Niels Lohse

An ontology for the definition and validation of assembly processes for evolvable assembly systems

Assembly forms eighty per cent of the cost of manufacturing a product and this is where the greatest competitive advantage can be gained as assembly has not been fully understood in the research community. This paper reports on the development of a specification ontology to describe assembly processes at a level where they can be mapped to individual assembly modules. The ontology is an extension of the process specification language (PSL) defined by NIST. The developed ontology is applied to an existing case to demonstrate the capabilities. It is believed that the development of this ontology will lead to the opening of further channels for research in modular control and modular assembly processes, which are building blocks for evolvable assembly systems

A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information

Manufacturing technology selection is traditionally a human-driven approach where the trade-off of alternative manufacturing investments is steered by a group of experts. The problem is a semi-structured and subjective-based decision practice influenced by the experience and intuitive feeling of the decision-makers involved. This paper presents a distinct experience-based decision support system that uses factual information of historical decisions to calculate confidence factors for the successful adoption of potential technologies for a given set of requirements. A fuzzy-decision-tree algorithm is applied to provide a more objective approach given the evidence of previous manufacturing technology implementation cases. The model uses the information relationship of key technology decision variables, project requirements of an implemented technology case and the success outcome of a project to support decision problems. An empirical study was conducted at an aircraft manufacturer to support their technology decision for a typical medium complexity assembly investment project. The experimental analysis demonstrated encouraging results and practical viability of the approach.

Towards Web‐enabled design of modular assembly systems

The development of reconfigurable modular production systems is one of the crucial factors for manufacturers to sustain their competitive advantage in areas such as precision assembly. To ensure the effective and cost efficient configuration and successive reconfigurations it is of critical importance to involve all stakeholders in the decision‐making process. The reported research is targeting the development of an integrated Web‐enabled decision‐making environment that supports some of the key assembly system engineering stages from user requirement specification to system implementation. The focus is on the design of assembly workstations based on detailed process requirements with a target of developing highly efficient and cost‐effective solutions. The paper presents an application framework for collaborative distributed design supported by domain ontologies and is illustrated using an industrial case study.

Evolvable Assembly Systems - On the role of design frameworks and supporting ontologies

Evolvable assembly systems (EAS) are aimed to enable enterprises to rapidly respond to changes in todays increasingly volatile and dynamic global markets. One of the key success factors for the effective use of EAS is methods and tools that can rapidly configure and reconfigure assembly systems driven by changing requirements. The focus of this paper is on the analysis of modular assembly systems within the EAS paradigm. The specific roles of synthetic design environments and their supporting knowledge models are being explored within the scope of EAS systems. Furthermore, the paper outlines an ontology for the design of modular assembly systems (ONTOMAS) and illustrates its enabling role within the EAS paradigm. The results of this work are expected to significantly improve the evolvability of modular assembly systems

A framework for performance measurement during production ramp-up of assembly stations

Production ramp-up is an important phase in the lifecycle of a manufacturing system which still has significant potential for improvement and thereby reducing the time-to-market of new and updated products. Production systems today are mostly one-of-a-kind complex, engineered-to-order systems. Their ramp-up is a complex order of physical and logical adjustments which are characterised by try and error decision making resulting in frequent reiterations and unnecessary repetitions. Studies have shown that clear goal setting and feedback can significantly improve the effectiveness of decision-making in predominantly human decision processes such as ramp-up. However, few measurement-driven decision aides have been reported which focus on ramp-up improvement and no systematic approach for ramp-up time reduction has yet been defined. In this paper, a framework for measuring the performance during ramp-up is proposed in order to support decision making by providing clear metrics based on the measurable and observable status of the technical system. This work proposes a systematic framework for data preparation, ramp-up formalisation, and performance measurement. A model for defining the ramp-up state of a system has been developed in order to formalise and capture its condition. Functionality, quality and performance based metrics have been identified to formalise a clear ramp-up index as a measurement to guide and support the human decision making. For the validation of the proposed framework, two ramp-up processes of an assembly station were emulated and their comparison was used to evaluate this work.

Multi-agent Architecture for Reconfiguration of Precision Modular Assembly Systems

Precision assembly systems today are subject to high levels of change which require fast physical and logical adaption. As such, new methods and tools need to reflect this need providing adaptive systems that can react to market changes in a timely and cost effective manner. Towards achieving increased responsiveness several developments of new modular concepts provided the bases for higher system adaptability through increased module interchangeability and reusability. The modularization of physical and control infrastructure only solves one aspect of the issue, there is still a lack of appropriate tools and methods to support requirements driven reconfiguration of such systems. This paper proposes an agent architecture for reconfiguration of equipment modules driven by a set of requirements. A new agent model is proposed which addresses the specific needs of precision modular assembly systems catering both for physical and logical constraints of the modules as well as their joint emergent behaviour.

Data modelling for web enabled design of modular precision assembly devices

One of the key factors for success in assembly automation at present is the ability of system integrators to deliver more reliable and customised systems within shorter periods of time. Despite the active research in the development of distributed engineering support systems, there is a clear need for practical methods and tools to support the interactions between different stakeholders at the early product and assembly system design stages. This paper reports on the development of assembly process and equipment data models using context free grammars. The models enable a web‐based decision‐making for formation of assembly workstations using distributed linguistic approach.

An MDP Model-Based Reinforcement Learning Approach for Production Station Ramp-Up Optimization: Q-Learning Analysis

Ramp-up is a significant bottleneck for the introduction of new or adapted manufacturing systems. The effort and time required to ramp-up a system is largely dependent on the effectiveness of the human decision making process to select the most promising sequence of actions to improve the system to the required level of performance. Although existing work has identified significant factors influencing the effectiveness of ramp-up, little has been done to support the decision making during the process. This paper approaches ramp-up as a sequential adjustment and tuning process that aims to get a manufacturing system to a desirable performance in the fastest possible time. Production stations and machines are the key resources in a manufacturing system. They are often functionally decoupled and can be treated in the first instance as independent ramp-up problems. Hence, this paper focuses on developing a Markov decision process (MDP) model to formalize ramp-up of production stations and enable their formal analysis. The aim is to capture the cause-and-effect relationships between an operators adaptation or adjustment of a station and the stations response to improve the effectiveness of the process. Reinforcement learning has been identified as a promising approach to learn from ramp-up experience and discover more successful decision-making policies. Batch learning in particular can perform well with little data. This paper investigates the application of a Q-batch learning algorithm combined with an MDP model of the ramp-up process. The approach has been applied to a highly automated production station where several ramp-up processes are carried out. The convergence of the Q-learning algorithm has been analyzed along with the variation of its parameters. Finally, the learned policy has been applied and compared against previous ramp-up cases.

Review of semantic modelling technologies in support of virtual factory design

Comprehensive data models and standardised terminologies are required across tools and their developers to rapidly design and prototype digitised virtual factories. Also, various types of software are required to be integrated for cost-effective modelling exercises. To meet this requirement, semantic modelling technologies involving ontologies have featured prominently in many manufacturing applications. This paper considers, extensively, the current semantic requirements for digitised virtual factory design and prototyping and compares them with the capabilities of existing semantic technologies. As a result, a review of methodologies, tools and languages for creating ontologies has been reported. The review concludes with the next generation requirements for methodologies, tools and languages for creating ontologies suitable for the virtual factory design process, and further explains ongoing work associated with the creation of ontologies and reasoning mechanisms through the integrated use of ObjectLogic, OntoStudio and OntoBroker.

Towards an Integrated Assembly Process Decomposition and Modular Equipment Configuration

In today’s increasingly volatile and dynamic global markets it is increasingly important to react to changing market demands and reduce the time-to-market. The design and re-design of assembly systems has a significant impact on the product development time. This paper reports on the effort that has been put into developing an assembly process decomposition and modular assembly equipment configuration methodology that takes advantage of the current trend towards modular equipment solutions and is expected to reduce design time and improve the design process integration. A general framework for the proposed methodology has been outlined and an ontology for the design of modular assembly systems is being discussed.