Methodology for capturing and formalizing DFM Knowledge

Design for manufacturing (DFM) practices lead to more competitive products from the point of view of cost, development time and quality. However, the success of considering manufacturing issues during design process would be higher if manufacturing information was more readily available and designers needed less experience to select information relevant to DFM.     

A knowledge support approach for the preliminary design of platform-based products in Engineering-To-Order manufacturing

A product platform is a design approach for meeting the demand for customizable products. Traditional knowledge-based technologies or systems lack flexibility in supporting both configuration and parameter design of platform-based products. In many cases, customers’ requirements and knowledge models both contain incomplete information, and there are complex relations among various solutions, functions and solution parameters in Engineering-To-Order (ETO) products. A knowledge model for the preliminary design of ETO products is presented in this paper, and linkages are established between configuration design knowledge and parameter design procedures. The basis of the knowledge model is the Extended Function-Solutions (EFS) tree, from which design case trees, design modules, constraint checking rules, and module interface templates derive. A corresponding knowledge retrieval and reuse strategy is also presented. It uses an improved fuzzy information axiom to search for the optimal configuration with incomplete information. The parameter design process model of new products then can be generated based on the optimal configuration. The case study demonstrates the knowledge modeling, retrieval and reuse for the preliminary design of open-type crank presses. Moreover, the effectiveness of the methodology is discussed by analyzing the verification approach and the satisfaction of customers’ requirements.     

A hypernetwork-based approach to collaborative retrieval and reasoning of engineering design knowledge

Complex product development increasingly entails creation and sharing of design knowledge in a collaborative and integrated working environment. In this context, it has become a central issue to address the multifaceted feature of design knowledge for such a collaborative knowledge sharing scheme. This paper proposes a hypernetwork-based approach to explicitly capturing the relationships between various elements in a multifaceted knowledge representation. Specifically, a knowledge hypernetwork model is constructed, which is composed of a designer network, a product network, an issue network and a knowledge unit network. The relationships between various nodes from different networks are identified and defined according to specific node properties. In addition, topological characteristics of the hypernetwork structure are analyzed together with the statistical indicators. Based on this model, the Bayesian approach is adopted to conduct the collaborative reasoning process whereby knowledge elements relevant to the current design task are recommended according to the issues to be resolved and the current design context. A case study conducted in this work shows that the proposed approach is effective in capturing the complex relationships between multi-faceted knowledge elements and enables collaborative retrieval and reasoning of knowledge records.     

An approach to capturing and reusing tacit design knowledge using relational learning for knowledge graphs

Tacit design knowledge plays an important role in the process of product design and is a valuable knowledge asset for enterprises. In terms of the characteristics of tacit rational design knowledge, this paper puts forward a scientific hypothesis and approach on capturing and reusing tacit rational design knowledge. The presented approach represents the observable design result facts of products using design knowledge graphs. A design issue-solving oriented knowledge graph model is presented, where directed relation edges represent design issues, and nodes stand for design solutions. When a new design solutions requirement needs to be searched, tacit design knowledge can be reused by relational learning for the constructed design knowledge graphs. In relational learning, the design knowledge graph is converted into a three-order tensor, where two modes are solution nodes, and the third mode holds the issue relations. Then, a tensor factorization approach is employed to calculate the latent features between design solutions for an issue relation. As a result, a score vector to represent the existence of issue-solution relations can be obtained. By sorting the scores in descending order, we may select the solution node with the highest score as the design solution to be searched. Finally, a stamping die design case study is provided. The case study shows that the proposed approach is feasible, and effective, and has better flexibility, scalability and efficiency than CBR methods.     

A part affordance-based approach for capturing detailed design knowledge

Design knowledge reuse is widely accepted as an effective strategy for designers to develop robust artifacts with less time and lower cost. However, there has been very little research on how to help designers capture detailed design knowledge for reuse. As a result, most detailed design knowledge still has to remain in designers’ memories as tacit knowledge, which can easily get lost due to oblivion or the mobility of designers. Therefore, this paper attempts to develop a part affordance-based approach for externalizing and capturing detailed design knowledge for effective reuse. It first introduces a part model for representing the detailed design-related information. Based on the relational theory for design, the concept, part affordance, is then employed to help designers externalize and capture various lifecycle factors that are implicit in a detailed design. Based on the affordance constraint axiom, a systematic approach is then proposed for deriving tacit design knowledge from captured part affordances through the analysis of extreme working situations. The proposed approach has been implemented as the Design Knowledge-Capturing System (DKCS). A fixture design case has been employed to illustrate the proposed approach.     

Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation

Cloud-based design manufacturing (CBDM) refers to a service-oriented networked product development model in which service consumers are enabled to configure, select, and utilize customized product realization resources and services ranging from computer-aided engineering software to reconfigurable manufacturing systems. An ongoing debate on CBDM in the research community revolves around several aspects such as definitions, key characteristics, computing architectures, communication and collaboration processes, crowdsourcing processes, information and communication infrastructure, programming models, data storage, and new business models pertaining to CBDM. One question, in particular, has often been raised: is cloud-based design and manufacturing actually a new paradigm, or is it just “old wine in new bottles”? To answer this question, we discuss and compare the existing definitions for CBDM, identify the essential characteristics of CBDM, define a systematic requirements checklist that an idealized CBDM system should satisfy, and compare CBDM to other relevant but more traditional collaborative design and distributed manufacturing systems such as web- and agent-based design and manufacturing systems. To justify the conclusion that CBDM can be considered as a new paradigm that is anticipated to drive digital manufacturing and design innovation, we present the development of a smart delivery drone as an idealized CBDM example scenario and propose a corresponding CBDM system architecture that incorporates CBDM-based design processes, integrated manufacturing services, information and supply chain management in a holistic sense.     

Fostering continuous innovation in design with an integrated knowledge management approach

In the global competition, companies are propelled by an immense pressure to innovate. The trend to produce more new knowledge-intensive products or services and the rapid progress of information technologies arouse huge interest on knowledge management for innovation. However the strategy of knowledge management is not widely adopted for innovation in industries due to a lack of an effective approach of their integration. This study aims to help the designers to innovate more efficiently based on an integrated approach of knowledge management. Based on this integrated approach, a prototype of distributed knowledge management system for innovation is developed. An industrial application is presented and its initial results indicate the applicability of the approach and the prototype in practice.     

Simulated annealing with auxiliary knowledge for process planning optimization in reconfigurable manufacturing

In this paper, three simulated annealing based algorithms that exploit auxiliary knowledge in different ways are devised and employed to handle a manufacturing process planning problem for reconfigurable manufacturing. These algorithms are configured based on a generic combination of the simulated annealing technique with; (a) heuristic knowledge, and (b) metaknowledge. Capabilities of the implemented algorithms are tested and their performances compared against a basic simulated annealing algorithm. Computational and optimization performances of the implemented algorithms are investigated and analyzed for two problem sizes. Each problem size consists of five different forms of a manufacturing process planning problem. The five forms are differentiated by five alternative objective functions. Experimental results show that the implemented simulated annealing algorithms are able to converge to good solutions in reasonable time. A computational analysis indicates that significant improvements towards a better optimal solution can be gained by implementing simulated annealing based algorithms that are supported by auxiliary knowledge.     

An integrated form-feature-based design system for manufacturing

Much of the knowledge that is applied in or communicated between design and manufacturing activities is primarily shape based or shape indexed. Previous attempts to acquire and organize shape knowledge have been mostly concentrated on feature recognition from solid models, group technology (GT) coding schemes, and feature-based modeling. This paper presents the development of an efficient form-feature-based modeling system, and addresses the important issue of utilizing feature information for manufacturing, which has not been extensively discussed by previous work. In this paper we first present a Euler operator-based approach for efficient and effective form-feature encoding and manipulation in a feature-based design environment. Subsequently, a hybrid representation scheme called enhanced CSG tree of feature (ECTOF), which integrates feature model with solid model in a tree structure, is discussed. A feature interference resolution methodology to maintain the correct and consistent feature information in an ECTOF is also deliberated. Finally, we present a machinability-checking module, which employs global accessibility criteria to analyze a feature's machinability on a three-axis machining center. By developing feature interference resolving and machinability testing techniques and integrating with an efficient feature-based design system, this research makes the development of an integrated feature-based design and manufacturing system possible.     

A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing

The Industrial Internet of Things (IIoT) interconnects a large number of interconnected sensors, actuators, and edge computing devices in the manufacturing systems, where the massive data collected in the manufacturing process has the characteristics of multi-dimensional, heterogeneous, and time series. An effective data representation manner, which can fuse such complex information and enable cognitive manufacturing decision-making from a global perspective, is necessary and challenging. To solve this issue, this paper proposes a knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing and applies it in a Cyber-Physical Production System (CPPS) scenario. Based on the digital thread of manufacturing process data, a multi-layer manufacturing knowledge graph is established, including device sensing data, production processing data, and business processing data. With the established knowledge graph, a cognition-driven approach is proposed with a perception-cognition dual system, which achieves perception analysis and cognition decision-making in the resource allocation of the manufacturing process. Finally, responding to the orders of personalized products in a workshop is taken as an illustrative example. The performance of allocating resources of workshop devices under dynamic demand changes shows the advantages of the proposed approach. The proposed manner will lay the foundation for a human-like cognition for processing massive real-time industrial information in CPPS, thus paving a pathway towards the era of cognitive manufacturing.     

Knowledge-based integrated product design framework towards sustainable low-carbon manufacturing

With a global challenge on the serious ecological problems, low-carbon manufacturing aiming to reduce carbon emission and resource consumption is gaining the ever-increasing attention. Due to the significant impact on the product lifecycle, low-carbon product design is considered as an effective and attractive approach to improve the eco-market trade-off of electromechanical products. Existing low-carbon product design approaches focus on solving specific low-carbon problems, and how to explore and navigate the integrative design space considering low-carbon and knowledge in a holistic perspective is rarely discussed. In response, this paper proposes a knowledge-based integrated product design framework to support low-carbon product development. An ontology-based knowledge modelling approach is put forward to represent the multidisciplinary design knowledge to facilitate knowledge sharing and integration. Subsequently, a function–structure synthesis approach based on case-based reasoning is presented to narrow down the design space to generate suitable design solutions for achieving desired functions. A multi-objective mathematical model is established, and the multi-objective particle swarm optimization is adopted to solve the low-carbon product optimization. Furthermore, a decision-making ranking approach based on the closeness degree is employed to prioritize the potential solutions from Pareto set. Finally, a case study of low-carbon product design of hydraulic machine is demonstrated to show the effectiveness.     

A collaborative system for capturing and reusing in-context design knowledge with an integrated representation model

Current research on design knowledge capture and reuse has predominantly focused on either the codification view of knowledge or the personalisation view of knowledge, resulting in a failure to address designers’ knowledge needs caused by a lack of context of information and insufficient computational support. Precisely motivated by this gap, this work aims to address the integration of these two views into a complete, contextual and trustworthy knowledge management scheme enabled by the emerging collaborative technologies. Specifically, a knowledge model is developed to represent an integrated knowledge space, which can combine geometric model, knowledge-based analysis codes and problem-solving strategies and processes. On this basis, a smart collaborative system is also designed and developed to streamline the design process as well as to facilitate knowledge capture, retrieval and reuse as users with different roles are working on various tasks within this process. An engineering case study is undertaken to demonstrate the idea of collaborative knowledge creation and sharing and evaluate the effectiveness of the knowledge representation model and the collaborative technologies employed. As evidenced in the development and evaluation, the methods proposed are effective for capturing an integrated knowledge space and the collaborative knowledge management system not only facilitates problem-solving using knowledge-based analysis but also supplies in-context tacit knowledge captured from the communications between users throughout the design process.     

Causal design knowledge: Alternative representation method for product development knowledge management

This paper presents a mathematical comparison of procedural knowledge and causal knowledge, and discusses the potential roles and feasibility of causal knowledge across product development knowledge management. Since reuse of knowledge is so important in product development, various knowledge management approaches have been introduced. Most of the product design knowledge is represented by procedural knowledge, which unfortunately requires cumbersome processes to define, and is typically inadequate for representing the kind of knowledge generated during the product development process. A causal knowledge representation, however, can help us to overcome this limitation and is an alternative formalism for representing product design knowledge. In this paper we compare the procedural and causal knowledge representations. We present the mathematical definitions of two knowledge paradigms, then mathematically describe the relationship between the two. Both knowledge paradigms are then compared based on the perspective of knowledge expression, decision alternative representation, reasoning capability, and knowledge cultivation. This paper concludes that causal knowledge representation is superior to procedural knowledge representation based on the four perspectives. Finally, the knowledge systems are modeled using Systems Modeling Language (SysML), and we present a case study that demonstrates the causal knowledge features using a realistic example from industry.     

Adaptability analysis of design for additive manufacturing by using fuzzy Bayesian network approach

The rapid development of Additive Manufacturing (AM) has been conspicuous and appealing towards manufacturing end-use products and components over the past decade. The continual advancement of AM has brought many advantages such as personalization and customization, reduction of material waste, cutting off the existence of special tooling during fabrication, etc. However, the AM approach has its limitations, such as a lack of knowledge of AM process activities and the progressive industrialization of AM, which makes the design process activities unstable, unpredictable, and have a limited effect. The concept of “design for AM (DFAM)” is increasing, which means we have the opportunity to concentrate almost totally on product functioning. Therefore, the entire design paradigm must be revised to accommodate new production capabilities, geometries, and parameters to avoid molding or machine tooling technology constraints. Few studies have attempted to provide systematic and quantitative knowledge of the relationship between these elements and the feasibility of the design process, making it difficult for designers to assess and control AM industrialization. For this reason, DFAM is needed to reform AM from rapid manufacturing to a mainstream manufacturing method. This paper put forward a framework based on the Fuzzy Bayesian Network (FBN) for DFAM decision-making. Twenty impact factors were encapsulated from experts’ experience and existing literature to investigate the potential adaptability of DFAM. The proposed approach uses expert knowledge and Fuzzy Set Theory (FST) presented with Triangular Fuzzy Numbers (FFN) to perceive the uncertainties. The Bayesian Network (BN) captures the causal relationships and dependencies among the impact components and analyzes the DFAM adaptability for robust probabilistic reasoning. A robot arm claw was used to show the effectiveness of our approach. The results showed that FBN could be used to guide DFAM adaptability in the manufacturing industry.     

A knowledge engineering approach to developing mindtools for context-aware ubiquitous learning

Recent developments in computing and mobile technologies have enabled the mobile and ubiquitous learning approach, which situates students in an environment that combines real-world and digital-world learning resources. Although such an approach seems to be innovative and interesting, several problems have been revealed when applying it to practical learning activities. One major problem is owing to the lack of proper learning strategies or tools that can guide or assist the students to learn in such a complex learning scenario. Students might feel excited or interested when using the mobile devices to learn in the real world; however, their learning achievements could be disappointing. To cope with this problem, in this study, a knowledge engineering approach is proposed to develop Mindtools for such innovative learning scenarios. Experimental results from a natural science course of an elementary school show that this innovative approach not only enhances learning motivation, but also improves the learning achievements of the students.     

A design methodology for form-based knowledge reuse and representation

Paper forms are regularly used for collecting and disseminating knowledge in offices; they are a natural way of eliciting requirements of knowledge workers. Many organizations have implemented a groupware system to integrate the organizational knowledge and support knowledge creation. However, design methods for flexible form-based knowledge reuse and representation are limited. We developed a methodology based on the enhanced cognitive fit theory; it utilizes factoring and synthesis principles to manipulate form-based knowledge. The methodology was articulated using the design science research methodology. A prototype embedded methodology was built to support a knowledge worker in knowledge creation and reuse in a high tech firm. The resulting system allowed flexible form-based knowledge creation that was useful for problem solving and exploiting opportunities. Implications and conclusions are discussed.     

A new DFM approach to combine machining and additive manufacturing

Design for manufacturing (DFM) approaches aim to integrate manufacturability aspects during the design stage. Most of DFM approaches usually consider only one manufacturing process, but product competitiveness may be improved by designing hybrid modular products, in which products are seen as 3-D puzzles with modules realized individually by the best manufacturing process and further gathered. A new DFM system is created in order to give quantitative information during the product design stage of which modules will benefit in being machined and which ones will advantageously be realized by an additive process (such as Selective Laser Sintering or laser deposition). A methodology for a manufacturability evaluation in case of a subtractive or an additive manufacturing process is developed and implemented in a CAD software. Tests are carried out on industrial products from automotive industry.     

Data-driven generative design for mass customization: A case study

Generative design provides a promising algorithmic solution for mass customization of products, improving both product variety and design efficiency. However, the current designer-driven generative design formulates the automated program in a manual manner and has insufficient ability to satisfy the diverse needs of individuals. In this work, we propose a data-driven generative design framework by integrating multiple types of data to improve the automation level and performance of detail design to boost design efficiency and improve user satisfaction. A computational workflow including automated shape synthesis and structure design methods is established. More specifically, existing designs selected based on user preferences are utilized in the shape synthesis for creating generative models. For structural design, user-product interaction data gathered by sensors are used as inputs for controlling the spatial distributions of heterogeneous lattice structures. Finally, the proposed concept and workflow are demonstrated with a bike saddle design with a personalized shape and inner structures to be manufactured with additive manufacturing.     

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.