Bovet Pascal
Professeur HES ordinaire/Directeur INNOSQUARE
Compétences principales
Product Life Cycle Management
Model-Based System Engineering
FEM Analysis
Durability of mechanical systems
Bureau: HEIA_D10.09
SeSi - Sustainable Engineering Systems Institute
MSc HES-SO en Engineering - HES-SO Master
Innovation de produits et PLM
Fiabilité et durabilité des systèmes industriels
MSc HES-SO en Integrated Innovation for Product and Business Development - Innokick - HES-SO Master
Gestion du cycle de vie des produits - PLM
BSc HES-SO en Génie mécanique - Haute école d'ingénierie et d'architecture de Fribourg
Analyse de structures FEM
Simulation FEM et tests
Requirement-driven Optimization of System Concept with Integrated Model Based Safety Analysis
Financement: NPR Canton de Fribourg
Today's technical systems are getting more and more complex associated with the rapid increase of new technologies in a number of industrial domains. These systems have one feature in common: the increasing amount and complexity of software. And they have to be safe against humans and the environment. Ascertaining the safe behavior of technical systems is key. Therefore, a number of safety regulations and standards have emerged just over the last decade. Consequently, there is a significant growth of the scope and the intensity of safety assessments of technical systems required to being compliant with these safety regulations and standards. However, this has also an impact on today's approach of performing safety assessments which are predominantly carried out "manually", i.e. today's commercially available and cross-industry used safety analysis tools, that are no longer up to date to cope with the complexity of technical systems. Regulations in aerospace support to get off the traditional safety analysis way to a Model-Based Safety Analysis (MBSA) in order to minimize analysis errors as early as possible in the development phases of technical systems though the systems are getting constantly more complex. Model-based safety analysis has the benefits of identifying failure scenarios in a repetitive manner prior to the detailed design of technical systems and allows an automated execution of the required safety assessments, hence, further reducing potential "human errors" when analyzing systems safety. Other industries have already started to follow the aerospace approach.
In order to compete with the increasing complexity of technical systems in combination with the faster time-to-market demands guaranteeing the required level of safety, a framework for a requirements-driven optimization of the system concepts in conjunction with a Model-Based Safety Analysis (MBSA) respectively Model-Based Systems Engineering (MBSE) is proposed for this research project. By integrating MBSA into the MBSE based development of the system concepts, an automated procedure was developed respecting the relevant safety regulations/standards.
The automated MBSE-MBSA procedure is available for the industrial partners after being tested for several use cases. This procedure makes the automatic generation of FTA and FMEA from a common qualitative technical system model described with the SysML language. The SysML modeling includes both the description of the nominal system behavior and of the failure system behavior. This way of system modeling ensures a full MBSE-MBSA integration enabling the industrial partners to identify earlier in the preliminary concept phase the critical and in many cases safety-related design aspects. The automated procedure combines the modeling method of the nominal and failure system behavior using SysML and the coupled safety analysis using smartIflow Workbench v0.3.9 with the automatic generation of the safety analysis artifacts.
The tooled-procedure for MBSE-MBSA integration consists of enhanced system modeling structure, using the SysML language, which includes the nominal and failure modes in a single model. The interface between SysML and smartIflow is bridged by a specific Plug-in . Once to export/translate the data in
smartIflow, where the analysis of failure modes is conducted and post-processed. The automated generation of fault tree analysis (FTA) and failure mode and effect analysis (FMEA) is performed by two Plug-ins into smartIflow Workbench.
For simple technical systems analyzed, the automatically generated FTA and FMEA are similar to the manual FTA and FMEA, except additional detected failure modes for automated FTA and FMEA. The automatic creation of the safety artifacts helps to avoid human errors of omission or misunderstanding of the system. For technical systems of industrial partners, the automated procedure has limitations and is not yet sufficiently developed. The model checking algorithm of smartIflow transforms the generic model representation into a transition system. Ideally, the graph contains paths for every possible sequence of input event, so that every possible evolution of the system is covered. Since the complexity can be enormous, suitable solutions to model high-level system, to control the size of the transition system and to compute the potential failure modes require further research and development
Equipe de recherche au sein de la HES-SO: Bovet Pascal
Partenaires académiques: Pascal Bovet, Haute école d'ingénierie et d'architecture de Fribourg; Roland Scherwey, Haute école d'ingénierie et d'architecture de Fribourg; Rüdiger Lunde, Hochschule Ulm
Partenaires professionnels: Melina Brunet, Johnson Electric Morat; Alexandre Chassot, Meggitt, Villars-sur-Glâne; Juan Manuel Florez, Liebherr Machine Bulle, Bulle; Robert Fritsch, Brusa Eletronik, Sennwald
PROCESS 4 PLASTICS - Productivity Improvement for the Plastic Processing Sector in Preparation for Industry 4.0
Financement: NPR du Canton de Fribourg
In the study called Process 4 Plastics (P4P), research works were performed by three institutes of School of Engineering and Architecture Fribourg aiming to improve the productivity and to reduce production costs for plastic injection molding though the preparation of Industry 4.0. The industrial partners involved in the P4P study were DuPont, Johnson Electric Switzerland, GF Machining Solutions, Plaspaq, Plastechnik, Redel and Schoeller-Allibert. Kistler Instrumente were sponsor of the study with the availability of Kistler measurement equipment on mold and STASA QC process optimization software. Arburg has also contributed to the P4P study by providing access to its ALS software needed to retrieve the data from the machines. The research works were focused on two characteristics of Industry 4.0 that are the vertical networking of smart production system and the acceleration due to exponential technologies (sensor technologies).
The basis of the injection molding optimization was introduced with the main characteristics of the plastic phase transformation process requiring settings on machine and process parameters. The quality indices defining the final responses of the plastic injection molding in terms of quality were classified in geometrical, surfacic and volumic categories. The mathematical expressions of the optimization problem and of the rheological simulation were presented for the injection molding.
An experimental platform for the execution of the P4P study was specially built from an injection molding unit provided by one of the participating institutes. The injection molding unit was completed by in-cavity pressure and temperature sensors into the mold, ejection force sensor into the mold and two data processing systems. The first data processing system (Kistler CoMo) recorded the measured data from the in-cavity mold sensors during each injection cycles and processes the data. The profiles of in-cavity pressure and temperature as a function of time were stored over several injection cycles. The second data processing of a MES system (Arburg ALS) focuses on the process parameters with the recording of data delivered by the injection molding machine. The process data are stored for each injection cycle. To archive the injection molding experimentation program, two different molds were used with the injection of POM, ABS and PA66-GF30 polymers. In addition to the experimental platform, three case studies were analyzed at production sites of three industrial partners.
The outputs of the P4P study are mainly three methodological components provided to industrial partners to improve their productivity and reduce cost for injection molding:
Comprehensive procedure for process qualification and validation (P4P procedure) collecting the studies of scientific molding with specific improvements based on the experimentation and on the design of experiment
Optimization principle based on Data Mining applied on the injection molding with significant results indicating the potential of Data Mining
Improvement procedure for injection molding including a prototype of the vertical integration with reference to Industry 4.0 from in-cavity sensors and injection machine to the manufacturing execution system with the use of Data Analytics
By the Process 4 Plastics research, the industrial partners have a set of process improvements for injection molding and guidance to deploy in their industrial areas the first levels of vertical networking with reference to Industry 4.0. The performed research was backed by the industrial practices of partners to get them to evolve with vertical networking and sensor technologies towards Industry 4.0.
A web application was successfully tested to visualize the data and to define notification services. The developed services of the P4P improvement procedure are Data Analytics functionalities with following features: Alert Routing, Remote Access and Responsive Design, Secured Login Page, Injection Machine Management, Data Visualization and Service Management. Further development of services based on Data Analytics are well possible to improve the productivity and to reduce cost for the injection molding. It is one of the most effective answers of the P4P study.
Partenaires académiques: Pascal Bovet, Haute école d'ingénierie et d'architecture de Fribourg