Methodology for Periodic Compliance Control of Composite Reservoirs Installation in Motor Vehicles

The paper points out the problem of periodic compliance control of composite reservoirs installation in motor vehicles and necessity of designing methodologies in this field. The survey indicated that with expert systems application periodic compliance of composite reservoirs installation can be achieved. The paper presents the design of an expert system for visual control of composite tanks for storing compressed natural gas as a fuel for motor vehicles, using a shell expert system as a tool to design the knowledge base. The knowledge base is designed using production rules. The expert system is tested on a concrete example. The research results point to the possibility of periodical compliance control of composite reservoirs installation in motor vehicles using designed knowledge base. Also, the quality of visual control, reducing the time for analysis and reasoning on the state of composite reservoirs and auxiliary equipment for storing compressed natural gas as a fuel for motor vehicles, is achieved using the designed expert system.Advanced Engineering System

An expert system for LD steel making

LD process of steelmaking accounts for 70% of steel production throughout the world. Due to various constraints like the complexity of LD process; variation in quantity, quality and grade of input materials; varying operating conditions, random parameters and their values; the interventions of an expert and skilled operator is necessary to tackle the complex situation. To overcome this, there exists, a need of a system, which does not only possess the process control capabilities, but also emulates operator's expertise in terms of his knowledge and skill. Such a system has been developed named "Expert Steelmaker". The paper discusses the "Expert Steelmaker" which can presently be used in an advisory mode by the LD steelmaking operator

An expert system for selecting attribute sampling plans

A considerable portion of quality control managers lime in a production system is spent in routine and complex decision-making processes that have significant impact on average outgoing quality, quality improvement, and quality cost. The quality control manager must decide among various statistical process control methods and sampling plans for each part (or characteristic). These decisions arc usually based on how critical a part is, historical information about the quality of the parts, and other factors. Many of these factors require subjective judgments by the quality control manager. For a production facility with an inventory system of thousands of different parls l determination of feasible sampling plans and process control charts is a time-consuming and difficult task. As demons: rated in this paper, an expert system has been designed 10 faciliratc the selection of an appropriate sampling plan for each part . The system is referred to as Adviser for Selecting Auribute Sampling Plan (ASASP). © 1990 Taylor & Francis Ltd

Ontology-based assistance system for control process reconfiguration of Robot-Based Applications

Due to increasing global competition, companies are challenged to make their production flexible and adaptable. This leads to a steadily increasing complexity of production systems and thus their automation and control processes. At the same time, control processes must be quickly configurable in order to be able to react to short product life cycles. Robot-based adhesive application in automotive body assembly represents one such control and automation process. In car body assembly, industrial robots are increasingly being used for gluing side panels, enabling flow operation in assembly. In the event of a functional change in the production process, such as the replacement of the adhesive to be used, all the given process interrelationships must be analysed again and reconfigured if necessary in order to ensure the quality of the bonded joint. Comprehensive data management systems that provide an overview of all the system parameters and control levers are often not available in companies, so that reconfiguration is based on experience. Correct adjustment of the process parameters thus requires the user to have precise knowledge of the complex interrelationships between the process and bonding parameters, which makes the search for solutions in the event of a process change more difficult and time-consuming. In order to master the complexity of process planning and configuration, a large number of user-supporting solutions exist in the area of product lifecycle management (PLM). However, these neither have the functionality to generate solution and optimization proposals, nor do they map the existing expert knowledge with so-called empirical values about the system behaviour. The advantages of semantic technologies including ontologies, such as their graph structure and suitability for the use of optimization algorithms, illustrate their potential as the basis of a knowledge-based assistance solution. Against this background, the aim of this paper is to develop an ontology-based knowledge management system that can consolidate existing product and process information and add expert knowledge to it. The resulting knowledge graph of the process is then examined using selected optimization algorithms (PMS, Parallel Machine Scheduling). From the analysis, configuration suggestions can be derived, which can be presented to the user with a visualisation interface. Finally, the potential of ontologies as the basis of a knowledge-based assistance system is evaluated based on given results

Decision making models embedded into a web-based tool for assessing pest infestation risk

Current practices in agricultural management involve the application of rules and techniques to ensure high quality and environmentally friendly production. Based on their experience, agricultural technicians and farmers make critical decisions affecting crop growth while considering several interwoven agricultural, technological, environmental, legal and economic factors. In this context, decision support systems and the knowledge models that support them, enable the incorporation of valuable experience into software systems providing support to agricultural technicians to make rapid and effective decisions for efficient crop growth. Pest control is an important issue in agricultural management due to crop yield reductions caused by pests and it involves expert knowledge. This paper presents a formalisation of the pest control problem and the workflow followed by agricultural technicians and farmers in integrated pest management, the crop production strategy that combines different practices for growing healthy crops whilst minimising pesticide use. A generic decision schema for estimating infestation risk of a given pest on a given crop is defined and it acts as a metamodel for the maintenance and extension of the knowledge embedded in a pest management decision support system which is also presented. This software tool has been implemented by integrating a rule-based tool into web-based architecture. Evaluation from validity and usability perspectives concluded that both agricultural technicians and farmers considered it a useful tool in pest control, particularly for training new technicians and inexperienced farmers

Development and evaluation of an automated prototype for the fertigation management in a closed system

In France, in order to prevent imbalances and accumulation of nutrients in the plant root zone, most of the soilless crops in glasshouses are cultivated in open drainage systems, which leads to water and fertilizer losses. The goal of the EU project CLOSYS (CLOsed SYStem for water and nutrient management) was to build a prototype which delivers water and nutrients according to the plant needs in a recirculating system. This prototype aimed at controlling production and quality as well as reducing nutrient accumulation or shortage in the root zone in a closed system and avoiding pollution of the environment. This paper deals with the development and the evaluation of the prototype in comparison with a classical closed system for a sweet pepper crop. This prototype includes: substrate and plant models incorporated in an expert system, using substrate and plant sensors, and a real time controller. Technical details and results of each module will be presented. The plant model provided proper simulations of growth and development parameters, nutrient concentrations in the plant organs, plant nutrient and water consumption. The expert system enabled the coupling between plant and substrate models, thus ensuring the system to take into account the weather forecasts. The real time controller managed to control relative water content and electric conductivity in the substrate slabs. A Leaf Area Index sensor was used to calibrate the plant model according to the real crop area development. As a conclusion, the CLOSYS system led to a lower nutrient consumption, lower sodium and chloride accumulation and a proper electric conductivity control in comparison with the classical closed system, while maintaining production and quality

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

Directions of Digital Technologies Development in the Supply Chain Management of the Russian Economy

Abstract— The main objective of this paper is to investigate the digitalization and technologies impact on supply chain management of agricultural industry.  This paper provides practical examples of supply chain digitalization, as well as its socio-economic and environmental effects. The absence of processes that are compatible with the high production requirements adopted in foreign markets can lead to crisis phenomena in domestic industries with high potential and rapid growth dynamics in agriculture industry. Agriculture in Russia is an integral part of the agro-industrial complex, and the program “Digitalization of its supply chain” should provide participants with the opportunity to use broadband, mobile, LPWAN communications, information technologies (small and big data, management platforms, etc.) of the domestic instrument industry (tags, controllers, sensors, control units) to improve significantly the efficiency of agriculture. The opportunities for modernizing the industry are huge. Food security of the country and the development of export potential, turn agriculture into a high-tech industry that can not only provide food for itself, but also many countries of the world through the global supply chain system, as well as create opportunities for the introduction of new innovative developments that have not exist before, stimulate management decisions that can provide the population with high-quality and safe products. According to expert estimates, during the season, the farmer has to make more than 40 different decisions in limited time intervals. Many of these solutions, which affect directly the production economy, are objects of digitalization in supply chain

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

An expert system for a local planning environment

In this paper, we discuss the design of an Expert System (ES) that supports decision making in a Local Planning System (LPS) environment. The LPS provides the link between a high level factory planning system (rough cut capacity planning and material coordination) and the actual execution of jobs on the shopfloor, by specifying a detailed workplan. It is divided in two hierarchical layers: planning and scheduling. At each level, a set of different algorithms and heuristics is available to anticipate different situations.\ud \ud The Expert System (which is a part of the LPS) supports decision making at each of the two LPS layers by evaluating the planning and scheduling conditions and, based on this evaluation, advising the use of a specific algorithm and evaluating the results of using the proposed algorithm.\ud \ud The Expert System is rule-based while knowledge (structure) and data are separated (which makes the ES more flexible in terms of fine-tuning and adding new knowledge). Knowledge is furthermore separated in algorithmic knowledge and company specific knowledge. In this paper we discuss backgrounds of the expert system in more detail. An evaluation of the Expert system is also presented