Computer Integrated Ship Production

1997 ◽  
Vol 13 (03) ◽  
pp. 215-223
Author(s):  
Einar Pedersen ◽  
John Fredrik Hatling
Keyword(s):  
Computer Integrated Manufacturing ◽  
Planning System ◽  
Quality Data ◽  
Shop Floor ◽  
Just In Time ◽  
Line Production ◽  
Control Programs ◽  
Organizational Issues ◽  
On Line ◽  
Data Files

Wider exploitation of computer integrated manufacturing (CIM) in shipbuilding is inevitable as yards seek continuing improvements in productivity and quality to sharpen competitiveness. The buzz word CIM has been used in many ways through the years. The correct interpretation may be the integration of all computer systems directly supporting the definition and production of the ship. This comprises CAD and also administrative systems such as planning and material control programs. CIM in this paper is defined as optimal utilization of CAD data and planning data in shop floor production, including feedback to the planning system and accumulation of quality data. The paper defines the state-of-the-art of CIM solutions at shipyards, and outlines a case study of one larger CIM installation. The case presented shows the structure of one installed system, and outlines the experience gained through planning, preparation and installation of the system. The case also includes cost budgets for the installation, including necessary training for planners and operators. Important organizational issues when planning the structure of CIM based production is described in the case. Terms such as Central Work Preparation and Local Work Preparation are introduced. The tasks performed in each area that add value to the CIM data files are described. At the end of the paper, the authors have outlined some future thoughts on further developments of CIM at shipyards. Important issues are, e.g., on-line production simulation systems capable of constantly updating the planning system in order to ensure the highest possible productivity and to support just in time (JIT) philosophies in all areas of the production.

Intelligent Process Planning Implemented As an Integrated Module of CIM

1997 ◽  
Author(s):  
Dusan N. Sormaz ◽  
Behrokh Khoshnevis
Keyword(s):  
Process Planning ◽  
Search Algorithm ◽  
Computer Integrated Manufacturing ◽  
Planning System ◽  
Process Selection ◽  
Process Sequencing ◽  
Knowledge Based ◽  
Completion Process ◽  
Integrative Process ◽  
The One

Abstract In this paper we describe an architecture of a new integrative process planning system as a part of computer integrated manufacturing research system. The process planning procedure is comprised of three phases: feature completion, process selection and process sequencing. We applied a knowledge-based approach to feature completion and process selection, and the space search algorithm for process sequencing. Description of these phases is provided and underlying knowledge representation explained. Integration between the process planning, on the one side, and CAD and scheduling, on the other, is discussed. System implementation has been described and several examples of the system execution are shown.

Computer-Aided Process Planning: A Path to Just-in-Time Manufacturing for Shipyards

1988 ◽  
Vol 4 (03) ◽  
pp. 197-215
Author(s):  
Richard L. DeVries
Keyword(s):  
Real Time ◽  
Process Planning ◽  
Group Technology ◽  
Shop Floor ◽  
Just In Time ◽  
Time Data ◽  
Computer Aided Process Planning ◽  
Computer Aided ◽  
Time Basis ◽  
Manufacturing Data

The use of computers to improve the productivity of U.S. shipyards has never been as successful as hoped for by the designers. Many applications were simply the conversion of an existing process to a computerized process. The manufacturing database required for the successful application of computer-aided process planning (CAPP) to the shipyard environment requires a "back-to-basics" approach, one that can lead to control of the processes occurring in the fabrication and assembly shops of a shipyard. The manufacturing database will not provide management feedback designed for the financial segment of the shipyard (although it can be converted to be fully applicable): it provides "real-time" manufacturing data that the shop floor manager can utilize in his day-to-day decisions, not historical data on how his shop did last week or last month. The computer is only a tool to be used to organize the mountains of manufacturing data into useful information for today's shop manager on a "real time" basis. The use of group technology to collect similar products, the use of parameters to clearly identify work content, the use of real-time efficiency rates to project capacity and realistic schedules, and the use of bar codes to input "real time" data are all tools that are part of the process—tools for the shop floor manager of tomorrow.

On-line Production Quality Prediction for a Commercial Naphtha Catalytic Reforming Process

2013 ◽  
Vol 12 (18) ◽  
pp. 4553-4560
Author(s):  
Hou Weifeng ◽  
Zhao Lujun ◽  
Zhang Liming ◽  
Wang Guihong
Keyword(s):  
Production Quality ◽  
Quality Prediction ◽  
Catalytic Reforming ◽  
Line Production ◽  
On Line

Hierarchical Database Model for Querying Economic Network Independence Distribution

2011 ◽  
Vol 3 (3) ◽  
pp. 58-67
Author(s):  
Akinwale Adio Taofiki
Keyword(s):  
Physical World ◽  
Shop Floor ◽  
The Internet ◽  
Database Model ◽  
N Level ◽  
Physical Barriers ◽  
On Line ◽  
Level Cluster ◽  
User Friendly ◽  
Order Algorithm

The development of the internet has been triggering numerous mutations in the visualization of actors in economic network independence distribution (ENID) of goods. ENID overcomes the physical barriers of shop-floor space so unprecedented variety of products could be offered to the customers. Avoidance of expensive trade space allows suppliers to reduce price compared to those in the physical world. User friendly and easy contact with the supplier of the goods make shopping very convenient. Despite these advantages of ENID, there is a need to develop better theories about how this system should behave in order to protect participants’ interests. This work employed hierarchical database model using B-tree and pre-order algorithm to insert and traverse participant records for easy processing. N-level models were adopted to calculate each level and sub-level cluster commission. The implementation was carried out using C# and sql. The application of the model permits the participants to query any information about ENID for on line real time decision makings.

scholarly journals Improving Just-in-Time Delivery Performance of IoT-Enabled Flexible Manufacturing Systems with AGV Based Material Transportation

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6333 ◽  
Author(s):  
Fengjia Yao ◽  
Bugra Alkan ◽  
Bilal Ahmad ◽  
Robert Harrison
Keyword(s):  
Decision Making ◽  
Flexible Manufacturing ◽  
Flexible Manufacturing Systems ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Mixed Integer ◽  
Shop Floor ◽  
Just In Time ◽  
Delivery Performance ◽  
Material Transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

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