A case analysis for PEArL: software on wheels

The problems and difficulties in complex systems design are more subjective and ambiguous than commonly acknowledged and soft systems tools and frameworks can help to address gaps in knowledge and support judgement throughout the design process. This paper describes the application of the PEArL framework, an intellectual device based on systemic principles, to support a team in a complex product environment manage the ‘soft’ challenges in complex systems design. The PEArL framework facilitated the creation and management of inter-disciplinary relationships in a pressurised business environment and provided a structure for validation of the way in which the design process had been undertaken

The Integrated Realization of Materials, Products and Associated Manufacturing Processes

Problem: A materials design revolution is underway in the recent past where the focus is to design (not select) the material microstructure and processing paths to achieve multiple property or performance requirements that are often in conflict. The advancements in computer simulations have resulted in the speeding up of the process of discovering new materials and has paved way for rapid assessment of process-structure-property-performance relationships of materials, products, and processes. This has led to the simulation-based design of material microstructure (microstructure-mediated design) to satisfy multiple property or performance goals of the product/process/system thereby replacing the classical material design and selection approaches. The foundational premise for this dissertation is that systems-based materials design techniques offer the potential for tailoring materials, their processing paths and the end products that employ these materials in an integrated fashion for challenging applications to satisfy conflicting product and process level property and performance requirements. The primary goal in this dissertation is to establish some of the scientific foundations and tools that are needed for the integrated realization of materials, products and manufacturing processes using simulation models that are typically incomplete, inaccurate and not of equal fidelity by managing the uncertainty associated. Accordingly, the interest in this dissertation lies in establishing a systems-based design architecture that includes system-level synthesis methods and tools that are required for the integrated design of complex materials, products and associated manufacturing processes starting from the end requirements. Hence the primary research question: What are the theoretical, mathematical and computational foundations needed for establishing a comprehensive systems-based design architecture to realize the integrated design of the product, its environment, manufacturing processes and material as a system? Major challenges to be addressed here are: a) integration of models (material, process and product) to establish processing-structure-property-performance relationships, b) goal-oriented inverse design of material microstructures and processing paths to meet multiple conflicting performance/property requirements, c) robust concept exploration by managing uncertainty across process chains and d) systematic, domain-independent, modular, reconfigurable, reusable, computer interpretable, archivable, and multi-objective decision support in the early stages of design to different users. Approach: In order to address these challenges, the primary hypothesis in this dissertation is to establish the theoretical, mathematical and computational foundations for: 1) forward material, product and process workflows through systematic identification and integration of models to define the processing-structure-property-performance relationships; 2) a concept exploration framework supporting systematic formulation of design problems facilitating robust design exploration by bringing together robust design principles and multi-objective decision making protocols; 3) a generic, goal-oriented, inverse decision-based design method that uses 1) and 2) to facilitate the systems-based inverse design of material microstructures and processing paths to meet multiple product level performance/property requirements, thereby generating the problem-specific inverse decision workflow; and 4) integrating the workflows with a knowledge-based platform anchored in modeling decision-related knowledge facilitating capture, execution and reuse of the knowledge associated with 1), 2) and 3). This establishes a comprehensive systems-based design architecture to realize the integrated design of the product, its environment, manufacturing processes and material as a system. Validation: The systems-based design architecture for the integrated realization of materials, products and associated manufacturing processes is validated using the validation-square approach that consists of theoretical and empirical validation. Empirical validation of the design architecture is carried out using an industry driven problem namely the ‘Integrated Design of Steel (Material), Manufacturing Processes (Rolling and Cooling) and Hot Rolled Rods (Product) for Automotive Gears’. Specific sub-problems are formulated within this problem domain to address various research questions identified in this dissertation. Contributions: The contributions from the dissertation are categorized into new knowledge in four research domains: a) systematic model integration (vertical and horizontal) for integrated material and product workflows, b) goal-oriented, inverse decision support, c) robust concept exploration of process chains with multiple conflicting goals and d) knowledge-based decision support for rapid and robust design exploration in simulation-based integrated material, product and process design. The creation of new knowledge in this dissertation is associated with the development of a systems-based design architecture involving systematic function-based approach of formulating forward material workflows, a concept exploration framework for systematic design exploration, an inverse decision-based design method, and robust design metrics, all integrated with a knowledge-based platform for decision support. The theoretical, mathematical and computational foundations for the design architecture are proposed in this dissertation to facilitate rapid and robust exploration of the design and solution spaces to identify material microstructures and processing paths that satisfy conflicting property and performance for complex materials, products and processes by managing uncertainty

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Powertrain Assembly Lines Automatic Configuration Using a Knowledge Based Engineering Approach

Technical knowledge and experience are intangible assets crucial for competitiveness. Knowledge is particularly important when it comes to complex design activities such as the configuration of manufacturing systems. The preliminary design of manufacturing systems relies significantly on experience of designers and engineers, lessons learned and complex sets of rules and is subject to a huge variability of inputs and outputs and involves decisions which must satisfy many competing requirements. This complicated design process is associated with high costs, long lead times and high probability of risks and reworks. It is estimated that around 20% of the designer’s time is dedicated to searching and analyzing past available knowledge, while 40% of the information required for design is identified through personally stored information. At a company level, the design of a new production line does not start from scratch. Based on the basic requirements of the customers, engineers use their own knowledge and try to recall past layout ideas searching for production line designs stored locally in their CAD systems [1]. A lot of knowledge is already stored, and has been used for a long time and evolved over time. There is a need to retrieve this knowledge and integrate it into a common and reachable framework. Knowledge Based Engineering (KBE) and knowledge representation techniques are considered to be a successful way to tackle this design problem at an industrial level. KBE is, in fact, a research field that studies methodologies and technologies for capturing and re-using product and process engineering knowledge to achieve automation of repetitive design tasks [2]. This study presents a methodology to support the configuration of powertrain assembly lines, reducing design times by introducing a best practice for production systems provider companies. The methodology is developed in a real industrial environment, within Comau S.p.A., introducing the role of a knowledge engineer. The approach includes extraction of existing technical knowledge and implementation in a knowledge-based software framework. The macro system design requirements (e.g. cycle time, production mix, etc.) are taken as input. A user driven procedure guides the designer in the definition of the macro layout-related decisions and in the selection of the equipment to be allocated within the project. The framework is then integrated with other software tools allowing the first phase design of the line including a technical description and a 2D and 3D CAD line layout. The KBE application is developed and tested on a specific powertrain assembly case study. Finally, a first validation among design engineers is presented, comparing traditional and new approach and estimating a cost-benefit analysis useful for future possible KBE implementations