Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

System Interface for an Integrated Intelligent Safety System (ISS) for Vehicle Applications

This paper deals with the interface-relevant activity of a vehicle integrated intelligent safety system (ISS) that includes an airbag deployment decision system (ADDS) and a tire pressure monitoring system (TPMS). A program is developed in LabWindows/CVI, using C for prototype implementation. The prototype is primarily concerned with the interconnection between hardware objects such as a load cell, web camera, accelerometer, TPM tire module and receiver module, DAQ card, CPU card and a touch screen. Several safety subsystems, including image processing, weight sensing and crash detection systems, are integrated, and their outputs are combined to yield intelligent decisions regarding airbag deployment. The integrated safety system also monitors tire pressure and temperature. Testing and experimentation with this ISS suggests that the system is unique, robust, intelligent, and appropriate for in-vehicle applications

A next generation manufacturing control system for a lean production environment

This thesis focuses on addressing the need for a new approach to the design and implementation of manufacturing control systems for the automotive industry and in particular for high volume engine manufacture. Whilst the operational domain in the automotive industry has moved to lean production techniques, the design of presentday manufacturing control systems is still based on systems intended for use in a mass production environment. The design and implementation of current manufacturing control systems is therefore inappropriate when viewed from a business context. The author proposes that it is possible to create a more appropriate manufacturing control systems based on an optimised use of advanced manufacturing technology within the complete business context. Literature is reviewed to provide a detailed understanding of the relationship between modem operating practices and the application of contemporary control systems. The primary tasks of manufacturing control systems, within the context of a structured systems approach to manufacturing technology, production management and industrial economics are identified. A study of modem manufacturing control system technology is carried out, highlighting the fundamental principles that influence application engineering in this area. The thesis develops a conceptual design framework that aids the identification of attributes required of a next generation manufacturing control system (NGCS), in order to enhance the business performance of lean automotive manufacturing. The architecture for a next generation control system is specified and a Proof of concept system implemented. Potential advances over contemporary practice are identified with the aid of a practical implementation at a major automotive manufacturer

Reusability in manufacturing, supported by value net and patterns approaches

The concept of manufacturing and the need or desire to create artefacts or products is very, very old, yet it is still an essential component of all modem economies. Indeed, manufacturing is one of the few ways that wealth is created. The creation or identification of good quality, sustainable product designs is fundamental to the success of any manufacturing enterprise. Increasingly, there is also a requirement for the manufacturing system which will be used to manufacture the product, to be designed (or redesigned) in parallel with the product design. Many different types of manufacturing knowledge and information will contribute to these designs. A key question therefore for manufacturing companies to address is how to make the very best use of their existing, valuable, knowledge resources. […] The research reported in this thesis examines ways of reusing existing manufacturing knowledge of many types, particularly in the area of manufacturing systems design. The successes and failures of reported reuse programmes are examined, and lessons learnt from their experiences. This research is therefore focused on identifying solutions that address both technical and non-technical requirements simultaneously, to determine ways to facilitate and increase the reuse of manufacturing knowledge in manufacturing system design. [Continues.

The impact of the convergence of information technology and industrial automation on operational excellence in the manufacturing environment.

Thesis (MBA)-University of KwaZulu-Natal, 2007.The need to increase productivity, improve quality and increase flexibility whist continuously reducing costs is driving manufacturers to search for alternative means of converting the product idea into a manufactured product. Plant automation systems which are the nervous system and increasingly the intelligence of the plant have an integral role to play in this regard. This study investigates the convergence between traditional IT and Industrial Automation with a view to understanding how this phenomenon will affect operational excellence within the manufacturing environment. The study further investigates the key determinants of success for automation systems within the broader business context and how this can lead to an advantage over competitors. The study is limited to manufacturing operations within the greater Durban area. The results revealed that there is a clear relationship between industrial automation and information technology in manufacturing organisations. However, of interest is the fact that in the majority of the organisations surveyed the two functions operate as separate entities within the organisation resulting in overlaps of responsibility and accountability for key equipment and processes. Factory efficiency was found to be the key determinant of success in the majority of the organisations surveyed whilst the provisioning of production data when used strategically was found to have a positive effect in allowing the organisation to gain an advantage over its competitors. Due to the limitation of the short time frame allocated to this research, the study could not go in detail into the drivers of these findings consequently recommendations for further research is made

The design and development of a knowledge-based lean six sigma maintenance system for sustainable buildings. The design and development of a hybrid Knowledge-based (KB)/Gauging Absence of Pre-requisites (GAP)/Analytic Hierarchy Process (AHP) model for implementing lean six sigma maintenance system in sustainable buildings' environment

The complexity of sustainable building maintenance environment requires managers to define and implement appropriate quality benchmark system suitable for this function. Lean Six Sigma (LSS) is one of the most effective process improvement and optimization philosophy that maintenance organisations can implement in their environment. However, literature review has shown that 90% of failures in LSS implementations are due to lack of readiness to change, the unawareness of the required benchmark organisation capabilities, and improper control of priorities. The contribution of the current research approach is in developing a hybrid Knowledge-Based (KB)/GAP/AHP System, consisting of three stages (Planning, Designing and Implementation) and containing over 2500 KB rules. The KB System can assist the decision-makers in identifying the obstacles behind the organisation readiness to change into a benchmark LSS maintenance environment. Thus the KB System will be used to achieve benchmark standards by determining the gap existing between the current environment and the benchmark goal, and then suggest a detailed plan to overcome these hurdles in a prioritised and structured manner, thus achieving cost benefits. To ensure its consistency and reliability, the KB System was validated in three Oman-based maintenance organisations, and one published case study for a UK-based organisation. The results from the validation were positive with the System output suggesting list of top priorities and action plans for achieving benchmark LSS standards for these organisations. The research concludes that the developed KB System is a consistent and reliable methodology for assisting decision-makers in designing, planning, and implementing LSS for benchmark sustainable building maintenance

Development of Multiple Linear Regression Model and Rule Based Decision Support System to Improve Supply Chain Management of Road Construction Projects in Disaster Regions

Supply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas