Syed Imran Shafiq

Virtual Engineering Object / Virtual Engineering Process: A specialized form of Cyber Physical System for Industrie 4.0

Abstract This paper reviews the theories, parallels and variances between Virtual Engineering Object (VEO) / Virtual Engineering Process (VEP) and Cyber Physical System (CPS). VEO and VEP is an experience based knowledge representation of engineering objects and processes respectively. Cyber–physical systems (CPSs) are the next generation of engineered systems in which computing, communication, and control technologies are tightly integrated. The analysis of basic concepts and implementation method proves that VEO/VEP is a specialized form of CPS and it can play a vital role in the structure building of Industry 4.0. Integration of the two models may result in intelligent machines and advanced analytics.

Virtual Engineering Object VEO: Toward Experience-Based Design and Manufacturing for Industry 4.0

In this article we propose the concept, its framework, and implementation methodology for Virtual Engineering Objects (VEO). A VEO is the knowledge representation of an engineering object that embodies its associated knowledge and experience. A VEO is capable of adding, storing, improving, and sharing knowledge through experience. Moreover, it is demonstrated that VEO is a specialization of a Cyber-Physical System (CPS). In this article, it is shown through test models how the concept of VEO can be implemented with the Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The test model confirmed that the concept of VEO is able to capture and reuse the experience of engineering artifacts, which can be beneficial for efficient decision-making in industrial design and manufacturing.

Set of Experience Knowledge Structure SOEKS and Decisional DNA DDNA: Past, Present and Future

This article reviews research work on set of experience knowledge structure (SOEKS)-decisional DNA (DDNA) done in the past, ongoing, and planned for the future. Firstly, the concept of the knowledge representation technique of SOEKS-DDNA is discussed, and then an attempt is made to organize the past research related with it in chronological order. This work focuses on the review on SOEKS-DDNA, its application in different domains, the various implementation platforms, as well as its benefits and its limitations. The second part of this article provides an idea of the SOEKS-DDNA-related research endeavors currently carried out by us and the last part is a sneak peek into our planned future work.

Virtual Engineering Factory: Creating Experience Base for Industry 4.0

ABSTRACT In recent times, traditional manufacturing is upgrading and adopting Industry 4.0, which supports computerization of manufacturing by round-the-clock connection and communication of engineering objects. Consequently, Decisional DNA-based knowledge representation of manufacturing objects, processes, and system is achieved by virtual engineering objects (VEO), virtual engineering processes (VEP), and virtual engineering factories (VEF), respectively. In this study, assimilation of VEO-VEP-VEF concept in the Cyber-physical system-based Industry 4.0 is proposed. The planned concept is implemented on a case study. Also, Decisional DNA features such as similarity identification and phenotyping are explored for validation. It is concluded that this approach can support Industry 4.0 and can facilitate in real time critical, creative, and effective decision making.

Decisional DNA Based Framework for Representing Virtual Engineering Objects

In this paper, we propose a frame-work to represent the Virtual Engineering Objects VEO utilizing Set of Knowledge Experience Structure SOEKS and Decisional DNA. A VEO will enable the discovery of new knowledge in a manufacturing unit and the generation of new rules that drive reasoning. The proposed VEO framework will not only be knowledge based representation but it will also have its associated experience embedded within it. This concept will evolve and discover implicit knowledge in industrial plant, which can be beneficial for the engineers and practitioners. A VEO will be a living representation of an object; capable of adding, storing, improving and sharing knowledge through experience, similar to an expert of that area.

Effect of Scheduling and Manufacturing Flexibility on the Performance of FMS

A framework for studying the effect of scheduling and manufacturing flexibility on the performance of flexible manufacturing system has been presented in this paper. Scheduling and manufacturing flexibility are among the many manufacturing strategies considered by the researchers to improve the system performance. In this paper in addition to scheduling and manufacturing flexibility other manufacturing strategies being considered are system configuration, buffer capacity, routing flexibility (manufacturing flexibility), number of pallets, volume of parts, dispatching and sequencing rules (scheduling). Performance of systems is evaluated on make-span time, cost, machine utilization and queue waiting time. The key issues which are addressed in this paper are the impact of different levels of routing flexibility, dispatching and sequencing rules and the increase in number of pallets on the system performance. Simulation results indicate that, with increase in routing flexibility, make-span time decreases. However the maximum benefit is obtained when routing flexibility increased from level 1 to 2. Combinations of sequencing and dispatching rules are identified, which can yield best results for make-span, cost of production, queue waiting time and machine utilization. It is suggested that the proposed methodology can be used in practice for not only setting priorities on specific manufacturing factors but also for highlighting likely factor level combinations that could yield improved shop performance.

A Semiautomatic Experience-Based Tool for Solving Product Innovation Problem

ABSTRACT In this paper, we present the idea of Smart Innovation Engineering (SIE) System and its implementation methodology. The SIE system is semiautomatic system that helps in carrying the process of product innovation. It collects the experiential knowledge from the formal decisional events. This experiential knowledge is collected from the group of similar products having some common functions and features. The SIE system behaves like a group of experts in its domain as it collects, captures, and stores the experiential knowledge from similar products as well as reuses this experiential knowledge that ultimately enhances the innovation process of manufactured goods. Moreover, with SIE in hand, entrepreneurs and manufacturing organizations will be able to take proper, enhanced decisions and most importantly at appropriate time. The system gains expertise each time a decision is taken and stored in the form of set of experience that can be used in future for similar queries. Implementation of the SIE system using Set of Experience Knowledge Structure and Decisional DNA for case study suggests that the SIE system is capable of capturing and reusing the innovation-related experiences of the manufactured products. The case study confirmed that the SIE system can be beneficial for entrepreneurs and manufacturing organizations for efficient decision making in the product innovation process.

Virtual engineering process (VEP): a knowledge representation approach for building bio-inspired distributed manufacturing DNA

The objective of this research is to provide a user-friendly and effective way of representing engineering processes for distributed manufacturing systems so that they can develop, accumulate and share knowledge. The basic definition and principle of the approach is introduced first and then the prototype version of the system is developed and demonstrated with case studies, which verify the feasibility of the proposed approach. This paper proposes a novel concept of virtual engineering process (VEP), which is experience-based knowledge representation of engineering processes. VEP is an extension of our previous work on virtual engineering object (VEO). VEP model includes complete process knowledge required to manufacture a component. This knowledge is captured from three distinctive aspects related to manufacturing. First, information about the manufacturing operations involved. Second, information about the resources/machines required to perform operations and third, information about process level decisions that are taken. It also aims to combine/share experience of engineering objects, manufacturing processes, and systems. It applies bio-inspired knowledge engineering approach called decisional DNA and set of experience-based knowledge representation.

Virtual Engineering Objects: Effective Way of Knowledge Representation and Decision Making

This paper presents a knowledge representation case study by constructing Decisional DNA of engineering objects. Decisional DNA, as a knowledge representation structure not only offers great possibilities on gathering explicit knowledge of formal decision events but also it is a powerful tool for decision-making process. The concept of Virtual engineering Object (VEO), which is a knowledge and experience representation of engineering artefacts, is also discussed. In this paper, we present several Sets of Experience of engineering objects used in manufacturing that were collected for the construction of a VEO-chromosome within the VEO-Decisional DNA. VEO is used to enhance manufacturing systems with predicting capabilities, facilitating decision-making in engineering processes knowledge handling.

Towards an experience based collective computational intelligence for manufacturing

Knowledge based support can play a vital role not only in the new fast emerging information and communication technology based industry, but also in traditional manufacturing. In this regard, several domain specific research endeavours have taken place in the past with limited success. Thus, there is a need to develop a flexible domain independent mechanism to capture, store, reuse, and share manufacturing knowledge. Consequently, innovative research to develop knowledge representation models of an engineering object and engineering process called Virtual engineering object (VEO) and Virtual engineering process (VEP) has been carried out and extensively reported. This paper proposes Virtual engineering factory (VEF), the final phase to create complete virtual manufacturing environment which would make use of the experience and knowledge involved in the factory at all levels. VEF is an experience based knowledge representation for a factory encompassing VEP and VEO within it. The novelty of this approach is that it uses manufacturers own previous experience and formal decisions to collect and expand intelligence for future production. The experience based collective computational techniques of Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA) are used to develop aforesaid models. In this article the concept and architecture of VEF is explained as well as the integration of all three levels of virtual manufacturing i.e. VEO, VEP and VEF is presented. Furthermore, a case-study is presented to validate the practical implementation of the proposed concept. The benefits of this approach are manifold as it creates the environment for collective intelligence of a factory and enhances effective decision making. The models and research presented here embody the important first step into developing the future computational setting as required by the emerging next generation of cyber-physical systems. A concept of knowledge based virtual representation for manufacturing processes.Approach to collect, represent, and store experiences as knowledge representation.Experience based manufacturing DNA is proposed, created, and implemented.Approach and tools to integrate virtual and physical system.