Changeable Manufacturing - Classification, Design and Operation

Manufacturing enterprises are changing the way they behave in the market to face the increasing complexity of the economic, socio-political and technological dynamics. Manufacturing products, processes and production systems result in being challenged by evolving external drivers, including the introduction of new regulations, new materials, technologies, services and communications, the pressure on costs and sustainability. The co-evolution paradigm synthesises the recent scientific and technical approaches proposed by academic and industrial communities dealing with methodologies and tools to support the coordinated evolution (co-evolution) of products, processes and production systems. This paper aims at reviewing and systemising the research carried out in the field of manufacturing co-evolution with a particular focus on production systems. An introductory investigation of various industrial perspectives on the problem of co-evolution is presented, followed by the description of the co-evolution model and the methodology adopted for framing the existing scientific contributions in the proposed model. Then, the core part of the work is presented, consisting in a systemised analysis of the current methodologies dealing with co-evolving product, process and system and a description of problems that remain unsolved, thus motivating future research strategies and roadmaps.

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SPECIES—Co-evolution of products, processes and production systems

Reconfigurable manufacturing systems (RMS) are considered the future of manufacturing, being able to overcome both dedicated (DMS) and flexible manufacturing systems (FMS). In fact, they provide significant cost and time reductions in the launch of new products, and in the integration of new manufacturing processes into existing systems. The goals of RMS design are the extension of the production variety, the adaption to rapid changes in the market demand, and the minimization of the investment costs. Despite the interest of many authors, the debate on RMS is still open due to the lack of practical applications. This work is a review of the state-of-the-art on the design of cellular RMS, compared to DMS, by means of optimization. The problem addressed belongs to the NP-Hard family of combinatorial problem. The focus is on non-exact meta-heuristic and artificial intelligence methods, since these have been proven to be effective and robust in solving complex manufacturing design problems. A wide investigation on the most recurrent techniques in DMS and RMS literature is performed at first. A critical analysis over these techniques is given in the end.

A review on artificial intelligence applications to the optimal design of dedicated and reconfigurable manufacturing systems

Reconfigurable Process Plans For Responsive Manufacturing Systems

A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment

A computer-aided process planning (CAPP) and machine assignment system, capable of coping with the changes in products and system, are required for reconfigurable manufacturing systems (RMS) or other dynamic manufacturing environments. The capabilities of the machines and manufacturing systems in RMS change with each configuration. An approach is presented for selecting the different types of machine(s) and their appropriate configurations to produce different types of parts and features, according to the required machine capabilities. The structure of the machine tool is represented in a kinematic chain-like format that captures the number, type and order of different axes of motion on both the tool or workpiece side for the machine. The proposed method is illustrated with an example. More than one minimum machine configuration may be generated for a single-operation cluster, which increases the flexibility in machine tool selection. The developed approach is general in nature and not limited to RMS. It is applicable to any manufacturing system where dynamic and flexible process planning and machine assignments are required. It could serve as an important tool in aiding the machine assignment/selection and process planning activities at the initial stages of manufacturing systems design and subsequent changes in products scope, and facilities expansion, downsizing and modification.

Generation of machine configurations based on product features

A new model for optimizing the manufacturing cost of process plans for reconfigurable manufacturing systems (RMS) using genetic algorithms is proposed. The model represents machines' configurations in the process plan representation string. It considers all process planning parameters simultaneously while simplifying the problem formulation and reducing the computational complexity by choosing the following parameters: machine assignment, machine configuration, operation sequencing, operation cluster sequencing and assigning the tools and tool approach directions (TAD) to the operations. A new approach is proposed, which guarantees that operations with specific constraints are clustered together. A string of continuous variables to represent a process plan is introduced. A new method, which insures that any randomly generated chromosome will result in a feasible process plan, has been developed. The problem formulation is presented and illustrated with two examples and the results are presented and analysed. The results showed that process planning for RMS would cost less, depending on the different cost indices. The presented optimal process planning method can also be used in aiding the part/machine assignment activities and in the initial design stage of reconfigurable manufacturing systems (RMS).

A. I. Shabaka

Papers

Generation of machine configurations based on product features

A model for generating optimal process plans in RMS