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Traditional production management systems are often designed to support manufacturing based on a limited number of product variants. With the emerging trend of producing customized products to meet diverse customer needs, the number of product variants increases exponentially in mass customization. In a situation of assembly‐to‐order production, production planning and control involve not only product variety, but also process variety. It is imperative to synchronize product and process variety in a coherent manner.

This paper discusses integrated product and production data management for assembly‐to‐order production. An integrated BOM and routing generator is proposed for the purpose of unifying BOM and assembly‐planning data in order to accommodate a wide range of product variability and production variations.

An integrated BOM and routing generator excels in variety synchronization for assembly‐to‐order production planning.

Variety synchronization opens many opportunities for research into mass customization production. It is important to deal with not only the results of high variety production but also the causes of process variations.

The proposed methodology is applicable to manage high variety production like mass customization.

The paper proposes the variety synchronization issue in mass customization. An object‐oriented methodology is applied to manage variety of BOMs and variety of routings.

The purpose of this paper is to develop a planner for finding an optimal assembly sequence for robots to assemble objects. Each manipulated object in the optimal sequence is stable during assembly. They are easy to grasp and robust to motion uncertainty.

The input to the planner is the mesh models of the objects, the relative poses between the objects in the assembly and the final pose of the assembly. The output is an optimal assembly sequence, namely, in which order should one assemble the objects, from which directions should the objects be dropped and candidate grasps of each object. The proposed planner finds the optimal solution by automatically permuting, evaluating and searching the possible assembly sequences considering stability, graspability and assemblability qualities.

The proposed planner could plan an optimal sequence to guide robots to do assembly using translational motion. The sequence provides initial and goal configurations to motion planning algorithms and is ready to be used by robots. The usefulness of the proposed method is verified by both simulation and real-world executions.

The paper proposes an assembly planner which can find an optimal assembly sequence automatically without teaching of the assembly orders and directions by skilled human technicians. The planner is highly expected to improve teachingless robotic manufacturing.

Flexible automated assembly is an emerging need in several industries. The purpose of this paper is to address the introduction of an innovative concept in flexible assembly: the fully flexible assembly system (F‐FAS).

After an analysis of the state of the art, the authors describe the proposed F‐FAS, from a layout, constitutional elements, functioning principles and working cycle point of view. Second, the authors compare the traditional FAS and the manual assembly system versus the proposed F‐FAS according to their throughput and unit production costs, deriving a convenience map as a function of the number of components used in assembly and of the efficiency of the F‐FAS. Finally, using a prototype work cell developed at the Robotics Laboratory of University of Padua, the authors validate the F‐FAS concept.

Results of the research indicate that the concept of full‐flexibility can be exploited to bring automation to a domain where traditional FAS are not competitive versus manual assembly. In fact, the F‐FAS outperforms both traditional FAS and manual assembly, in terms of unit direct production costs, when the size of the batch is small, the number of components used in assembly is large and the efficiency of the F‐FAS is reasonably high. The F‐FAS prototype demonstrated the possibility of working, for certain conditions (models/components/production mix), in the F‐FAS convenience area, highlighting the achievable cost reduction versus traditional assembly systems.

The novelty of the study lies in the F‐FAS concept, its performances in terms of flexibility, compactness, throughput and unit direct production costs. A prototype work cell validated the concept and demonstrated its viability versus traditional assembly systems, thanks to convenience analysis.

THE increasing pressure of national and international competition is forcing firms to rationalise even further, especially in the field of assembly. In order to perform assembly tasks with the least possible expenditure of time, assembly facilities, space requirements and personnel, it has become necessary to include these objectives in the development stage of the product. Therefore, assembly‐oriented design now provides a good opportunity for rationalisation.

The problems involved in assembly of packaged ready‐to‐assemble (RTA) products such as bicycles, furniture, etc., are well known. Some of the factors that contribute to the difficulty in assembly are unclear assembly instructions and poor grouping of parts in packages. Typically, RTA products are packed in one bulky carton with some parts, such as hardware, grouped in a separate package within the carton. As a result, consumers often spend a significant portion of the assembly time on searching for the right part. Through an experimental study we demonstrate that the assembly time can be significantly reduced by forming logical part groups (packages within the carton) according to the sequence of assembly operations. Our result is significant in the context of recent surveys that indicate customer dissatisfaction with the assembly of RTA products.

In this paper, an assembly sequence planning system, based on binary vector representations, is developed. The neural network approach has been employed for analyzing optimum assembly sequence for assembly systems.

The input to the assembly system is the assembly's connection graph that represents parts and relations between these parts. The output to the system is the optimum assembly sequence. In the constitution of assembly's connection graph, a different approach employing contact matrices and Boolean operators has been used. Moreover, the neural network approach is used in the determination of optimum assembly sequence. The inputs to the networks are the collection of assembly sequence data. This data is used to train the network using the back propagation (BP) algorithm.

The proposed neural network model outperforms the available assembly sequence‐planning model in predicting the optimum assembly sequence for mechanical parts. Due to the parallel structure and fast learning of neural network, this kind of algorithm will be utilized to model another types of assembly systems.

In the proposed neural approach, the back propagation algorithm is used. Various training algorithms can be employed.

The simulation results suggest that the neural predictor would be used as a predictor for possible practical applications on modeling assembly sequence planning system.

This paper discusses a new modelling scheme known as artificial neural networks. The neural network approach has been employed for analyzing feasible assembly sequences and optimum assembly sequence for assembly systems.

Steam turbine final assembly is a dynamic process, in which various interference events occur frequently. Currently, data transmission relies on oral presentation, while scheduling depends on the manual experience of managers. This mode has low information transmission efficiency and is difficult to timely respond to emergencies. Besides, it is difficult to consider various factors when manually adjusting the plan, which reduces assembly efficiency. The purpose of this paper is to propose a knowledge-based real-time scheduling system under cyber-physical system (CPS) environment which can improve the assembly efficiency of steam turbines.

First, an Internet of Things based CPS framework is proposed to achieve real-time monitoring of turbine assembly and improve the efficiency of information transmission. Second, a knowledge-based real-time scheduling system consisting of three modules is designed to replace manual experience for steam turbine assembly scheduling.

Experiments show that the scheduling results of the knowledge-based scheduling system outperform heuristic algorithms based on priority rules. Compared with manual scheduling, the delay time is reduced by 43.9%.

A knowledge-based real-time scheduling system under CPS environment is proposed to improve the assembly efficiency of steam turbines. This paper provides a reference paradigm for the application of the knowledge-based system and CPS in the assembly control of labor-intensive engineering-to-order products.

This roadmap is primarily concerned with the adaptive assembly technology situation in Europe, a topic of particular interest as assembly is often the final process within manufacturing operations. Being the final set of operations on the product, and being traditionally labour‐intensive, assembly has been considerably affected by globalisation. Therefore, unlike most technology roadmaps, this report will not focus solely on particular technologies, but will strive to form a broader perspective on the conditions that may come to influence the opportunities, including political aspects and scientific paradigms. The purpose of this paper is to convey a complete view of the global mechanisms that may come to affect technological breakthroughs, and also present strategies that may better prepare for such a forecast.

The paper describes a technological roadmap.

This paper provides a complete overview of all aspects that may come to affect assembly in Europe within the next 20 years.

The paper gives an original Evolvable Ultra Precision Assembly Systems FP6 project result which will be of general interest for strategic R&D.

The ever‐increasing demand for rationalization of manufacturing activities requires closer co‐operation and integration between design and production planning. Since the impact on the overall cost of production of designing a product for ease of assembly was realized, feedback from assembly planning to the design process has become important for improving the design. Traditionally, planning for assembly takes place after the design of the product has been completed. Often it proves necessary to modify a design, because some aspect is not particularly convenient or efficient under the available assembly facilities. However, considerable time and cost savings can be made by making assembly‐planning information available to design engineers, so that they can take account of assembly factors when designing a product.

The assembly‐initiated production (AIP) project aims at developing a strategy for mass‐customisation with short lead times through the production. The presented results are the outcome of cooperative work between KTH and 19 companies of different sizes, active in Sweden. AIP is formed around the idea to assemble products from product modules on customer orders. The total delivery time would be time to process order + assembly time + shipping‐time. This gives a total delivery time considerably shorter than when manufacturing the entire product to order. There are many factors to consider. Challenges like modularising the products to fit the strategy, finding ways to automate assembly and manufacturing operations, and at the same time, accomplish a flexible production solution. There are also many other factors to consider being successful in mass customisation, like materials supply and material handling issues, information system design and creating a suitable organisation form. Of utmost importance is the way the factors affect each other and the production as a whole when changes are made.