Hybrid Intelligent Techniques in Manufacturing Systems

Published by Elsevier Science on behalf of IFAC

HYBRID INTELLIGENT TECHNIQUES IN MANUFACTURING SYSTEMS

loan DLJMITRAClIE 'Politehnica ' Uni versity of Bucharest Spl. Independentei. 313 . sect. 6. Bucharest, Romania, 77206 [email protected]

In this paper are presented some applications or intelligent techniques in manufacturing systems. After a short introduction in the manufacturing systems. different functions that can be implemented by new techniques are detined . Some advantages and di sadvantages of different intelligent techniques are outlined . This paper also presents some hybrid intelligent techniques. like neuro-fuzzy. geno-fuzzy and geno-neuro-fuzzy, taking into account the natural synergism of these intelligent techniques. The last section of the paper presents a structure of autonomous control , applied in manufacturing. The control structure includes the exccution level. the coordination level and the organization level into an hierarchical architecture. Different functions , that can be implemented using hybrid intelligent techniques, for different level s are al so defined . Kcy words : manufacturing systems. intelligent control systems. scheduling. hybrid intelligent techniques, abstract reasonlllg. process planning

GENONEUROFUZZY for many applications, with real impact on intelligent control systems [1). The complexity of the manufacturing systems defined many real problems that cannot be solved with classical engineering methods . In the last 30 years, the evolution of manufacturing systems has a trend to the intelligent systems with a high level of autonomy. Manufacturing systems should solve complex functions like the Job-Shop Scheduling (JSS), the Flow-Shop Scheduling (FSS), the Dynamic Scheduling (OS), Process Planning (PP), Optimization of Assembly Lines (OAL) and Planning and Control (PC). These different functions have to be solved taking into account the hybrid techniques. The hybridization in this paper is defined in connection with the most important intelligent techniques: expert systems (KBS), neural networks (NN), fuzzy logic (FL) and genetic algorithms (GA). The history of evolving manufacturing paradigms stress on the following step-by-step technological progress : I. Transfer lines for productivity and mass production ('60s); 2. Manufacturing island enriching job professional content (' 70);

1. INTRODUCTION The need for intelligent systems has continually increased in the last decade, due to higher performance requests in all social and economical structures. For complex systems, intelligence must be a real opportunity to solve the real problems in uncertain environments. Intelligent manufacturing systems have evolved in the last decade, today representing a real challenge for many other fields of activity. Intelligence requires the ability to sense the environment, to take decisions and to control actions. Higher levels or intelligence may include the ability to recognize objects and events, to represent knowledge in a world model and reason about and plan for the future. There are strong inter-relationships between knowledge based systems , neural networks , jilzzv logic and genetic algorithms. The natural synergism of these intelligent methodologies recommends different hybrid techniques like GENOFUZZY, GENONEURAL, NEUROruzzy and

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3. Automated factory supporting flexible automation paradigm (' 80s); 4. Integrated factory, high technology centred ('90s); 5. Lean production, where workers are highly skilled problem solvers ('2000); 6. Human-centred, based on team-work, responsible for putting together and entire I i fe-eye le of the product - Concurrent Engineering ( 19852005); 7. Agile manufacturing, according to the concept of soft integration - design process becomes a production capability; 8. Holonic manufacturing !Jyslems : the amount of information processing determines the manufacturing structure. If the keywords for lean production, human centred, agile manufacturing, autonomolls and distributed manufacturing, etc. , are learning and continuos improvement, the key for concurrent enterprising will be intelligence based on knowledge network. Then, the next generation of manufacturing companies will tend to emphasis the role of human resources as a sort of reaction to the previous unsuccessful technology-centred model. This is implemented and becomes the paradigm unti I technology may provide new solutions.

2. CONTROL AND OPTIMIZATION PROBLEMS IN MANUFACTURING SYSTEMS In the manufacturing systems, we could identify different complex functions, as shown in figure I.

2.1. The Job-Shop Scheduling Problem (JSSP) The job-shop scheduling problem consists of ordering n jobs to be processed on m machines. Each job involves a number of different machining operations. The following conditions hold the classic formulation of the JSSP: )0- Each machine can process only one job at the time; )0- The sequence of operations for each job is predefined; )0- Two operations of the job cannot be processed at the same time;

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Preemption is not allowed (an operation cannot be withdrawn from a machine unless it is completed); ,. Processing times are known in advance; ,. Transportation time between machines is zero.

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The most often used quality criterion for the JSSP is the minimization of makcspan (C,nax), which is defined as the completion time of the final job to leave the system. The JSSP could be described as a "representative of constrained combinatorial problems" . Minimization of the makespan is still used as an objective in many cases, but the general belief is that the objective of manufacturing optimization should be the minimization of production costs . Addressing the overdominance of makespan-oriented work in the field r4], employed seven quality criteria for the evaluation of good schedules: maximum tardiness, average tardiness, weighted flow time , weighted lateness, weighted tardiness, weighted number of tardy jobs and weighted earliness plus weighted tardiness. The last criterion is in accordance with the just-in-time (JIT) principle of having a product made exactly when it is required. This minimizes storage costs (earliness) and lateness fines (tardiness).

2.2. The Flow-Shop Scheduling Problem (FSSP) The permlltation flow-shop scheduling problem involves ordering n jobs to be processed on m machines. The difference between the job-shop and the flow-shop scheduling problem is that in the latter case, each job undergoes the same machining sequence, while the sequence of operations is the

same on each machine. This means that the solution of the problem can he represented as a permutation of all jobs to be processed:

is proposed. In this case, the scheduling process was divided into a series of ordered scheduling points. An evolutionary algorithm examines which dispatching rules performed better for each of these points, given a set of plant conditions (system status). The chromosome is formed by a series of genes, each one representing a respective scheduling point and taking as a value one of the available dispatching rules. The performance of the algorithm was simulated under different plant conditions, forming a knowledge base that describes the scheduling rules that are preferable in different cases. A binary decision tree was used to describe the gained knowledge. This method has the advantage of being able to modify its existing knowledge, without having to reconstruct the entire knowledge base. Another method based on machine learning was introduced by using classifier systems . In this case, an initial knowledge base is given, and an EA modifies it, using results taken from the simulation of the production line. In this way, the system learns to react to certain unexpected events. This is a hybrid system formed from EA and KBS (ES). In [13] a hybrid system is used, formed from GA and Neural Networks and an inductive learning algorithm called trace-driven knowledge acquisition (TDKA) to infer knowledge about the scheduling process . A BP neural network selects a number of candidate dispatching rules out of a large set of available rules . The schedules formed by these dispatching rules are used as the initial population of a GA that evolves optimal schedule.

[J p J 2 ,J3 ,···,J,J , where n is the total numbcr of jobs. The conditions that were introduced for the JSSP hold for the flow-shop scheduling problem as well. In recent years, more complicated formulations of the problem have been considered, with various alternative optimization criteria included. In [5], a multiobjective GA (MOGA) approach is used for a flow-shop scheduling problem, aImtng to simultaneously minimize makespan, {ota! tardiness and total flow time of the production. In [6] is presented the concept of a flexible flow Iine with variable lot sizes . EA was used to simultaneously optimize the ordering of jobs and the lot sizing. In (7], a fuzzy mathematical formulation of the problem is proposed, using the concept of fuzzy due dates. The optimization criteria were the maximization of the mll1lmUm satisfaction grade and the maximization of the total satisfaction grade .

2.3. The dynamic scheduling problem and comparisons between different scheduling algorithms In practical scheduling, a scheduler often has to react to unexpected events. The main uncertainties encountered in a real manufacturing system are: )0Machine breakdowns, including uncertain repair times~ ~

Increased priority of jobs~ Change in due dates; ~ Order cancellations. Whenever an unexpected event happens in a manufacturing plant, a scheduling decision must be made in real time, about the possible reordering of jobs. This process is known as " rescheduling". The main objective of rescheduling is "to find immediate solutions to problems resulting from disturbances in the production system". In the last years, EA's have been employed as parts of the hybrid dynamic scheduling systems, which exploit their useful characteristics . Machine learning is one of the methods that have traditionally been used in manufacturing environments, to face uncertainties . In [8 j, such a learning-based methodology for dynamic scheduling .,.

2.4. Process Planning (PP) Process planning takes as input the design characteristics of a product (CAD files), and gives as output its complete production plan. This plan should determine the machining process needed, the tools to be used and the sequencing of operations. If more than one plan is available, then an optimal plan should be selected. Process planning can be more or less elaborate, according to the processing requirements of a particular part. pp is the link between the design and the manufacturing phase of a product.

2.5. Control, quality and diagnosis in manufacturing processes Many other problems appear in manufacturing, where different intelligent techniques should be

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applied . Therefore, the PID controllers used on the first level of executive control in manufacturing are optimized by using GA or NN . Different variants of PID controllers or fuzzy controllers for SISO and MIMO processes, even Smith predictor, are optimized by using GA or NN [I]. Another problem connected with manufacturing is the process model identification. In this direction we can use different intelligent techniques like GA, NN or hybrid techniques. In [9], genetic programming is employed for system identification, constructing the trees with common mathematical functions. The failure of machines in the plant is inevitable . Shop-floor engineers aim to diagnose the failure of a machine as quickly as possible. They normally use a number of system parameters that are sensitive to changes of specific signals from the plant. A hybrid method for machine-noise diagnosis based on neural networks, expert systems and genetic algorithms is presented in [10] . Maintenance scheduling is another important operation in the shop-floor since the disruption of the production process must be as small as possible, but at the same time, the machines must work without failures for the longest time possible. An interesting hybrid system of evolutionary algorithms and simulated annealing for optimal maintenance scheduling is proposed in [10]. Quality control is an important aspect of modern manufacturing. The optimal allocation of inspection stations in the plant ensures that products are manufactured according to the quality criteria set by the management team . We could address this problem in a mUltistage manufacturing system and use an evolutionary algorithm to optimall y locate inspection stations. The solution was binary coded, with each gene representing a manufacturing stage . A hybrid approach was proposed in [I I], that designs a system that perform statistical processing control using neural networks and evolutionary computation. The neural network identifies the process model, and the evolutionary algorithm adjusts the control parameters in order to obtain the desired quality performance .

3.

HYBRID INTELLIGENT TECHNIQUES

In many applications we employ the intelligent techniques, like symbolic AI, fuzzy logic, neural networks and evolutionary algorithms. These techniques give the possibility to obtain good performances for large classes of appl ications. Intelligent hybrid systems (lHS) represent a challenge for understanding the human information and knowledge processing mechanisms. From the real time applications point of view, the four intelligent techniques, especially the hybrid architectures, have accomplished a substantial growth in solving control and optimization problems. The combinations that have maximum efficiency in solving different problems can be emphasized considering the advantages and disadvantages of different technologies. The methodologies based on knowledge processing have the following limitations: ~ Th e knowledge acquisition process is slow; ~ Inability to process incomplete information ; 'r Combinatorial explosion of the rules; Y Inability to reason when time restrictions are met. In spite of these limitations, the systems are successfully used in several domains including manufacturing, planning, designing, diagnosis, commerce, real time process control , etc. Fuzzy systems operate with linguistic representations . They are strongly dependent on human expertise in the knowledge acquisition process, like expert systems. Beside the restrictions imposed by the knowledge acquisition process, fuzzy systems adapting and learning abilities have limitations. Due to their capabilities of information and knowledge parallel processing, learning, generalization and incomplete information processing, neural networks are used in a variety of fields , like industry, finance, communications, environment, quality control, adaptive and predictive control, etc. Among the limitations of neural networks one can notice: ).. Inability to interact with conventional symbolic databases; ~ Inability to explain the results;

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Difficulties in training for larger and complex problems and, also, In evaluating their generalization abilities. Genetic algorithms are widely used in industry, as well as in economy and finance, because of their ability to solve complex optimization problems. Their main disadvantage is the important computational effort that limits the range of real time applications involving genetic algorithms. In fact, real time systems add a new dimension to the problems associated with different intelligent methodologies. Those problems are subject to temporal and spatial limitations introduced by real time systems. The limitations, but also the performances of various intelligent methodologies led to their hybridization from the applications point of view. At the same time as the hybridization process, in the case of various appl ications, research has been developed to confirm this concept while modeling human information processing systems. The four intelligent methodologies have a real life representation in cogl1ltlve sciences, cogl1ltlve psychology, structure and behavior of human brain and evolution. Hybrid systems represent a set of methodologies developed to portray human knowledge from the points of view of information processing, learning and knowledge representation. Hybrid intelligent systems can be grouped in four main classes: ~ FUSION SYSTEMS (F) ~ TRANSFORM SYSTEMS (T) ).> COMBINATION SYSTEMS (C) ).> ASSOCIATIVE SYSTEMS (A) In fusion systems, the properties of information representation and / or processing of a methodology A are included in the structure of representation of another methodology B. [n this case, methodology B improves its performances, realizing various intelligence levels. This system class develops around neural networks and genetic algorithms The transform systems are used to change a form of representation into another. They are used in those situations where the required knowledge for accomplishing a task is not available and an intelligent methodology must rely on another one for processing or reasoning. For example the neural networks are lIsed to transform discrete/continuolls

data into symbolic rules that can be used in a KBS for future processing. T systems, in conjunction with F systems, are also used to refine or optimize knowledge. Combination systems involve explicit hybridization. Unlike fusion, they model different levels of information processing and intelligence using those intelligent methodologies that best model a particular level. These systems involve a modularization of two or more methodologies to solve a given problem. Associative systems integrate F, T and C systems in a manner that maximizes the task or problem domains and the quality of their solutions. The classes of Hybrid Intelligent Systems (HIS) are depicted in Fig. 1.

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In the last 8 years, intelligent hybrid systems evolved in developing better strategies for problem solving and better understanding of human information processing system. These hybrid systems have good results in modeling the two levels of information processmg: macrostructure and microstructure. Concepts like fusion, transformation and combining are used in different situations or tasks, applying top-down and for bottom-up strategies, that are specific for knowledge engineering. Fusion is present in primary tasks, where the complexity is bigger and both adaptation and

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performance optimization are important for solving a problem. When the knowledge requested for a task is not available and an intelligent methodology depends on another intelligent methodology for reasoning or processing, transformation is needed (for example, rules extraction from a neural network and use of these rules in a KBS for reasoning). Usually, the T systems use numerical data as starting point for problem solving. On the other hand, the F systems use symbolic data or fuzzified data to initiate processing. In T systems task restrictions are fulfilled using two representations or two methodologies (one representation is based on neural networks for rule learning and the other one uses the rules extracted for reasoning). NEURO-SYMBOLIC systems (F and T) and NEURO-FUZZY systems are included in this category of intelligent hybrid systems. The topdown procedure is mainly based on a neural network that represents a symbolic function. The neurosymbolic systems can be divided into three categories: ~ Modeling of knowledge based symbolic structures using neural networks; ~ Modeling of symbolic reasoning using neural networks; ~ Modeling of decision trees using neural networks. The second category of Neuro-Fuzzy systems is based on the fact that both ANN and FS are estimators free of dynamic models. Geno-fuzzy and geno-neural FUSION and TRANSFORMA TION systems can be defined in a similar way. These systems use genetic algorithms for designing fuzzy systems and neural architectures . Different intelligent methodologies outline different aspects of human knowledge. By combining these methodologies we can design systems that might help in better understanding the human information processing system and more powerful techniques can be developed for complex problem solving. By combining different methodologies we can obtain intelligent hybrid systems like: NeuroSymbolic, Neuro-Genetic, Symbolic-Genetic, Neuro-Fuzzy, Geno-Neuro-Fuzzy. Many industrial applications are using such combinations of intelligent techniques.

A special category of hybrid intelligent systems is associative systems. In the general frame of hybrid associative systems, three levels of information and knowledge processing can be counted: the PROGRAM level, the COMPUTATIONAL level and the STRUCTURAL level. The development of such hybrid architectures involves the integration of all hybrid system categories into a configuration that models the human brain ability to process information. The achievement of this purpose assumes a systematic approach from the neuro-biological perspective, the cognitive sciences perspective, knowledge representation perspective, learning, artificial intelligence and computational intelligence and obviously from users and hardware implementations' perspective.

4. INTELLIGENT SYSTEMS IN MANUFACTURING PROCESSES The complexity and the high performances imposed to manufacturing systems must take into account new architectures of control systems for manufacturing. The diversity of problems and functions connected to the market of products with a very high level of uncertainty imposed in the last ten years these intelligent techniques into autonomous control systems. A standard autonomous intelligent system structure (figure 2) assumes that the process is highly interfaced with the environment. This is made using interfaces assuring data and knowledge acquisition and control sequence transfer from the data and knowledge processing system. The first hierarchical level is called the execution level. This level carries out the signal processing and generates numerical commands, based on conventional PID algorithms or some adaptive or optimal control strategies. The specific functions of parameter and/or state estimation are implemented at this level together with fault detection and isolation algorithms. This first control level is based on conventional techniques, numerically implemented. The execution level has a low intelligence level. The second hierarchical level is called the coordination level. This level assures tuning,

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planning, supervising and design reconfiguration of the algorithms from the execution level. Also, this level fulfills executive tasks management, and has learning and decision making capacities regarding the control algorithms and PID algorithms. .,

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abstractions of those composed by lower level models. Figure 3 presents the simplified structure of an autonomous system based on models. The specific functions of an intelligent system are outlined. The identification, decision making and tuning classical functions are implemented using techniques that are specific to perception, reasoning, planning, dynamic reconfiguration, decision making and learning. The knowledge base contains models of the process, models of the controllers and models that define the control objectives. The search in the control law data base is based, on one hand, on optimal selection of process' model, and on the other hand, on the control requests for the imposed objectives. Within the intelligent unit are identified models for planning, models for diagnosis, operational models, repairing models. All this models can be selected depending on the condition of the entire system, using dynamical reconfiguration procedures. [1, 2, 3]

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The third hierarchical level - strategic level or organizational level supervises the others. It also carries out the strategic management by monitoring the performances and elaborating the objective functions . This level assures the intelligent interfacing with the human operator. It also administrates controller's objective elaboration and evaluates their capacities. Functions like learning, knowledge processing, behavior generation and activities planning are implemented at this strategic level. It is obvious that the complexity of the functions implemented at this level presumes symbolic processing. This level is more intelligent than the execution level. Within the intelligent autonomous system, one can easily identify functions specific to human intelligence and specific control functions, based on the mathematical model of the process . Autonomous systems are hierarchically and heterarchically organized. They are constituted by interconnected intelligent units. At each node of the execution structure, the intelligent units use intern models from the model base. At the superior hierarchical levels, they use models representing

Fig. 4 Simplified structure of an autonomous system Thus, the intelligent control systems have the ability of self-organizing and total fault tolerance . These abilities make the system an AUTONOMOUS one. Depending on the process complexity and the control objectives, control systems with different levels of autonomy can be developed. Autonomy represents a goal in designing and implementation

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are taken based on the selection of the sequence having the greatest probability of success, respectively that action sequence that assures the minim knowledge flow entropy. Implementation of these functions uses the four intelligent methodologies (genetic algorithms, neural networks, fuzzy techniques and expert systems). The activity plan selected by the strategic level is taken over by the coordination level and the plan is asynchronous transmitted for execution. At the coordination level there is a dispatcher that interprets the plan, assigns individual tasks to other coordinators, monitors their behavior and transmits messages and commands from one coordinator to another, if necessary. From the analysis of the multilevel architecture of an autonomous system, one can notice that the functions fulfilled by each level have an abstraction level that increases from the execution level to the strategic level (organization level). The operations complexity increases along with the abstraction level, and implicitly the difficulties of problem solving using methods based on mathematical formalisllls.

of control systems. The intelligence of the control system represents a solution in achieving autonomy. A higher or a lower level of autonomy is provided to the control system by its level of intelligence. Thus, an autonomous system naturally incorporates conventional or advanced control techniques and intelligent techniques, such as neural and fuzzy techniques, evolutionary techniques and, of course, symbolic techniques, that are specific to artificial intelligence. Autonomous systems assure that complex dynamic systems will work with optimal performances even when uncertainty is present, with working domains that need models and control objectives change. This implies the use of some advanced mechanisms for decision making and behavior generation, by elaborating some control actions that maintain the proposed performance level, even when drastic changes of functionality appear. Use of intelligent control techniques assures the autonomy of some adaptive control systems. Hybrid systems naturally appear in autonomous systems' structure. Here are some examples of hybrid systems: the supervisor, reference planning for the controller, tuning of the controllers from the execution level. Within these hybrid systems, the discrete controller from the management and coordination level, operating with symbols commands the execution level. Taking into account the functions of the strategic level, the attributes that are specific to intelligent systems, like: abstract reasoning, planning, decision generation and memonzmg can be outlined within this level. At this level, the decisions

5.

CONCLUSIONS

This paper gives an overview of the applications of intelligent techniques in manufacturing systems. The functions and the architectures are defined and implemented by using hybrid intelligent techniques and the performances of these intelligent autonomous systems are analyzed.

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