Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms

Braking system plays a major safety feature in any vehicle; it is considered as one of the key points in safe motoring. The main objective of a good braking system is to provide safe stopping, in addition to the fact that it provides repeatable stopping. Anti-lock Braking System (ABS) is one of the recently developed safety feature used in a vehicle in order to prevent wheels from locking. This article proposes an intelligent adaptive fuzzy logic (FL) genetic algorithm (GA) controller for the Anti-Lock Braking System (ABS) to control the braking of a vehicle by controlling the slip ratio based on quarter car model. The main objective of the proposed controller is to prevent wheels from locking, which is a major safety issue in any braking system for different road conditions. The proposed controller consists of a Fuzzy Logic (FL) Genetic Algorithm (GA) based controller. The Genetic Algorithm is utilized in tuning the membership functions of the Fuzzy logic controller, which is developed to calculate the amount of reduction of the applied torque due to applying brake based on the error of the measured slip ratio signal compared with the desired one. Then a Genetic Algorithm (GA) is utilized in tuning the Fuzzy Logic parameters, which are the center of the Gaussian membership function, and the spread (б). The simulation results of the proposed fuzzy logic genetic algorithm controller are compared with both the results of the Fuzzy logic controller and the PD controller developed in previous work of the authors. The simulation results present many advantages of using Fuzzy Logic Genetic Algorithm based controller over both the Fuzzy Logic and the PD controllers, and the simulation is carried out at different road conditions, namely dry, wet and icy road surface.

Disassembly planning and sequencing play an important role in recycling a fast-growing number of end-of-life products. Optimal sequences can effectively reduce carbon emissions and save natural resources in the remanufacturing industry. Considering the development of intelligent manufacturing technology, this work deals with the optimization problem of selective disassembly sequences with an objective of maximizing disassembly profit. Disassembly sequences are generated based on AND/OR graphs. After setting up an environment matrix based on such graphs, this proposes a Q-learning technique to find an selective optimal disassembly sequence. The algorithm is applied to real-life disassembly cases. Experimental results show that the algorithm is superior a popularly-used genetic algorithm (GA) in both computing speed and solution quality through their various comparisons.

The representation of the disassembly sequence is a key issue in maintenance planning. It involves a highly constrained combinatory problem, which is coupled with varying start and end nodes. These start and end nodes are dependent upon the nature of a maintenance task. In this paper, different representation and modelling schemes for disassembly sequence planning are first reviewed. Then, a novel representation scheme for disassembly sequence, which is generic and can be used to represent both the geometrical and precedence constraints dynamically in product disassembly, is proposed. Based on such a representation scheme, the process for the determination of possible disassembly sequences can be simplifled. By taking into consideration disassembly constraints, the optimal disassembly sequence in relation to the component to be maintained (target component) can be quickly derived. This is achieved by pruning the search space of disassembly sequences, grouping related components into subassemblies, and identifying free components to facilitate disassembly operations. Subsequently, the optimal disassembly sequence in relation to the target component can be obtained using genetic algorithms. In this manner, the disassembly sequences for a complex product comprising a relatively large number of components can be derived within a short time. A case study is used to illustrate the effectiveness of the representation scheme. Comparisons are made using the same case study with the AND/OR graph representation and the Petri net approach for disassembly sequence planning. The results show that the proposed representation scheme is simpler and is more efficient than the rest.

At the end-of-life (EOL) of a product, there are several options available for its processing including reuse, remanufacturing, recycling and disposing. In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as the reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly sequencing be developed. In this paper, we present a genetic algorithm for disassembly sequencing of EOL products. A case example is considered to demonstrate the functionality of the algorithm.

The generation of a set of optimal or near-optimal disassembly sequences is an important task associated with the study of disassembly planning. This task encapsulates actions at the required disassembly depth to reach maximum net revenue while satisfying economic constraints. The Petri net modelling, combined with heuristic search procedures developed in this study, offers an efficient procedure for disassembly sequence generation. The heuristic generates and searches a partial reachability graph to arrive at an optimal or near-optimal disassembly sequence based on the firing sequence of transitions of the Petri net model. The proposed methodology reduces the search space in two areas: (1) pruning the disassembly tree (DT) and, (2) selective tracking of the reachability graph. The efficacy of the proposed methodology is demonstrated by using two examples from the literature.

Nowadays in order to perform assembly process planning by using CAPP (Computer Aided Process Planning), researches to generate the assembly sequence have come under constant interest by many researchers and they are devoted to optimize the assembly sequence according to the novel approach such as Genetic Algorithm and the newly biology inspired principle such as ant colony optimizing method. In this paper, the developed assembly sequence generating system analyzes the relationship between assembled parts based on surface contacting information. Moreover, this system generates the new type of parts liaison graph for the assembly sequences via analyzed information such as common area between parts, related ratio and number of the connected parts. Finally, the optimized assembly sequence is generated logically through the parts liaison graph.KeywordsAssembly SequenceLiaison GraphRelated RatioCommon Area

The goal of assembly sequencing is to plan a feasible series of operations to construct a product from its individual parts. Previous research has thoroughly investigated assembly sequencing under the assumption that parts have nominal geometry. This paper considers the case where parts have toleranced geometry. Its main contribution is an efficient procedure that decides if a product admits an assembly sequence with infinite translations that is feasible for all possible instances of the components within the specified tolerances. If the product admits one such sequence, the procedure can also generate it. For the cases where there exists no such assembly sequence, another procedure is proposed which generates assembly sequences that are feasible only for some values of the toleranced dimensions. If this procedure produces no such sequence, then no instance of the product is assemblable. Finally, this paper analyzes the relation between assembly and disassembly sequences in the presence of toleranced parts. This work assumes a simple, but non-trivial tolerance language that falls short of capturing all imperfections of a manufacturing process. Hence, it is only one step toward assembly sequencing with toleranced parts.

PurposeThe purpose of this paper is to propose a framework to identify all the feasible disassembly sequences for a multi‐component product and to find an optimal disassembly sequence, according to specific criteria such as cost, duration, profit, etc.Design/methodology/approachTaking into account topological and geometrical constraints of a product structure, an AND/OR disassembly graph is built. Each graph node represents a feasible subassembly. Two nodes i and j are connected by an arc (i, j), called a transition, if the subassembly j can be obtained from the subassembly i by removing one or several connectors. Constraint programming approach is used to generate the feasible subassemblies and related transitions.FindingsIf a cost zij is incurred to perform a transition (i, j), an optimal disassembly sequence can be generated for a given subassembly, using the shortest path algorithm or a linear programming model.Research limitations/implicationsThe proposed approach performs very well compared to other approaches published in the literature, even when applied to products requiring parallel disassembly and including a large number of parts.Practical implicationsThis approach has been successfully applied to assess the wheelchair maintainability at the design stage and will be implemented in CAD systems. One other application, regarding the disassembly process and total revenue maximization for product recycling, is now under consideration.Originality/valueApplying constraint programming to efficiently generate the set of the feasible subassemblies constitutes the main contribution in this paper. This process is the hardest step in the disassembly sequencing problem.

Assembly planning calls for the subtle consideration of certain limitation factors such as geometric features and tools so as to work out a specific assembly sequence. From the assembly sequence, all parts will be turned into a product. It is evident that the degree of complexity of the assembly problem will increase when the number of constraints is larger. Using Genetic Algorithms (GAs) to solve the assembly sequence features speed and flexibility can fit the requirements of various domains. In the case of larger constraint assembly problems, however, GAs will generate a large number of infeasible solutions in the evolution procedure, thus reducing the efficiency of the solution-searching process. Traditionally, using GAs is a random and blind-searching procedure in which it is not always the case that the offspring obtained through the evolutionary mechanism will meet the requirements of all limitations. In this study, therefore, Guided-GAs are proposed wherein the proper initial population and the alternation of crossover and mutation mechanisms are covered to overcome assembly planning problems that contain large constraints. The optimal assembly sequence is obtained through the combination of Guided-GAs and the Connector-based assembly planning context as previously suggested. Finally, practical examples are offered to illustrate the feasibility of Guided-GAs. It is found that Guided-GAs can effectively solve the assembly planning problem of larger constraints.

Introduction: With regard to increasing population of country, need to high agricultural production is essential. The most suitable method for this issue is high production per area unit. Preparation much food and other environmental resources with conservation of biotic resources for futures will be possible only with optimum exploitation of soil. Among effective factors for the most production balanced addition of fertilizers increases production of crops higher than the others. With attention to this topic, determination of soil fertility degree is essential tobetter use of fertilizers and right exploitation of soils. Using fuzzy logic and Analytic Hierarchy Process (AHP) could be useful in accurate determination of soil fertility degree. Materials and Methods: The study area (at the east of Rasht city) is located between 49° 31' to 49° 45' E longitude and 37° 7' to 37° 27' N latitude in north of Guilan Province, northern Iran, in the southern coast of the Caspian sea. 117 soil samples were derived from0-30 cm depth in the study area. Air-dried soil samples were crushed and passed through a 2mm sieve. Available phosphorus, potassium and organic carbon were determined by sodium bicarbonate, normal ammonium acetate and corrected walkly-black method, respectively. In the first stage, the interpolation of data was done by kriging method in GIS context. Then S-shape membership function was defined for each parameter and prepared fuzzy map. After determination of membership function weight parameters maps were determined using AHP technique and finally soil fertility map was prepared with overlaying of weighted fuzzy maps. Relative variance and correlation coefficient criteria used tocontrol groups separation accuracy in fuzzy fertility map. Results and Discussion: With regard to minimum amounts of parameters looks some lands of study area had fertility difficulty. Therefore, soil fertility map of study area distinct these lands and present soil fertility groups for better management of soil and plant nutrition. Weight of soil parameters was0.54, 0.29 and 0.17 for organic carbon, available phosphor and potassium, respectively. Fuzzy map of study area includes five soil fertility groups as: 22.9% very high fertility, 27.7% high fertility, 35.53% medium fertility, 10.48% low fertility and 3.39% very low fertility. Consequently, a separated map for soil fertility prepared to evaluate soil fertility of study area for rice cultivation. Toinvestigatethe efficiency of fuzzy model and AHP in increasing the accuracy of soil fertility map, soil fertility map with Boolean method prepared as well. Boolean map showed 58.88% fertile and 41.12% unfertile.15 soil samples from different soil fertility groups of study area were derived fromcontrol of maps accuracy. 13 renewed samples of 15 and 9 soil samples have matched with fuzzy and Boolean map, respectively. Comparison of parameters mean in fuzzy map fertility groups showed that parameters mean amounts of very high and high fertility groups are higher than optimum level except potassium that is a few lower than optimum level in high fertility group, therefore, addition of fertilizers in these groups could not be useful to increase rice crop production. Phosphorus parameter amount is lower than the critical level in very low, low and medium fertility groups, then in these groups phosphorus fertilizer should be added to the soil toincreaserice production. The amount of potassium parameter is higher than the critical level and lower than optimum limit in very low, low, medium and high fertility groups, then in these groups addition of potassium fertilizer will results in theincrease of production. Organic carbon amount is lower than optimum level in very low and low fertility groups. With regard to the relation between organic carbon andnitrogen and phosphorus, therefore, the addition of organic carbon fertilizer could compensate deficit of nitrogen and phosphorus in these groups as well. Attention to the presented explanations and comparison of fuzzy and Boolean maps using parameters amounts in renewed sampling points for control of maps accuracy, it is distinct that fuzzy logic could influencetheoptimum using of fertilizers with increasing map efficiency and accuracy. In addition, relative variance and correlation coefficient amounts showed that fuzzy map has separatedquite wellparameters changes. Conclusion: Effective parameters in soil fertility, includingorganic carbon, phosphorus and potassium were used topreparesoil fertility map for rice cultivation. With regard to the minimum amounts of parameters looks some lands of study area had fertility difficulty. Therefore, soil fertility map of study area distinct these lands and presents soil fertility groups tobetter management of soil and plant nutrition. Fuzzy and Boolean methods were used topreparesoil fertility map. Comparison of these two approaches showed that fuzzy method with AHP caused to increase theefficiency and accuracy of fertility map for rice. Separated and distinguish soil fertility groups in fuzzy map help suitable distribution and optimum use of fertilizers for rice production.

This paper addresses the problem of creating intelligent, green, and financially-beneficial disassembly sequences for end-of-life (EOL) electronic products. These complex EOL products contain a broad spectrum of materials including precious metals. Therefore, one would have to process these products to retrieve the value buried in them. EOL processing options include, reuse, remanufacturing, recycling or proper disposal. Each of this option requires a certain level of disassembly. Hence, obtaining an optimal or near optimal disassembly sequence is crucial to increasing the efficiency of EOL processing. Since the complexity of determining the best disassembly sequence increases as the number of parts in a product grows, an efficient methodology is required for disassembly sequencing. In this paper, we present an evolutionary algorithm for generating near-optimal and/or optimal sequences for selective disassembly of EOL products. A numerical example is provided to demonstrate the functionality of the algorithm.

Multi-sequence modelling is proposed to optimise assembly sequences for compliant body assemblies such as automotive and aircraft bodies. Multi-sequence modelling is composed of assembly modelling, assembly sequences, tolerance analysis, and assembly operations. Assembly modelling describes the geometric modelling through the liaison graph, which is based on assembly sequence optimisation. Assembly sequences provide information for assembly sequence optimisation. Three-dimensional tolerance analysis is employed to evaluate assembly operations, which is different from previous conceptual tolerance analysis. A genetic algorithm is presented to optimise assembly operations among components. Results show that different sequences lead to a different tolerance of key product characteristics because assembly operations among components are not identical. This paper provides the flowchart of optimising assembly sequences according to the tolerance of key product characteristics.

Assembly and disassembly sequences were planned and assessed independently. However, as eco-friendly product design has been enforced by government regulation, it is needed to plan assembly and disassembly sequence at the same time in early product design stage. This paper aims to propose a sequence structure design method by integrating both assembly and disassembly sequence planning. First, graphs represent feasible assembly sequence with components. Second, adjacency matrices are developed to accumulate the sequence information for computational analysis. Third, a black box diagram is applied to understand and consider the functions of a product during assembly and disassembly operations. Finally, factors are determined to overcome gaps between each assembly and disassembly sequence structure. By considering product architecture with Design Structure Matrix (DSM), we can determine modular design for selective disassembly and interface type for assembly. A case study is performed with two coffee makers to demonstrate the effectiveness of the proposed method.

An ontology and rule-based method for human–robot collaborative disassembly planning in smart remanufacturing

If a product is made of more than one component, then it needs to be assembled. Complexity of assembling a product is largely subject to the implication of product design as well as the number of assembly components that may also affect attainable assembly sequences. In addition, it is widely understood that efficiency of assembling a product can be partially achieved by reducing assembly times (therefore costs) and this is notably important for small and medium-sized manufacturing companies. Thus, it is useful for seeking an optimal assembly sequence of a product at the early product design stage. This paper presents a study by applying the genetic algorithm (GA) for solving an assembly sequence optimisation problem of a car engine pump valve providing a quick solution in obtaining an optimal or near-optimal assembly sequence of the product.