In the context of Industry 4.0, flexible manufacturing is especially essential for developing
future factories with enhanced planning, scheduling, and control [
Similar disruptions may arise from extreme weather, longer-term climate change, declining international order, economic crises, changing societal priorities, cyber threats, or terrorism. Furthermore, flexibility in the manufacturing industries is more critical than ever to tackle ever-growing product variability, supply chain volatility, and unpredictability of customer requirements. Product life cycles are becoming more and more dynamic; also, at the same time, the number of product variants continues to grow. There is a need for more flexible, trustworthy, and eficient manufacturing processes.Traditional manufacturing paradigms such as lean manufacturing, just-in-time production, and KANBAN 3 systems often struggle to adapt to unforeseen disruptions. These systems can fall into a "rupture condition" when faced with unexpected challenges like pandemics, economic crises, or cyber threats. Rapid reconfiguration of operations highlights the need for flexibility in manufacturing to manage product variability, supply chain volatility, and unpredictability of customer demand.
Most production planning still depends on industry standards, best practices, manufacturing know-how, and machinists’ experiential knowledge. Despite the advent of Artificial Intelligence (AI), industries still depend on human expertise to make process planning decisions in an unprecedented situation because of uncertain or low expectations of AI investments (Trust). Personal judgment overrides AI-based decision-making (Human-reasoning).
However, human experts will only trust and accept AI results if the automated
decisionmaking process is transparent [
Semantics is the study of meaning [
The use of semantics in explaining various types of supervised and unsupervised learning
models was presented by Seeliger et al. [
Commonsense knowledge (CSK) explanation is the most meaningful and sought-after explanation technique [12]. According to the state of the art, incorporating and implementing CSK capabilities into AI can enhance the overall manufacturing potential and accelerate the growth of AI applications in the industry [13]. However, representing CSK related to manufacturing is challenging [14]. In the literature, significant progress has been made in four areas related to CSK in AI, mainly reasoning about taxonomic categories, logic, time and space, and actions [15].
Yet, there seems to be no CSK for the industry that integrates all main aspects of manufacturing process requirements. The manufacturing commonsense knowledge (MACS) should cover generic manufacturing background knowledge that human experts, such as machinists, planners, and shopfloor managers, carry as part of their experience to know or assume to understand and reason about information (or situation) that they are dealing with (e.g., a machine needs some kind of energy, a machine can breakdown, the production process needs raw materials, etc.). Generating commonsense explanations requires integrating richer domain knowledge [16] represented through ontologies.
The ontologies should cover production-relevant domains, including machine capabilities, production process, product specifications, raw materials, etc.
We examine current methods for manufacturers to increase transparency in AI system decision-making as a state of the art methodology structure shown in Fig.1. Two promising areas of XAI are ontology-based (O-XAI) and semantic-based (S-XAI), which use semantic information to provide human-readable explanations of AI decisions. Translating AI algorithm decision paths to meaningful explanations using semantics, O-XAI, and S-XAI helps humans identify cross-cutting concerns influencing AI system decisions. We discuss the pros and cons of using O-XAI and S-XAI systems in manufacturing and future research potential to guide researchers and practitioners in utilizing these explainable systems for decision-making [18].
"CSK and Hybrid AI for Trustful and flexible manufacturing 4.0" (Chaikmat 4.0) 6 is a research project funded by the French National Agency of Research ANR that aims to add flexibility and transparency to manufacturing through trustful automatic decision.
In the context of flexible manufacturing, the selection of resources must also consider the dynamically changing conditions of the shop floor, including factors such as the age of the machines, maintenance histories, and the availability of operators. These considerations are crucial to determining the actual performance of machines and equipment. CHAIKMAT’s core objective is to evaluate whether available machines can execute specific production processes efectively.
The project seeks to add flexibility and transparency to manufacturing through reliable automatic decision-making systems. These systems are designed to provide human experts with meaningful explanations about decisions, using MACS to make the process clear and understandable. To meet these challenges, CHAIKMAT,s proposes a hybrid approach that bridges the gap between human expertise and AI-based decision-making. This strategy proposes eficient resource use and enhanced industrial flexibility, laying the groundwork for more advanced manufacturing operations [17].
The primary objective of this thesis, within the scope of the project, is to investigate the use and efects of ontology-based models in manufacturing in the context of semantic reasoning, explainability, and eficient formalization of machine capabilities. The focus is primarily on robotics, with the intention of improving the explainability of how robots are assigned tasks based on their capabilities. This involves developing and validating ontological models to encapsulate MACS and robotics capabilities for decision-making and explainability. The following are the objectives that have been outlined below: • Formalization of machine specifications that include capabilities, capacities, functions, quality, and process characteristics, focusing on robotics. • Establish a framework for identifying MACS patterns, extracting MACS, formalizing MACS using standard vocabulary, converting them into semantic rules, and leveraging MACS patterns within decision-making processes. • Utilization of machine capabilities and MACS patterns, alongside a neural network framework, to explain whether an existing set of machines can perform a specific task or not based on robot capabilities.
Developing methods for utilizing machine specifications and MACS patterns, along with integrating a semantics-based explainable AI (S-XAI) framework, is crucial to achieving the primary objectives of this thesis. These elements will enhance decision-making processes, increase user trust, and provide transparency. The following research questions have been formulated to guide this investigation: • How can we formalize key notions such as Capability, Capacity, Function, Quality, and
Process Characteristics in the context of robotics? • How can MACS be extracted, formalized, and integrated into decision-making processes to enhance explainability? • How can ontologies and knowledge graphs can facilitate the creation of explanations that align with human understanding while enhancing the use of S-XAI in manufacturing decision-making processes?
This thesis presents three critical contributions to improving the explainability of the decisionmaking process in manufacturing by integrating robotics capabilities, MACS, and S-XAI.
Our first contribution is the development of the Robotic Capability Ontology (RCO) [ 19], an application ontology specifically designed to model robotic capabilities [ 19]. RCO utilizes the Manufacturing Service Description Language (MSDL) [20], a domain reference ontology created for manufacturing services and aligned with the Basic Formal Ontology (BFO) [21], Industrial ontology Foundry (IOF) [22], Information Artifact Ontology (IAO) [23], and Relations ontology (RO) [24] as shown in Fig,2. MSDL’s modular structure and domain-neutral classes allow RCO to describe and expand upon robotic capabilities accurately. This enables the design of an ontological model that precisely captures robotic specifications from its specification manual as advertised capabilities to actual capabilities in an operational environment as operational capabilities.
Our second contribution introduces the concept of MACS. We propose a methodology for identifying MACS patterns, extracting, formalizing, and modeling this knowledge, and a standardized vocabulary that organizes MACS into semantic rules, as shown in Fig.3. These semantic rules are then converted into schema languages such as SPARQL and Datalog rules, which are used to create a MACS Knowledge Graph (MACS-KG) to demonstrate the applicability of the proposed method.
Our third contribution is developing a (S-XAI) framework as shown in Fig.4. that incorporates a neural network model trained on historical data about diferent tasks to predict robotic operational capabilities such as ’Repeatability’ and ’Precision’ on a given set of coordinates for a new task.
Also, utilizing XAI techniques like Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependency Plots (PDP), and Permutation Features Importance (PF) for the prediction of operational capabilities from neural networks alongside natural language explanations based on MACS patterns, the system provides clear, logical, and understandable explanations for each prediction.
We propose an S-XAI framework to address the concern about explainability issues related to the decision-making process in the manufacturing industry. The framework combines neural networks for predictive analysis of robot operational capabilities with rule-based reasoning grounded in MACS. This approach brings transparency and explainability to decision-making, ensuring stakeholders can trust and understand automated decisions.
A vital feature of the framework is the ability to integrate symbolic and sub-symbolic reasoning paradigms, enabling real-time, explainable decisions in manufacturing environments. By incorporating a neural network, the system will be scalable with the increasing data volume and continuously improve through active learning. At the same time, the use of manufacturing commonsense knowledge ensures that explanations provided to users are contextually relevant and easy to comprehend, fostering user trust and system acceptance.
This work is performed within the CHAIKMAT project funded by the French National Research Agency (ANR) under grant agreement ” ANR-21-CE10-0004-01” [9] Bianchi, F., Rossiello, G., Costabello, L., Palmonari, M., Minervini, P.: Knowledge graph embeddings and explainable AI. arXiv. (Apr. 2020). https://doi.org/10.3233/SSW200011 [10] Lombrozo, T.: Explanation and abductive inference. In: The Oxford Handbook of Thinking and Reasoning, pp. 260–276, New York, NY, US: Oxford University Press (2012) [11] Thagard, P.: Explanatory coherence. Behav. Brain Sci. 12(3), 435–467 (1989). https://doi.or g/10.1017/S0140525X00057046 [12] Reiter, E. (2019). Natural Language Generation Challenges for Explainable AI. Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019). https://doi.org/10.18653/v1/w19-8402 [13] Logic, language and commonsense. (1987). Artificial Intelligence, 169–176. https://doi.org/ 10.1016/b978-0-08-034112-5.50020-6 [14] Rehse, J.-R., Mehdiyev, N., Fettke, P.: Towards explainable process predictions for industry 4.0 in the DFKI-smart-lego-factory. KI - Künstliche Intell. 33(2), 181–187 (2019). https: //doi.org/10.1007/s13218-019-00586-1 [15] Davis, E.: Logical formalizations of commonsense reasoning: a survey. J. Artif. Intell. Res.
59, 651–723 (2017). https://doi.org/10.1613/jair.5339 [16] Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C. A., & Meersman, R. (2019).
Correction to: On the Move to Meaningful Internet Systems. On the Move to Meaningful Internet Systems: OTM 2019 Conferences, C1–C1. https://doi.org/10.1007/978-3-030-332 46-4_47 [17] A. Sarkar, M. R. Naqvi, L. Elmhadhbi, D. Sormaz, B. Archimede, M. H. Karray, CHAIKMAT 4.0 - Commonsense Knowledge and Hybrid Artificial Intelligence for Trusted Flexible Manufacturing, Springer International Publishing, 2023, p. 455–465. https://doi: 10.1007/978-3-031-17629-6_47. [18] M. R. Naqvi, L. Elmhadhbi, A. Sarkar, B. Archimede, M. H. Karray, Survey on ontologybased explainable ai in manufacturing, Journal of Intelligent Manufacturing (2024). https: //doi:10.1007/s10845-023-02304-z. [19] M. R. Naqvi, A. Sarkar, F. Ameri, S. N. Araghi, M. H. Karray, Application of msdl in modeling capabilities of robots (2023). https://ceur-ws.org/Vol-3595/paper7.pdf. [20] Ameri, F., & Dutta, D. (2006). An Upper Ontology for Manufacturing Service Description.
Volume 3: 26th Computers and Information in Engineering Conference. https://doi.org/10 .1115/detc2006-99600 [21] J. N. Otte, J. Beverley, A. Ruttenberg, Bfo: Basic formal ontology, Applied ontology 17.
https://doi.org/10.3233/AO-220262 (2022) 17–43. [22] Drobnjakovic, M., Kulvatunyou, B., Ameri, F., Will, C., Smith, B., & Jones, A. (2022). The industrial ontologies foundry (IOF) core ontology. embeddings and explainable AI. arXiv. (Apr. 2020). https://doi.org/10.3233/SSW200011 [23] Smith, B., Malyuta, T., Rudnicki, R., Mandrick, W., Salmen, D., Morosof, P., ... & Parent, K.
(2013). IAO-Intel: an ontology of information artifacts in the intelligence domain. [24] W. J. Wildman, An introduction to relational ontology, The trinity and an entangled world: Relationality in physical science and theology (2010) 55–73.