Moharram Challenger TT. asst. research professor

Research Moharram Challenger

Research team

Expertise

Software Engineering; Model-driven Engineering; Model-based System Engineering; Domain-specific Languages; Multi-agent Systems; Intelligent Agents;

Prediction & management of the 'reverse' remanufacturing supply chain (REMASC). 01/07/2024 - 30/06/2028

Abstract

Driven by sustainability, companies and customers alike are looking to set up a closed loop supply chain whereby products are returned to be 'remanufactured' (i.e., process to retain the usefulness of the product or the components). These product returns trigger a reverse manufacturing supply chain (REMASC). Companies are in need for tools that support both operational aspects, as well as strategic decision making related to the management of its remanufacturing activities. In this SBO project, Flanders Make will develop tools linked to three innovation goals: 1. To support strategic decision making related to the characteristics of product(family)-customer relationships required for product returns to be(come) a profitable business model. To this end REMASC will analyse and propose rewarding strategies. 2. To forecast (based on product type and customer profiles) the volume, reason for return, … of these product returns in order to organize the product inbound. It will provide tools to trigger fast decision making related to the quality of the product returned, i.e., deciding on 'waste' vs. defining the steps needed for the actual remanufacturing of the collected 'core'. 3. To efficiently manage the remanufacturing of returned products. This includes task generation, planning and scheduling of the remanufacturing activities; inventory management; needs for quality assessment and the potential integration of these remanufacturing activities in a classical manufacturing site. Enabled by industry 4.0 principles (such as digital product passports) and driven by sustainability, tools for managing the reverse manufacturing supply chain will benefit both end-users (OEM, TIER-1, TIER-2 and material providers), as well as service solutions providers (supply chain support, data analysis, logistics, ERP/MES integrators, operator support systems).

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  • Research Project

Nexor - Cyber-Physical Systems for the Industry 4.0 era 01/01/2021 - 31/12/2026

Abstract

The fourth industrial revolution (Industry 4.0 as it is commonly referred to) is driven by extreme digitalization, enabled by tremendous computing capacity, smart collaborating machines and wireless computer networks. In the last six years, Nexor — a multi-disciplinary research consortium blending expertise from four Antwerp research labs — has built up a solid track record therein. We are currently strengthening the consortium in order to establish our position in the European eco-system. This project proposal specifies our 2021 - 2026 roadmap, with the explicit aim to empower industrial partners to tackle their industry 4.0 challenges. We follow a demand driven approach, convincing industrial partners to pick up our innovative research ideas, either by means of joint research projects (TRL 5—7) or via technology licenses.

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  • Research Project

Dotation for the structural collaboration with Flanders Make. 01/01/2021 - 31/12/2024

Abstract

Flanders Make's mission is to strengthen the international competitiveness of the Flemish manufacturing industry on the long term through industry-driven, precompetitive, excellent research in the field of mechatronics, product development methods and advanced production technologies and by maximizing valorisation in these areas.

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  • Research Project

Modelling and Simulation of Cyber-Physical Systems 01/08/2020 - 31/07/2025

Abstract

Cyber-Physical Systems (CPS) consist of tightly integrated and coordinated computational and physical elements. They focus on interaction with highly uncertain environment with the limited resources. By introducing IoT and Industry 4.0, the CPSs are connected to each other to meet the emerging more complex requirements. These interconnected CPSs constitute complex systems called Cyber-physical System of Systems (CPSoS) in which there may be emergent behaviour, lack of central control, dynamic structure, and need for autonomy. Therefore, CPSoS cannot be designed and managed using theories and tools from only one single domain. A key point in a CPSoS is to obtain knowledge out of the information, collected by monitoring the environment. This knowledge can improve the control and feedback mechanism. This capability leads to the next generation of CPSoS with timely and more accurate decisions and actions called Smart CPSoS (sCPSoS). These smart systems can analyze a situation and make decisions based on the available data in an adaptive manner, to perform smart actions. However, such intelligent techniques put yet additional complexity to the systems, specifically to the computational part. Thus, these systems of the future have a high complexity (both from structural and behavioural points of view) throughout their lifecycle, including modeling & simulation, design & implementation, validation & verification, deployment, execution & monitoring, and maintenance & evolution. There is a need for new methodologies, architectures, process models, and frameworks to tackle this complexity. To overcome the challenges in the development and operation of sCPSoS, modeling techniques can be used for different aspect and various levels of abstraction in the system. To this end, appropriate modeling paradigms should be chosen for each aspect/level. These models and modeling paradigms will be integrated, hence called multi-paradigm modeling (MPM), to represent the whole system. Specifically, the idea is to integrate agent paradigm with the model-based system engineering (MBSE) for both modelling & simulation phase as well as execution and monitoring phase in the lifecycle of sCPSoS. Agent based system engineering (ABSE) uses software intelligent agents to successfully cross-fertilize the fields of systems engineering and artificial intelligence. In this way, the autonomy, dynamic behaviour and smart-ness of sCPSoS can be handled by intelligent agents, integrated with MBSE models.

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  • Research Project

Automatic assisted design for assembly (AssistedDfA_icon). 01/10/2021 - 30/09/2024

Abstract

The design of mechanical assembly products has become a complex task in which design engineers have to rely on Computer Aided Design, software to correctly assess pricing and performance of their product. However, unlike performance and cost, assembly-related knowledge is hard to formalize and current commercial CAD tools do not provide adequate support to evaluate assemblability in an accurate and company specific way. This means companies still have to rely on iterative interactions between the designers and assembly experts, during which the assemblability of a product is evaluated manually based on check lists and expert knowledge. Due to this iterative process, assembly issues in the design result in an increased development cost and time, which is detrimental for company competitiveness. This problem is especially relevant with the ever-increasing complexity of assembly products and the current tendency of mixing human operators and collaborative robots (cobots) in the assembly processes, in which design flaws become more likely, further emphasizing the need for supporting tools. The goal of the project is to substantially reduce the time to market and development cost of mechanical assembly products by incorporating automated assemblability evaluation in the early stages of product design. This goal will be achieved by investigating and implementing algorithmic methods capable of interact with the designer by means of CAD software and 3D visualization tools. By allowing the designer to evaluate the assemblability in the early stages, the number of design re-iterations will be strongly reduced. AnSyMo Group (MICSS Lab) in Department of Computer Science, University of Antwerp is responsible for work package 2. The goal of this work package is to address the lack of a standardized model to capture assembly knowledge by developing a framework and methodology to formalize assembly information into a knowledge-base. AnSyMo and CodesignS will collect requirements from the manufacturing partners (Daikin, Voxdale, Alberts and Siemens) with the aim to extend the meta-model developed in the PACo SBO project to enable the formalization of assembly knowledge across the three levels of the technical strategy. Additionally, AnSyMo will develop a programmatic interface (API) to make the knowledge-base accessible from within a CAD environment and provide a Domain-specific Language (DSL) to define custom DfA rules programmatically (via the API).

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  • Research Project

Intelligent Software Agents and Multi-agent Systems for the Lifecycle of Smart Cyber-physical System-of-Systems. 01/10/2020 - 30/09/2024

Abstract

AnSyMo (Antwerp Systems and Software Modeling) is a Computer Science research group investigating foundations, techniques, methods and tools for the design, analysis and maintenance of software-intensive systems. MICSS (Modeling Intelligente Complex Software and Systems) is a lab in AnSyMo group dedicated to the modeling of intelligent systems such as smart cyber-physical system of systems using intelligent agents and model driven engineering techniques. Cyber-Physical Systems (CPS) consist of tightly integrated and coordinated computational and physical elements. They are the evolution of embedded systems to a higher level of complexity, focusing on the interaction with highly uncertain physical environments (such as human interaction or wear & tear of devices). In these systems, embedded computers and networks monitor (through sensors) and control (through actuators) the physical processes, usually with feedback loops where physical processes and computations affect each other. The computational part of these systems plays a key role and needs to be developed in a way that can handle uncertain situations with the limited resources (including computational resource, memory resource, communication resource, and so on), mostly in real-time. With IoT and Industry 4.0 maturing, these systems are getting interconnected and making a complex larger system called the Cyber-physical System of Systems (CPSoS) to serve in more sophisticated tasks. In these systems, CPSs are working as part of a large system that is spatially distributed, has no central control, has autonomous subsystems, is dynamically configured, has emergent behaviour, and is continually evolving, even at runtime. A key point in CPSoS is to obtain knowledge out of the information that is collected by distributed monitoring of the environment, using artificial intelligence techniques. This knowledge can improve the control and feedback mechanism. Further, these capabilities lead to the smart systems of the future with timely and more accurate decisions and actions, called Smart CPSoS (sCPSoS), which can help to address a number of social, industrial, and environmental issues. This project aims to address the challenges of sCPSoS using intelligent agents and model-driven engineering techniques.

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  • Research Project