A Project-based Learning Approach in Teaching Simulation to Undergraduate and Graduate Students

In this study, application of experiential learning into graduate and undergraduate curricula of a industrial system simulation course is presented. Simulation has been among the courses against which students feel uncomfortable or frightened due to heavy software use, prerequisite of probability, and statistics knowledge, and its application requirements. To minimize this fear and improve student’s understanding about the subject matters and have them develop ample skills to build complex models, a project-based learning approach is proposed and used in undergraduate and graduate teaching settings. To achieve the project-based learning goals, a 15-week curriculum is designed to have a balanced lecture and lab sessions, which are specifically designed to address the needs of the term project as the semester continues. In the term project, groups of 2-3 students were asked to form a group, where each group was expected to work on a real system to 1) understand, conceptualize, and model the existing system as a mental, then software-model; 2) validate the existing system model statistically; 3) identify areas for improvement (in addition to the ones given by the supervisor); 4) complete the project with testing out system improvement scenarios and conducting cost/benefit analysis. The effectiveness of project-based learning is surveyed and studied based on the course learning outcomes. The results indicated that the proposed project-based learning approach was found to be effective in students’ learning experience and critically supportive on reaching the learning outcomes, and it was found that students’ learning and skills of simulation modeling and application are improved regardless of their grade

A First-Order Logic Formalization of the Industrial Ontology Foundry Signature Using Basic Formal Ontology

Basic Formal Ontology (BFO) is a top-level ontology used in hundreds of active projects in scientific and other domains. BFO has been selected to serve as top-level ontology in the Industrial Ontologies Foundry (IOF), an initiative to create a suite of ontologies to support digital manufacturing on the part of representatives from a number of branches of the advanced manufacturing industries. We here present a first draft set of axioms and definitions of an IOF upper ontology descending from BFO. The axiomatization is designed to capture the meanings of terms commonly used in manufacturing and is designed to serve as starting point for the construction of the IOF ontology suite

CHAIKMAT 4.0 - Commonsense Knowledge and Hybrid Artificial Intelligence for Trusted Flexible Manufacturing

International audienceFlexible manufacturing plays an important role in Industry 4.0 for developing the factory of the future and requires enhanced planning, scheduling, and control. The quick and effective adaptation in the production line in response to customers’ requirements or face of unwanted situations will promote considerable flexibility in manufacturing. CHAIKMAT is a research project funded by the French National Agency of research that aims to add flexibility and transparency to manufacturing through trustful automatic decision-making. The project proposes a human-centric AI approach that investigates whether an available set of machines can perform a specific production process and then provides human experts with meaningful explanations of how the decision process is conducted. A hybrid predictive model, comprising of both semantic reasoning and machine learning system will help in real-time decision making through the automated analysis of two sources of information: a stream of machine-monitoring data describing the current state of the production line and a common-sense knowledge graph (MCSKG) that is modelled based on machine capability and process planning ontology model. Furthermore, this hybrid predictive model will also be able to explain its prediction so that the user can fully comprehend the rationale behind such a decision. In this paper, we will describe the architecture of the proposed system along with a detailed plan for verification. The paper also presents the state-of-the-art of AI applications in flexible manufacturing to establish how CHAIKMAT project aims to apply some of the novel AI methodologies to circumvent the existing gaps