Integrated knowledge-based Petri net intelligent flexible assembly planning

Automatic assembly planning is recognized as an important tool for reducing manufacturing costs in concurrent product and process development. A novel knowledge-based Petri net (KBPN) is defined, based on the incorporation of expert systems into the usual Petri nets, and used for a unified assembly knowledge representation scheme. A KBPN-approach integrated with a sequence generation algorithm is proposed for the modeling, planning, simulation, analysis and evaluation of the flexible assembly system (FAS). The developed KBPN-based assembly planning system (KAPS) can automatically adjust the deviations between the theoretical planning parameters and the process parameters of real assembly operations to guarantee the best strategies and plans (sequences) for flexible assembly. The research findings are exemplified with a simple assembly to show the effectiveness of the method.     

Lane selection in an AGV-based asynchronous parallel assembly line

Automated Guided Vehicles (AGVs) have the flexibility to meet the material handling requirements of asynchronous assembly lines. Furthermore, AGVs can select from among alternative paths in accordance with a prescribed lane selection rule, thereby facilitating the use of parallel server workstations.

In this paper, motivated by our work in the automotive industry, several new lane selection rules are proposed. One of these, First Available Server/First Available Buffer/Balanced Work Content (FAS/FAB/BWC), is compared to two existing rules: Alternating Server, and First Available Server/First Available Buffer/Expected Completion Time (FAS/FAB/EC). Three performance measures — job throughput, workload balance among servers, and alteration of input job sequence — are employed. A SIMAN simulation model of a small, asynchronous parallel assembly line is used to study the impact on system performance of both the lane selection rule and the number of parallel servers. Interaction between these two factors is studied through ANOVA. A number of interesting findings are reported for results of the lane selection rules with respect to the three performance measures. Their interpretation is used to motivate further research.     

A complexity model for sequence planning in mixed-model assembly lines

Sequence planning is an important problem in assembly line design. It is to determine the order of assembly tasks to be performed sequentially. Significant research has been done to find good sequences based on various criteria, such as process time, investment cost, and product quality. This paper discusses the selection of optimal sequences based on complexity induced by product variety in mixed-model assembly line. The complexity was defined as operator choice complexity, which indirectly measures the human performance in making choices, such as selecting parts, tools, fixtures, and assembly procedures in a multi-product, multi-stage, manual assembly environment. The complexity measure and its model for assembly lines have been developed in an earlier paper by the authors. According to the complexity models developed, assembly sequence determines the directions in which complexity flows. Thus proper assembly sequence planning can reduce complexity. However, due to the difficulty of handling the directions of complexity flows in optimization, a transformed network flow model is formulated and solved based on dynamic programming. Methodologies developed in this paper extend the previous work on modeling complexity, and provide solution strategies for assembly sequence planning to minimize complexity.     

Chaotic particle swarm optimization for assembly sequence planning

Assembly sequence planning of complex products is difficult to be tackled, because the size of the search space of assembly sequences is exponentially proportional to the number of parts or components of the products. Contrasted with the conventional methods, the intelligent optimization algorithms display their predominance in escaping from the vexatious trap. This paper proposes a chaotic particle swarm optimization (CPSO) approach to generate the optimal or near-optimal assembly sequences of products. Six kinds of assembly process constraints affecting the assembly cost are concerned and clarified at first. Then, the optimization model of assembly sequences is presented. The mapping rules between the optimization model and the traditional PSO model are given. The variable velocity in the traditional PSO algorithm is changed to the velocity operator (vo) which is used to rearrange the parts in the assembly sequences to generate the optimal or near-optimal assembly sequences. To improve the quality of the optimal assembly sequence and increase the convergence rate of the traditional PSO algorithm, the chaos method is proposed to provide the preferable assembly sequences of each particle in the current optimization time step. Then, the preferable assembly sequences are considered as the seeds to generate the optimal or near-optimal assembly sequences utilizing the traditional PSO algorithm. The proposed method is validated with an illustrative example and the results are compared with those obtained using the traditional PSO algorithm under the same assembly process constraints.     

A genetic-algorithm-based approach to the generation of robotic assembly sequences

An assembly sequence is considered to be optimal when it minimizes the assembly cost while satisfying assembly constraints. To generate such sequences for robotic assembly, this paper proposes a method using a genetic algorithm (GA). This method denotes an assembly sequence as an individual, which is assigned a fitness related to the assembly cost. Then, a population consisting of a number of individuals evolves to the next generation through the genetic operations of crossover and mutation, based upon the fitness of the individuals. The population continues to evolve repetitively, and finally the fittest individual with its corresponding assembly sequence is found. Through case studies for industrial products such as an electrical relay and an automobile alternator, the effectiveness of the proposed method is demonstrated, and the performance is analyzed.     

A physically based approach with human–machine cooperation concept to generate assembly sequences

Assembly plan is considered one of the important stages to minimize the cost of manufacturer and to ensure the safety of assembly operation, the main problem of assembly sequence planning approach is how to reduce the deviation from the real manufacture conditions. In this paper, we have extensively investigated a novel approach to automatically generate the assembly sequences for industrial field, which is especially applied to other large-scale structures. A physically based assembly representation model includes not only the pre-determined basic assembly information, such as precedence relations between parts or subassemblies, geometric constraints, different assembly types, and also the dynamic real-time physical properties, such as the center position of gravity, the force strength of the part, et al. This representation model considered the influences on optimum sequences by assembly operations will be modified by the feedback from interactive virtual environment. Then, we select the safety, efficiency and complexity as the optimization objectives. A hybrid search approach may be used to find the optimum assembly sequence, which will be integrated into an interactive assembly virtual environment (IAVE). It means that the results of assembly interaction can be provided to update the assembly planning model as a feedback, by which the approach will take advantages of the immune memory for local optimum search. The user can adjust the assembly sequences with obvious good objective by interaction with IAVE to improve the performance of the search algorithm. We describe human–machine cooperation (HMC) method for ASP in this work, by which human also can play a pivotal role instead of pure soft-computing. A series of numerical experiments are done to validate the performance of the physically based approach (PBA) to generate assembly sequence, which shows the efficiency and the operability to guide the assembly work.     

Robot programming system for assembly: Conceptual graph-based approach

This work develops a method for robot program synthesis. Currently, the programming task is one of the major hurdles of robotic application. Progress towards automatic synthesis of robot programs will ease industrial robot application. The proposed system provides a means towards automated (guided by knowledge) conversion of a user's request, expressed in natural language, to the appropriate conceptual model of the required task. This model incorporates the information necessary for understanding, planning, and sensory-guided performance of the required robotic task.First, we state the problem of robotic assembly and recognize its hierarchic structure as the structure of a system that builds a predesigned assembly. Next, we present and analyze the requirements of the robotic assembly domain. This analysis enables us to draw conclusions concerning the most suitable methodology for the development of a support system for assembly program synthesis and interpretation. It is the conceptual graph-based approach. Then we present the algorithm of the proposed conceptual graph-based system and show how the system synthesizes robotic assembly operations, such as valid assembly sequences and sequences of special treatments for the assembled components (including the determination of the required resources). Finally, a case study illustrates the approach developed here on a large family of multi-axisymmetric components. We also present illustrative examples of working sessions with the current implementation of the system.     

Temporal logic programming for assembly sequence planning

A temporal-constraint logic programming framework for the specification and automatic verification and synthesis of assembly sequences is developed. The implemented tool is based on the formulated and derived precedence properties for a general mechanical assembly. This tool, called the Mechanical Assembly Sequence Satisfiability Checker (MASS-C), supports the use of a subset of temporal logic for assembly constraint specification. MASS-C provides the logic programming framework by which the designer can be relieved of the tedium of finding the assembly sequences, and the assembly sequence planning process manifests itself in the implicit modelling of assembly sequences by acquiring and formulating the set of correct and complete assembly constraints as a logic program. MASS-C implements a class of temporal expressions as predicates for logic programming of assembly constraints. It provides facilities to either verify an assembly sequence or synthesise all assembly sequences that satisfy the specified constraints composed as a logic program. Two examples illustrate the use of MASS-C for such verification and synthesis.     

Analysis of assembly operations difficulty: a fuzzy expert system approach

Typical mechanical products can be assembled in various sequences of assembly operations. These sequences have high impact on the assembly time, machine utilization and even on the product quality. In order to select the best sequence of assembly operations, proper evaluation of the various sequences of assembly operations is required. This, in turn, requires the consistent evaluation of each assembly operation in the sequence. The assembly operations can be evaluated for various criteria, of which the operation difficulty is the most meaningful. This paper describes a methodology to analyse the assembly operations and calculates an operation's degree of difficulty using an expert system. This analysis consists of two steps: the first one identifies the main parameters that affect the assembly difficulty and assigns fuzzy triangular values to these parameters. The second step assigns weights to the parameters in order to maximize the agreement with a domain expert. The expert system analyses the difficulty of the assembly operation performed in two orientations: horizontal and vertical. The expert system then assigns a triangular fuzzy number as the aggregate measure of the operation's difficulty.     

Automated generation of assembly system-design solutions

Any assembly system-design problem which consists of generating system configurations and assignment of tasks to the stations has a set of solutions. The number of feasible solutions may be staggering, even for products consisting of a relatively small number of parts assembled on a small number of stations. As the size of the product and the assembly system grow, it may become difficult to develop a complete set of design solutions for in-depth analysis. Here an automated method is described by which the complete sets of system configurations and assembly sequences may be generated, and feasible solutions, consisting of a matched element from each set, can be rapidly derived. These solutions may then be tested for various performance metrics, some of which may not be expressed mathematically. In designing assembly systems, the layout of stations and the assignment of assembly tasks to these stations are important design problems. This paper proposes a set of algorithms to quickly generate the configurations of the assembly system and assign tasks to the configurations. Once the matching of tasks to the configurations is complete, the performance of these various design alternatives, such as productivity, can be evaluated to allow selection of configurations with the best performance.     

Survey on assembly sequencing: a combinatorial and geometrical perspective

A systematic overview on the subject of assembly sequencing is presented. Sequencing lies at the core of assembly planning, and variants include finding a feasible sequence—respecting the precedence constraints between the assembly operations—, or determining an optimal one according to one or several operational criteria. The different ways of representing the space of feasible assembly sequences are described, as well as the search and optimization algorithms that can be used. Geometry plays a fundamental role in devising the precedence constraints between assembly operations, and this is the subject of the second part of the survey, which treats also motion in contact in the context of the actual performance of assembly operations.     

Image and model sequences matching for on-site assembly stage identification

In an assembly task, stage identification determines the subsequent parts to be assembled and therefore, it affects the quality and efficiency of the entire assembly operation in computer-aided manufacturing. However, due to the similarity between the assemblies in two adjacent stages, it is difficult to distinguish the assembly accurately based on the current visual recognition method. Considering the time and progress logic of assembly procedures, a stage identification method based on the on-site assembly sequence and stage model sequence is proposed. The on-site assembly sequence is generated with the assembly progress to record the history information in the time order and the stage model sequence is preprocessed in the database in the progress order. On the one hand, these two sequences provide the constraints to avoid the inefficient and indistinguishable assembly images to be analyzed. On the other hand, the two sequences contain time and assembly progress logic, which can be used to solve the stage identification issue using an optimal mapping model, so as to improve the identification efficiency. This paper introduces the methodology from three aspects, namely, offline stage model sequence generation, online assembly sequence matching and assembly stage identification. The experiments proved the proposed method can effectively improve the accuracy of real-time assembly stage identification compared with the original object recognition method.     

A hybrid genetic algorithm approach on multi-objective of assembly planning problem

In practice, modeling an assembly system often requires assigning a set of operations to a set of workstations. The aim is to optimize some performance indices of an assembly line. This assignation is usually a tedious design procedure so a significant amount of manpower is required to obtain a good work plan. Poor assembly planning may significantly increase the cost of products and reduce productivity. However, these optimization problems fall into the class of NP-hard problems. Finding an optimal solution in an acceptable time is difficult, even using a powerful computer. This study presents a hybrid genetic algorithm approach to the problems of assembly planning with various objectives, including minimizing cycle time, maximizing workload smoothness, minimizing the frequency of tool change, minimizing the number of tools and machines used, and minimizing the complexity of assembly sequences. A self-tuning method was developed to correct infeasible chromosomes. Several examples were employed to illustrate the proposed approach. Experimental results indicated that the proposed method can efficiently yield many alternative assembly plans to support the design and operation of a flexible assembly system.     

Symbolic OBDD representations for mechanical assembly sequences

Assembly sequence planning is one typical combinatorial optimization problem, where the size of parts involved is a significant and often prohibitive difficulty. The compact storage and efficient evaluation of all the feasible assembly sequences is one crucial concern. Ordered binary decision diagram (OBDD) is a canonical form to represent and manipulate the Boolean functions efficiently, and appears to give improved results for large-scale combinatorial optimization problems. In this paper, subassemblies, assembly states and assembly tasks are represented as Boolean characteristic functions, and the symbolic OBDD representation of assembly sequences is proposed. In this framework, the procedures to transform directed graph and AND/OR graph into OBDDs are presented. The great advantage of OBDD-based scheme is that the storage space of OBDD-based representation of all the feasible assembly sequences does not increase with the part count of assembly dramatically so quickly as that of both directed graph and AND/OR graph do. We undertake many experimental tests using Visual C++ and CUDD package. It was shown that the OBDD scheme represented all the feasible assembly sequences correctly and completely, and outperforms either directed graph or AND/OR graph in storage efficiency.     

A scheme for mechanical assembly design and assembly line layout conceptualization

A scheme in this paper makes possible to analyze a complex assembly design at an early design stage and generate assembly sequences efficiently. The designer's knowledge is used to obtain the preference module set. The modulability of the set is tested and used to generate assembly sequences hierarchically. Sequences generated by this scheme reflect the designers' preference for modules and facilitates to figure out an assembly line layout from an assembly design. The applicability of the scheme is verified in an example.     

An interactive approach of assembly planning

An interactive approach to assembly planning is presented. It provides a virtual reality interface for production engineers to program the virtual representation of robotic manipulators in a three-dimensional (3D) operation space. The direct human involvement creates a user-defined assembly sequence, which contains the human knowledge of mechanical assembly. By extracting the precedence relationship of machinery parts, for the first time it becomes possible to generate alternative assembly sequences automatically from a single sequence for robot reprogramming. This interactive approach introduces human expertise into assembly planning, thus breaking down the computational complexity of autonomous systems. Experiments and analysis provide strong evidence to support the incontestable advantages of "manufacturing in the computer".     

Operator-specific model: An assembly time prediction model

A model was developed to predict assembly time for a circuit board assembly task. It was based upon the GOMS language developed for task time prediction in human-computer interaction. It incorporated sequential elements of information processing, defined by cognitive, motor, and perceptual processors, in a critical path network. The model was used to predict assembly time for four assembly strategies using four different combinations of workstation and assembly sequences. The model was useful in predicting cognitive changes associated with the development of expertise. As assemblers gained experience, structural changes in the network implied that several processors were chunked. © 1997 John Wiley & Sons, Inc.     

The symbolic OBDD scheme for generating mechanical assembly sequences

Assembly sequence planning is one of typical combinatorial optimization problems, where the size of parts involved is a significant and often prohibitive difficulty. The compact storage and efficient evaluation of feasible assembly sequences is one crucial concern. Ordered binary decision diagram (OBDD) is a canonical form to represent and manipulate the Boolean functions efficiently, and appears to give improved results for large-scale combinatorial optimization problems. In this paper, assembly knowledge models of liaison graph and translation function are formulated by OBDDs, and OBDD-based representation of assembly sequences is proposed. A novel OBDD-based procedure was presented to generate all geometrically feasible assembly sequences from the OBDDs of liaison graph and translation relation. This procedure can be used conveniently on the computer and all the feasible sequences can be derived. The great advantage of OBDD-based scheme is that the storage space of OBDD-based representation of feasible assembly sequences does not increase with the part count of assembly dramatically so quickly as that of AND/OR graph does. We developed the prototype tool for generating assembly sequence using Visual C++ and CUDD package, and undertake some experimental tests. It was shown that the OBDD scheme generated feasible assembly sequences correctly and completely.     

Differential evolution variants to schedule flexible assembly lines

Scarce resources such as material, labor, and equipment are to be optimized to improve the performance and lower production costs in flexible assembly lines. These resources are usually allocated optimally through the generation of schedules. In this paper, variants of a differential evolution-based algorithm are employed to schedule flexible assembly lines (FAL). The performance of the assembly line is optimized based on two performance criteria, namely the weighted sum of Earliness/Tardiness penalties and the balance of the assembly line. Different variants of the Bi-level differential evolution (BiDE) algorithms are developed to study the effects of three FAL problems. The parameters of BiDE algorithm for FAL problems are fine-tuned. The performance of the BiDE algorithm is evaluated using the datasets and the Bi-level Genetic Algorithm (BiGA) available in the literature. The experimental results show that the proposed differential evolution-based algorithm outperforms BiGA in terms of mean best fitness.     

Analysis of assembly operations’ difficulty using enhanced expert high-level colored fuzzy Petri net model

Majority of the products can be assembled in several ways that means the same final product can be realized by different sequences of assembly operations. Different degree of difficulty is associated with each sequence of assembly operation and such difficulties are caused by the different mechanical constraints forced by the different sequences of operations. In the past, few notable attempts have been made to represent and enumerate the degree of difficulty associated with an assembly sequence (in the form of triangular fuzzy number) by using the concept of assembly graph. However, such representation schemes do not possess the capabilities to model the user's reasoning and preferences. In this research, an intelligent Petri net model that combines the abilities of modelling, planning and performance evaluation for assembly operation is presented. This modelling tool can represent the issues concerning degree of difficulty associated with assembly sequences. The proposed mechanism is enhanced expert high-level colored fuzzy Petri net (EEHLCFPN) that is a hybrid of knowledge-based system and colored Petri net. An example encompassing assembly of subassemblies is considered to efficiently delineate the modelling capabilities of proposed hybrid petri net model.