Formulating polyurethanes using case based reasoning

A large amount of historical knowledge exists in the form of ‘formulation experiences’ across polyurethane manufacturing companies. This knowledge is difficult to formalise, share and use in new formulations. As a part of an effort to support the polyurethane formulating problem, the use of case based reasoning (CBR) has been assessed. Two basic problems in the development of the proposed hybrid tool that uses past formulations to solve new problems are studied. The problems investigated are related to the retrieval of former formulations that are similar to a new problem description by the CBR module, and the adaptation of the retrieved case to meet the problem constraints using an artificial neural network (ANN). Results indicated that the CBR-ANN system is useful for reusing historical data. Although the obtained ANN is unable to generalise well when presented with more data independent from the original data set, results proved that real formulation data can be used as a ‘knowledge repository’ that can guide CBR adaptation without human expert intervention

A case-based reasoning approach for low volume, high added value electronics

This paper will report on the application of the Case-Based Reasoning (CBR) approach [1] to develop a defect prediction system to support the development of new printed circuit assembly (PCA) products. Using a CBR system, past PCA design specifications and manufacturing experiences including defect and yield results can be effectively stored and reapplied for future problem solving. For example, the CBR can then be used at design stage to amend designs or define process options to optimise the product yield and service reliability. A case study using a case-base provided by a PCA manufacturer is presented

Combining business process and failure modelling to increase yield in electronics manufacturing

The prediction and capturing of defects in low-volume assembly of electronics is a technical challenge that is a prerequisite for design for manufacturing (DfM) and business process improvement (BPI) to increase first-time yields and reduce production costs. Failures at the component-level (component defects) and system-level (such as defects in design and manufacturing) have not been incorporated in combined prediction models. BPI efforts should have predictive capability while supporting flexible production and changes in business models. This research was aimed at the integration of enterprise modelling (EM) and failure models (FM) to support business decision making by predicting system-level defects. An enhanced business modelling approach which provides a set of accessible failure models at a given business process level is presented in this article. This model-driven approach allows the evaluation of product and process performance and hence feedback to design and manufacturing activities hence improving first-time yield and product quality. A case in low-volume, high-complexity electronics assembly industry shows how the approach leverages standard modelling techniques and facilitates the understanding of the causes of poor manufacturing performance using a set of surface mount technology (SMT) process failure models. A prototype application tool was developed and tested in a collaborator site to evaluate the integration of business process models with the execution entities, such as software tools, business database, and simulation engines. The proposed concept was tested for the defect data collection and prediction in the described case study

Component detection with on-board UHF RFID reader for Industrie 4.0 capable Returnable Transit Items

Industrie 4.0, Cyber-Physical Systems and Smart Manufacturing are all terms used to describe a vision of how intelligent products, processes and services can provide connectivity and real time information based technologies to improve manufacturing. This can be realised by embedding intelligence at the product and operational level to provide predictive, risk preventative and high performing manufacturing systems. The work outlined in this paper details how a Returnable Transit Item (RTI) can become an integral part of the Industrie 4.0 vision as an intelligent container that can interact with components, machines and other manufacturing services

A data management system for identifying the traceability of returnable transit items using radio frequency identification portals

The advancement of paradigms such as Industry 4.0 and cyber physical systems herald increased productivity and efficiency for manufacturing businesses through increased capture and communication of data, information and knowledge. However, interpreting the raw data captured by sensing devices into useful information for decision making can be challenging as it often contains errors and uncertainty. This paper specifically investigates the challenges of analysing and interpreting data recorded using Radio Frequency IDentification (RFID) portals to monitor the movements of Returnable Transit Items (RTI), such as racks and stillage, within an automotive manufacturing environment. Data was collected over a yearlong pilot study using an RFID portal system installed across two automotive facilities to trace the movement of RTIs between the sites. Based upon the results key sources of errors and uncertainty have been identified and a data management framework is proposed to alleviate these errors

Industrie 4.0 implementations in the automotive industry

To address the challenges imposed by the adoption of new technology to realise the Industrial Internet also known as Industrie 4.0, manufacturing companies are recognising the need to set up and manage “intelligent test factories”. The result is networks of cyber-physical systems (CPS) where software interfaces and services are developed to support interoperability between physical and control structures. A test factory using Radio Frequency Identification (RFID) as a first generation enabler of CPS in industrial production systems is presented in this paper. The research outlined in this paper describes the first generation of CPS that uses identification technologies such as RFID tags embedded into engine components and their carries, which allow unique identification. Data storage, processing and analytics are also provided to support real-time algorithmic intelligent services that may be used in manufacturing operations including supply chain logistics, quality audits and manufacturing strategies

Integration issues in the development of a modelling and simulation tool for low volume high-complexity electronics manufacture

In order to design and implement the information systems and modules that could comprise an “industrial strong” knowledge-based tool, links to shop floor systems containing real-time production data and PCA customer information (e.g. bill of materials (BOM), CAD drawings) are required. Details of the issues of implementing the tool in an industrial organisation and the integration of various data sources (e.g. “in-house” developed systems, enterprise resource planning systems, ad-hoc developed databases, machine data and CAD data) are presented in this paper. The application of the CLOVES system in an industrial setup highlights the difficulties in integrating information from design as CAD data and shows how these setbacks could be overcome if the electronics industry were to adopt a common CAD assembly information exchange platform. Hence, this paper concludes that existing automation tool manufacturers should focus exclusively on developing generic connections by adopting industry standards that can facilitate the deployment of “plug and play” tools. This standardisation could in turn help software developers, to provide the electronics industry with more integrated systems that communicate better among loosely coupled information systems and avoid depending on extensive time consuming manual data input

Towards industrial internet of things: crankshaft monitoring, traceability and tracking using RFID

The large number of requirements and opportunities for automatic identification in manufacturing domains such as automotive and electronics has accelerated the demand for item-level tracking using radio-frequency identification technology. End-users are interested in implementing automatic identification systems, which are capable of ensuring full component process history, traceability and tracking preventing costly downtime to rectify processing defects and product recalls. The research outlined in this paper investigates the feasibility of implementing an RFID system for the manufacturing and assembly of crankshafts. The proposed solution involves the attachment of bolts with embedded RFID functionality by fitting a reader antenna reader to an overhead gantry that spans the production line and reads and writes production data to the tags. The manufacturing, assembly and service data captured through RFID tags and stored on a local server, could further be integrated with higher-level business applications facilitating seamless integration within the factory

A case-based reasoning approach for low volume, high added value electronics

This paper will report on the application of the Case-Based Reasoning (CBR) approach [1] to develop a defect prediction system to support the development of new printed circuit assembly (PCA) products. Using a CBR system, past PCA design specifications and manufacturing experiences including defect and yield results can be effectively stored and reapplied for future problem solving. For example, the CBR can then be used at design stage to amend designs or define process options to optimise the product yield and service reliability. A case study using a case-base provided by a PCA manufacturer is presented

Complex low volume electronics simulation tool to improve yield and reliability

Assembly of Printed Circuit Boards (PCB) in low volumes and a high-mix requires a level of manual intervention during product manufacture, which leads to poor first time yield and increased production costs. Failures at the component-level and failures that stem from non-component causes (i.e. system-level), such as defects in design and manufacturing, can account for this poor yield. These factors have not been incorporated in prediction models due to the fact that systemfailure causes are not driven by well-characterised deterministic processes. A simulation and analysis support tool being developed that is based on a suite of interacting modular components with well defined functionalities and interfaces is presented in this paper. The CLOVES (Complex Low Volume Electronics Simulation) tool enables the characterisation and dynamic simulation of complete design; manufacturing and business processes (throughout the entire product life cycle) in terms of their propensity to create defects that could cause product failure. Details of this system and how it is being developed to fulfill changing business needs is presented in this paper. Using historical data and knowledge of previous printed circuit assemblies (PCA) design specifications and manufacturing experiences, defect and yield results can be effectively stored and re-applied for future problem solving. For example, past PCA design specifications can be used at design stage to amend designs or define process options to optimise the product yield and service reliability