A process knowledge representation approach for decision support in design of complex engineered systems

https://doi.org/10.1016/j.aei.2021.101257Get rights and content

Abstract

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. Current research on the process knowledge representation of decision-making in engineering design has predominantly focused on either mathematical models of individual decisions at the micro-level or organizational models of group decision consensus at the macro-level. However, the management of complexity and uncertainty in the model-based realization of engineered systems is critical to achieving rational, comprehensive, and robust decisions, especially in terms of knowledge-intensive design. The efficiency and effectiveness of decisions in system design are intrinsically linked to the process, knowledge, and system concepts involved, necessitating a more flexible and systematic decision process representation scheme that supports both the management of complexity and uncertainty. Hence, in this paper, we propose a decision-centric design process representation scheme named the Phase-Event-Information X (PEI-X) diagram and its corresponding systematic design guidance method for designing decision workflows. Using the proposed method, designers have the ability to (1) model hierarchical decision processes that cover vertical and horizontal interaction patterns, and (2) exploit the synthesis of the “Formulating-Identifying-Reusing-Exploring” iterative process to extend the understanding and prediction of decision process behaviors in design. We achieve the aforesaid abilities through the implementation of a knowledge-based design guidance system for collaborative decision support and we demonstrate the efficacy by adopting a specific multi-stage manufacturing process design problem, hot rod rolling system design, and carry out an integrated design of materials, products, and related manufacturing processes.

Introduction

The digital intelligent manufacturing industry is right to pay more attention to the effective management and strategic methods of intellectual capital, especially capturing and reusing knowledge of the in-context decision process to realize complex engineered systems [1], [2]. Following the philosophy of decision-based design (DBD), the decision-making process has far-reaching consequences in the early stages of system design [3], [4]. For complex engineered systems (e.g., automobiles, aircraft, and ships) in particular, whose designs are typically characterized by being multi-stage, multi-level, and multi-disciplinary [5], [6], we assert that great care must be taken to identify the decision problem of system design correctly and to partition and plan the system design flexibly, as well as provide an appropriate framework to support the guidance and management of decision processes.

A complex system is defined as “a system for which tightly coupled interacting phenomena yield a collective behavior” [7]. Thus, the interactive coupling and the interdependence of components and processes are the typical characteristics of complex engineered systems [5], which also brings many design challenges to the problem-solving process with the necessary management of complexity and uncertainty [8], [9]. As Simon states, design process strategies can affect efficiency when using design resources and the nature of a final design [10]. An appropriately structured design process can improve the development process and performance of a single product or even an entire product family [11]. Designing design-processes, namely the systematic analysis and synthesis of design processes, is an essential ingredient for the strategic development of products. Panchal et al. [12], [13] highlighted the significance and requirements of leveraging knowledge related to the designing design process in the product life cycle management view. Various typical process modeling languages and commercially available software, which support the decomposition and planning of design activities, have been developed based on the basic demands of representing process-related knowledge [8], [14], [15]. It seems that these methods and tools are useful for the decision-making of activity organizing such as time utilization, task precedence, and resource allocation [14], [16]. However, due to the sole focus on business process modeling, it is difficult for human designers to obtain reasonable representation and practical support for determining when to make decisions, what decisions to make, and how to make decisions in system design. Some efforts have provided solutions to facilitate the representation of decision models in the design processes, such as domain-specific model languages for the design rationale of a decision and the Decision Model and Notation (DMN) standard published by the Object Management Group [2], [15].

In the model-based realization of complex engineered systems, the interactions and iterations between various decision activities and stakeholders increase the complexity of designing design processes [8], [13], [17]. Hence, a large amount of research has been carried out on representing and documenting the problem-solving process to decouple the complexity and increase the understanding of collaborative process behaviors in design [3]. Examples of this include the issue-based information system (IBIS) model for design rationale [18], a reflection-in-action process knowledge representation framework for design concept development [19], and a shared design thinking process model (S-DTPM) to record the individual and the team mentality of design intent [20]. The commonality of these efforts is the extraction of tacit knowledge about know-why and know-how embedded in the design process. Various knowledge representation schemes are given to structure the relevant design processes. However, the lack of representation of problem-solving processes from the perspective of DBD makes it difficult for designers to create a shared understanding of the decision-making processes for the design of a product and the process. With this in mind, in this paper, we propose a novel process knowledge representation approach for decision support in designing complex engineered systems. The approach covers a process representation scheme for designing decision processes called the PEI-X diagram, which forms the basis of an integrated management environment for complexity and uncertainty in decision-centric system design. Based on the PEI-X diagram scheme, a Concept-Decision-Knowledge (CDK) framework for the design guidance of decision workflows is presented to help a human designer find satisfying solutions with the features of robustness, flexibility, and modifiability, particularly in the early stages of design.

This paper’s remainder is organized as follows: Section 2 provides a related research literature review to clarify this paper’s research gaps and contributions. In Section 3, a novel knowledge representation scheme for designing decision processes named the PEI-X diagram is presented to respond to the identified requirements. Section 4 provides a CDK framework for designing decision workflows based on the PEI-X diagram, enabling a robust design concept exploration with an integrated decision process model. The proposed method’s efficacy is illustrated by the implementation of the Knowledge-Based Design Guidance System (KBDGS) using a design example associated with the multi-stage manufacturing processes, namely the hot rod rolling system described in Section 5. We end with the closing remarks in Section 6.

Section snippets

Literature review

The design of engineered systems is becoming increasingly complex due to demands for improved life cycle characteristics (e.g., functional diversity and performance enhancement). Several characteristics of complex engineered systems have been identified in [7], [21], including the interconnections and (or) dependencies of subsystems, hierarchical component architecture, and inherent uncertainty, which pose many challenges to effectively representing and managing the decision-making in the

Knowledge representation of decision processes

The detailed requirements for designing decision processes are clarified in this section based on the research gaps identified in Section 2. Consequently, we present a novel knowledge representation of decision processes, namely the PEI-X diagram, including process icons definition and knowledge representation principles.

Design and execution of decision workflows

The PEI-X diagram presents a domain-independent process knowledge representation approach for the decision-centric design process. Large amounts of the system information and knowledge associated with the decision-making are engaged in the PEI-X diagram, which can be applied to assist a human designer in finding satisfying solutions. This section discusses how the system information and design knowledge can be incorporated into decision processes using the PEI-X diagram.

Case study

As a significant component of networked manufacturing systems, a multi-stage manufacturing process (MMP) consists of multiple manufacturing stations and unit operations, commonly encountered in product matching, assembly, and material processing [61], [62]. In this section, the PEI-X diagram’s utility and design decision guidance based on the CDK framework are illustrated via a specific MMP design problem, namely the hot rod rolling system (HRRS), the critical segments of the steel

Conclusions

Process knowledge is of considerable significance to the digitalization and intelligentization of the manufacturing industry. In the model-based realization of complex engineered systems, the management of complexity and uncertainty in design puts forward higher requests for modeling and representing decision processes since it is capable of improving design efficiency and efficiency. Thus, in this paper we propose the PEI-X diagram for modeling decision-centric design processes and its

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

Ru Wang gratefully acknowledges the Project funded by China Postdoctoral Science Foundation [Grant 2018M640073]. Janet K. Allen and Farrokh Mistree gratefully acknowledge the John and Mary Moore Chair and the L.A. Comp Chair at the University of Oklahoma. Guoxin Wang gratefully acknowledges the National Ministries Projects of China (Grant JCKY2017207A001). This paper is an outcome of the International Systems Realization Partnership between the Institute for Industrial Engineering @ The Beijing

Glossary

Complexity Management
Manage interactions between various decisions and associated activities in designing decision processes.
Uncertainty Management
Manage uncertainties derived from input parameters, model parameters and structures, and uncertainty propagation in model chains in designing decision processes to minimize the uncertainty impact on the performances of systems.
Efficiency
A measurement of the swiftness with which information is generated can be used by a designer to decide.
Effectiveness

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