In recent years, the area of Product Design focusing on the Product's Life Cycle, from conception through disposal, has been an increasing area of research, in academia and industry. It provides a challenging, yet relatively unexplored, area of research for scientists and engineers from multiple disciplines. It is also an area of great interest to industry, with the refocusing of business models from increasing product performance to increasing corporate profitability while reducing expenditures. This paradigm shift has spawned multiple areas of research in Life-Cycle Design, as evidenced by the diversity in the topics of the papers submitted for this special issue of AI EDAM. We have collected five papers that present different areas of Life-Cycle Design; each provides a unique perspective on Life Cycle Design within the broad domain of Artificial Intelligence.

The problem addressed in this paper is that design decisions can have a propagation effect spanning multiple life-phases influencing life-cycle metrics such as cost, time, and quality. It introduces a computational framework of a “Knowledge of life-cycle Consequences (KC) approach” aimed at allowing designers to foresee and explore effectively unintended, solution specific life-cycle consequences (LCCs) during solution synthesis. The paper presents a phenomena model describing how LCCs are generated from two fundamentally different conditions: noninteracting and interacting synthesis decision commitments. Based on this understanding, the KC approach framework has been developed and implemented as a Knowledge-Intensive CAD (KICAD) tool named FORESEE. The framework consists of three frames: an artefact life modelling frame, an operational frame, and an LCC knowledge modelling frame. This paper focuses on the knowledge modelling frame, composed basically of synthesis elements, consequence inference knowledge, and consequence action knowledge. To evaluate the influence of design decision consequences on artefact life-phases, cost, time and quality performance measures are used within the frame. Using these metrics, the life-cycle implications of a decision can be instantly updated and fully appreciated. An evaluation of the approach was carried out by applying FORESEE to thermoplastic component design. The results provide a degree of evidence that the approach integrates the activity of component design synthesis with the activity of foreseeing artefact life issues including fluctuations in life-cycle metrics. This makes the approach fundamentally different from the conventional approach in which first a candidate design solution is generated and then, at a penalty of extra time, an analysis of the solution for conflicts with artefact life issues is carried out. The framework thus provides a significant step towards the realization of a “Design Synthesis for Multi-X” approach to component design, although further work is required to exploit practically its utilization.

The value of comprehensive rationale for documenting a design has long been recognized. However, designers rarely produce detailed rationale in practice because of the substantial time investment required. Efforts to support the acquisition of rationale information have focused on languages and tools for structuring the acquisition process, but still require substantial involvement on the part of the designer. This paper describes an experimental system, the Rationale Construction Framework (RCF), that acquires rationale information for the detailed design process without disrupting a designer's normal activities. The underlying approach involves monitoring designer interactions with a commercial computer-assisted design (CAD) tool to produce a rich process history. This history is subsequently structured and interpreted relative to a background theory of design metaphors that enable explanation of certain aspects of the design process. The framework provides an environment that can acquire rich, meaningful rationale information in a time- and cost-effective manner, with minimal disruption to the designer.

Current methods to develop standard Architecture, Engineering, Construction (AEC) product models focus on the definition of product model semantics without concurrent and formal consideration of the engineering analyses that such models must support, or formal consideration of the requirements for sharing information between applications. We present two case studies that demonstrate a service to extract data from product models and provide inputs to component analysis applications. The service was validated in a proof-of-concept application called the Internet Broker for Engineering Services (IBES) that extracts information for component analysis from product models that are external to the application and accessed across the Internet. IBES was tested for two research cases. The product model for the first case, control valve selection is based on STEP Application Protocol 227. The product model for the second case, control valve diagnosis, specifies additional semantics that support the operations and maintenance (O&M) phase of the facility life cycle. The cases offer evidence that large standard data models can support routine analyses for control valves. However, the amount of shared information between the case applications is small and is largely dependent upon the concurrence of component behaviors that are necessary to model analysis. The IBES reference model and reasoning to support information extraction was consistent for both cases. This consistency suggests that it is possible to define a general set of computational methods that integrate project information models with external component analysis applications across the product life cycle. We argue that enabling a web-based link between product models and applications requires a set of capabilities, including bi-directional communication between separated data and analysis nodes, query generation, data translation, and validation of data extracted from semistandard models. We discuss the tentative implication that minimal shared information calls into question the assumption that large core product models will work effectively in practice.

Environmental issues require a new manufacturing paradigm because the current mass production and mass consumption paradigm inevitably cause them. We have already proposed a new manufacturing paradigm called the “Post Mass Production Paradigm (PMPP)” that advocates sustainable production by decoupling economic growth from material and energy consumption. To realize PMPP, appropriate planning of a product life cycle (design of life cycle) is indispensable in addition to the traditional environmental conscious design methodologies. For supporting the design of a life cycle, this paper proposes a life-cycle simulation system that consists of a life-cycle simulator, an optimizer, a model editor, and knowledge bases. The simulation system evaluates product life cycles from an integrated view of environmental consciousness and economic profitability and optimizes the life cycles. A case study with the simulation system illustrates that the environmental impacts can be reduced drastically without decreasing corporate profits by appropriately combining maintenance, reuse and recycling, and by taking into consideration that optimized modular structures differ according to life-cycle options.

Concurrent Engineering needs a series of measures (or measurement criteria) that are distinct to each process, and a set of metrics to check (and validate) the outcome when two or more of the life-cycle processes are overlapped or required to be executed in parallel. Because product realization involves concurrent processes that occur across multiple disciplines and organizations, appropriate measures and the methods of qualifying metrics are essential. Inevitably, such concurrent processes generate design conflicts among multiple life-cycle concerns. Individual assurances of satisfying life-cycle design criterion (one at a time) do not capture the most important aspect of Concurrent Engineering—the system perspective—meaning achieving a well-balanced trade-off among the different life-cycle design measures. While satisfying life-cycle design measures in a serial manner only those, which are not in conflict, are occasionally met. The paper first describes a set of life-cycle measures and metrics and explains how those could be used for gaining operational excellence. Second, this paper provides an insight into the mechanisms (such as knowledge-based systems, rule-based simulation, and rule-based optimization) to ensure an effective trade-off across different life-cycle measures, customer requirements, and their inclusion into a product design, development, and delivery (PD3) process.