPS based on ontology-enabled CBR. By applying automatic reasoning and similarity retrieving on an industrial case study, we show that ontologies enable process selection by determining competitive matching between product features, material characteristics, and process capabilities and by endorsing effective case retrieval.

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Formal ontologies in manufacturing

Cited by 21 publications

References 32 publications

Current challenges and recent advances on the path towards continuous biomanufacturing

Current challenges and recent advances on the path towards continuous biomanufacturing

Ontological Approach to Support the Horizontal and Vertical Information Integration in Smart Manufacturing Systems: An Experimental Case in a Long-Life Packaging Factory

An Ontology-Enabled Case-Based Reasoning Decision Support System for Manufacturing Process Selection

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