Reconfigurable Machine Tools Research Articles

Optimal configuration selection in reconfigurable manufacturing system considering variable production rate

Various manufacturing companies produce different components, parts, or entire products based on market demands. These needs are met by a variety of manufacturing systems. However, in today's dynamic environment where production capacities and company capabilities are subject to change, reconfigurable manufacturing systems (RMS) have emerged as crucial resources. At the heart of RMS lies the reconfigurable machine tool (RMT), which is designed with modular components to adapt to varying requirements. Evaluating the performance of RMS requires an index. This paper employs weighted sum theory to map various characteristics of RMS and develop a cumulative reconfigurability index. To illustrate the applicability of this methodology, a case study is presented, examining both machine and system levels. There are four configurations having eight RMTs arranged in different manner. The cumulative reconfigurability index is calculated for each configuration and the optimal configuration is selected. From this research, it has been observed that the reconfigurability index of system (d) is the highest, with system (a) having a value close to that of system (d).

An integrated lot-sizing and scheduling problem in a reconfigurable manufacturing system under workforce constraints

ABSTRACT Nowadays, achieving higher levels of flexibility in manufacturing systems is necessary to maintain and enhance competitiveness. Accordingly, a new generation of production machine, namely Reconfigurable Machine Tool (RMT), has recently been introduced that can be effectively adapted to changes. Nevertheless, such systems are more worker-reliant, and neglecting workforce aspects results in suboptimal or even infeasible production schedules. In this regard, this study investigates an integrated lot-sizing and scheduling problem benefiting from RMTs under workforce constraints. First, a novel Mixed-Integer Linear Programming (MILP) model is provided to formulate the problem. Afterward, to confront the high complexity of the problem, an efficient Decomposition Heuristic (DH) empowered by a tailored feasibility cut is devised. A combination of MILP and Constraint Programming (CP) is employed in the DH to model the relevant master and sub-problems, respectively. Finally, the performance of the DH compared to the MILP model and an extended lower bound is evaluated. Moreover, the advantages of utilizing RMTs in the system are explored based on four defined key performance indicators.

A multi-objective joint optimisation method for simultaneous part family formation and configuration design in delayed reconfigurable manufacturing system (D-RMS)

ABSTRACT In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method.

A NSGA-II based approach for multi-objective optimization of a reconfigurable manufacturing transfer line supported by Digital Twin: A case study

In response to the wide range of customer demands, the concept of reconfigurable manufacturing systems (RMS) was introduced in the industrial sector. RMS enables producers to meet varying volumes of demand over varying time periods by swiftly adjusting its production capacity and functionality within a part family in response to abrupt market changes. In these circumstances, RMS are made to swiftly reconfigure their Reconfigurable Machine Tools (RMTs). RMTs are designed to have a variety of configurations that may be conditionally chosen and reconfigured in accordance with specific performance goals. However, the reconfiguration process is not an easy process, which entails optimization of several objectives and many of which are inherently conflictual. As a result, it necessitates real-time monitoring of the RMS's condition, which may be achieved by digital twinning, or the real-time capture of system data. The concept of using a digital replica of a physical system to provide real-time optimization is known as digital twin. This work considered a case study of discrete parts manufacturing on a reconfigurable single manufacturing transfer line (SMTL). Six manufacturing operations are required to be performed on the parts at six production stages. This work uses the Digital Twin (DT) based approach to assist a discrete multi-objective optimization problem for a reconfigurable manufacturing transfer line. This multi-objective optimization problem consists of four objective functions which is illustrated by using DT-based Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The innovative aspect of the current study is the use of a DT-based framework for RMS reconfiguration to produce the best optimum solutions. The produced real-time solutions will be of great assistance to the decision maker in selecting the appropriate real-time optimal solutions for reconfigurable manufacturing transfer lines.

Operator-Based Adaptive Tracking Capacity Control in Complex Manufacturing Processes

Nowadays, quickly changing customer demands are a big challenge in the manufacturing industry, especially for job shops, which are typical coupling and nonlinear multi-input–multi-output (MIMO) systems. In order to achieve good shop floor performance in the presence of short-term demand fluctuations, a key performance indicator—work in process (WIP)—is required to be effectively controlled in the vicinity of the desired levels. For this purpose, a machinery-oriented capacity adjustment approach via a reconfigurable machine tool (RMT) is employed to flexibly balance capacity and load in the case of a bottleneck. A mathematical model concerning the RMT and WIP was first established in the presence of uncertainty and delays. The operator-based robust right coprime factorization (RRCF) method was adopted to stabilize the uncertain system, and adaptive integral separated proportional–integral (ISPI) tracking controllers were further designed to improve the transient and robustness performance. The performance of the proposed ISPI-RRCF was analyzed and compared with that of a state-of-the-art method in a simulation. The results showed that both control systems could ensure that the WIP was within an allowed bound, while the former had lower overshoots, shorter setting times, and more concentrated distributions facing stochastic demands. This further indicated the effectiveness of the proposed algorithm in the avoidance of serious bottlenecks and unbalanced capacity distributions.

An improved event-triggered predictive control for capacity adjustment in reconfigurable job-shops

In order to regulate work in process (WIP) to the desired value in the job shop production control system, capacity adjustment as an effective and efficient measure, which is typically achieved by flexible staffs and working time. In this paper, instead of traditional labour-oriented approaches, we consider a machinery-based capacity adjustment via reconfigurable machine tools (RMTs) to compensate for unpredictable events. To this end, we employ model predictive control (MPC) in combination with genetic algorithm (GA) to explicitly consider complex reconfiguration strategies and address the related integer assignment optimisation problems. To further reduce energy consumption and avoid frequent and unnecessary reconfigurations while keeping a certain level of performance, we adopt an event-triggered MPC scheme with the proposed ‘Double-layer event-triggering conditions’. Through extensively illustrated simulations, we demonstrate the effectiveness and plug-and-play availability of the proposed method for a six-workstation four-product job shop system and compare it to a state-of-the-art method.

A centroid opposition-based coral reefs algorithm for solving an automated guided vehicle routing problem with a recharging constraint

Automated guided vehicles (AGVs) have been utilized extensively in material handling systems. In this paper, a synchronization problem of task scheduling and AGV routing is studied on a shop floor with reconfigurable machine tools. The objective is to minimize the maximum completion time (i.e., makespan) of all products. Studying scheduling and routing problems simultaneously, we propose a centroid opposition-based coral reefs optimization (COCRO) algorithm combined with a heuristic. The developed heuristic uses the Dijkstra algorithm to extract collision-free paths. To test the performance of the proposed algorithm, its results are compared to the results of the algorithm used in the literature recently. The results indicate the superiority of the COCRO algorithm. Finally, the performance of the system under different circumstances is analyzed. Based on the results, it can be claimed that reconfigurable machines reduce production time by up to 35%. Furthermore, the capacity of AGVs has a more significant role than the number of AGVs in the production time. Also, comparing four widely used types of batteries (i.e., sealed GEL, AGM pure-lead, Lithium, and flooded lead-acid), the result confirms the superiority of Lithium batteries.

Optimization of a multi-objective lean assembly problem with reconfigurable machine cells

Manufacturing sector is regularly facing the challenges caused due to high market dynamics and mass customization. Traditionally designed machine cells fail to address this issue due to lack of flexibility in capacity and functionality. However, the benefits of cell based production can be achieved by changing the machines involved and the design of cells. This paper presents a hybrid model of machine cells comprising of reconfigurable machine tools (RMTs) that are acting as part feeders for a lean assembly line of discrete products. The strategies of lean manufacturing to maintain the Takt time and synchronized one-piece-flow are considered in the model. A multi-objective optimization problem is formulated and solved to minimize the inter-cellular part movement, the error for Takt time among machine cells and the total reconfiguration time of the RMTs using NSGA-II metaheuristic. A numerical case example for the model is solved using MATLAB© and illustrated along with computational steps and Pareto optimal solutions.

Machining with a Precision Five-Axis Machine Tools Created by Combining a Horizontal Parallel Three-Axis Motion Platform and a Three-Axis Machine Tools.

Five-axis working machines are applied in the high-precision machining of complex convex surfaces. Therefore, this study integrated a horizontal parallel three-axis motion platform and a three-axis machine tools to create a reconfigurable precision five-axis machine tools (RPFMT). A DELTA OPEN computer numerical control controller was used as the control system architecture. A human–machine interface and programmable controller were incorporated into the developed tool to achieve automatic online measurement. A suitable cutting tool was selected to calculate the five-axis NC machining code for a complex convex surface. The NC codes were input into the LabVIEW software for five-axis postprocessing conversion. A concave workpiece was cut through rough and finishing machining to verify the accuracy of the produced RPFMT.

Integrated Lot-sizing and Job Shop Scheduling Benefiting From Reconfigurable Machine Tools

In recent years, the reconfigurable manufacturing system has received a great deal of attention as an effective manufacturing paradigm to tackle high diversity and volatility of demand. Its effective implementation requires responsive production planning to provide accurate capacity and functionality needed at any given time of the planning horizon. In the present work, an integrated lot-sizing and job shop scheduling problem as one of the most challenging problems in production planning is investigated considering reconfigurable machine tools. In this regard, a novel mathematical model is developed for simultaneous optimization of the lot-sizing, scheduling, and machine configuration to meet all demands with minimum operational costs, including setup, production, inventory holding, and machine reconfiguration. Three key performance indicators (KPIs) are considered to evaluate the system performance, including schedulability, optimality, and responsiveness. Finally, computational experiments are conducted affirming promising improvement in the KPIs by incorporating the machine reconfigurability in the integrated lot-sizing and job shop scheduling problem.

An improved genetic algorithm for flexible job shop scheduling problem considering reconfigurable machine tools with limited auxiliary modules

Reconfigurable manufacturing system is widely regarded as a major drive towards the next-generation manufacturing, where one of the most common scenarios is that advanced reconfigurable machine tools (RMT) are equipped with auxiliary modules (AM) to improve the production flexibility, thus rapidly responding to market demands. Production scheduling is facing great challenges in this situation, because the limited number of AMs becomes the primary difficulty for the resource allocation. This paper investigates a flexible job shop scheduling problem with machine reconfigurations (FJSP-MR) to minimize the total weighted tardiness (TWT). Firstly, a sequence-based mixed integer linear programming (MILP) model is established for the FJSP-MR. Afterwards, considering the characteristics of the AM selection sub-problem, an improved genetic algorithm (IGA) with problem-specific encoding and decoding strategies is developed, in which an iterated local search with a modified k-insertion neighborhood structure is introduced for further optimization. Critical paths for the less common TWT objective are fully considered to ensure the local search is performed on bottlenecks of incumbent solutions. The proposed MILP model and IGA are tested on three groups of instances extended from public benchmark sets. Numerical experimental results indicate that the proposed IGA is highly efficient for the FJSP-MR in a variety of scenarios, where the specially designed local search is an effective complementary component for the basic genetic algorithm. Furthermore, a real-world FJSP-MR case is studied to show the proposed IGA is applicable to large-scale engineering problems.

Reconfiguration effort based optimization for design problem of Reconfigurable Manufacturing System

The design problem of reconfigurable manufacturing system (RMS), depends upon part design specifications of product and system capabilities. The RMS consists of reconfigurable machines, tools, fixtures, layouts, etc. These system components are recon-figurable which makes the production system responsive to the change. This regular reconfiguration of the system’s components also requires effort. This effort affects the overall performance of the system. With maximization of the system’s component re-configurability to be more responsive towards the change, the overall effort should also be reduced to get good performance.A new reconfiguration effort index is defined, which considers all the efforts of reconfiguration at the process level, instead of just effort of machine configuration. The newly developed reconfiguration index is used as an objective function for solving the design problem of RMS, for selection of reconfigurable machine tool RMTs to get the best process plan with minimum effort of reconfiguration. A genetic algorithm-based approach is used to solve the problem. A numerical example is presented to illustrate the applicability of the proposed approach. The work is concluded with some future work directions.

Reconfigurability improvement in Industry 4.0: a hybrid genetic algorithm-based heuristic approach for a co-generation of setup and process plans in a reconfigurable environment

Reconfigurable manufacturing systems (RMS) are designed for adjustable production capabilities to cope with the fluctuating market demand. This adjustable capability and customised flexibility are offered by the modular Reconfigurable Machine Tools (RMTs), considered as the key component of an RMS. The main objective of this work is to develop a new approach to jointly consider the setup and process plan constraints. Indeed, based on the relationships between the operations to perform, a integrated setup and process plan is generated, minimising the total cost, including cost of processing, tolerance, setup change and tool module. The proposed new hybrid genetic algorithm-based approach is conducted in two stages. In the first stage, a heuristic is developed for the generation of setups and the assignments of fixtures to each set of operations. While in the second stage, a genetic algorithm is proposed to determine the best process plan to associate with the generated setup plan, under the economic cost consideration. A numerical experiment is performed to show the applicability and the efficiency of the developed approach. A test results highlight the economic gain of the simultaneous consideration of setup and process planning.

Reconfigurable Machine Tool Design for Box-Type Part Families

The reconfigurable manufacturing system (RMS) is a new manufacturing technology and paradigm that resolves the contradictions regarding high efficiency, low cost and flexible production in the mass production of part families. Reconfigurable machine tools (RMTs) are the core components of RMSs. A new approach is proposed for the design of RMTs, which is closely related to the process planning of a given box-type part family. The concepts of the processing unit and the processing segment are presented; they are not only the basic elements of the processing plans of machined parts, but also closely related to the structural design of RMTs. Processing units created by processing features can be combined into various processing segments. All the processing units of one processing segment correspond to the machining operations performed by one RMT. By arranging the processing segments according to the processing sequence, a variety of feasible processing plans for a part can be obtained. Through analysis of the established similarity calculation model for processing plans, the most similar processing plans for the parts in a given part family can be determined and used for the structural design of RMTs. Therefore, the designed RMTs can achieve rapid conversion of processing functions with the least module replacement or adjustment to realize the production of the parts in the part family. Taking the production of a gearbox part family as an example, the validity of the presented method is verified.

Distributed job-shop rescheduling problem considering reconfigurability of machines: a self-adaptive hybrid equilibrium optimiser

The recent trend of globalisation of the economy has been accelerated thanks to emerging new communication technologies. This forces some companies to be adapted to rapidly changing market requirements utilising a multi-factory production network. Job scheduling in such a distributed manufacturing system, is significantly complicated especially in the presence of dynamic events. Furthermore, production systems need to be flexible to timely react to the imposed changes. Hence, reconfigurable machine tools (RMTs) can be used as a resource for flexibility in manufacturing systems. This paper deals with a distributed job-shop rescheduling problem, in which the facilities benefit from reconfigurable machines. Firstly, the problem is mathematically formulated to minimise total weighted lateness in a static state. Then, the dynamic version is extent based on a designed conceptual framework of rescheduling module to update the current schedule. Since the problem is NP-hard, a self-adaptive hybrid equilibrium optimiser algorithm is proposed. The experiments show that the proposed EO algorithm is extremely efficient. Finally, a simulation-optimisation model is developed to evaluate the performance of the manufacturing system facing stochastic arriving jobs. The obtained results show that the production system can be very flexible relying on its distributed facilities and reconfigurable machines.

Methodology for the design of a reconfigurable guillotine shear and bending press machine (RGS&BPM)

Purpose In manufacturing, dedicated machine tools and flexible machine tools are failing to satisfy the ever-changing manufacturing demands of short life cycles and dynamic nature of products. These machines are limited when new product designs are introduced. The solution lies in developing responsive machines that can be adjusted or be changed functionally when these change requirements arise. These machines are reconfigurable machines which are becoming the new focus, as they rapidly respond to product variety and volume changes. A sheet metal working machine known as a reconfigurable guillotine shear and bending press machine (RGS&BPM) has been developed. The purpose of this paper is to present a methodology, function-oriented design approach (FODA), which was developed for the design of the RGS&BPM. Design/methodology/approach The design of the machine is based on the six principles of reconfigurable manufacturing systems (RMSs), namely, modularity, scalability integrability, convertibility, diagnosability and customisability. The methodology seeks to optimise the design process of the RGS&BPM through a design of modules that make up the machine, enable its conversion and reconfiguration. The FODA is focussed on function identification to select the operational function required. Two main functions are recognised for the machine, these being cutting and bending; hence, the design revolves around these two and reconfigurability. Findings The developed design methodology was tested in the design of a prototype for the reconfigurable guillotine shear and bending press machine. The prototype is currently being manufactured and will be subjected to functional tests once completed. This paper is being presented not only to present the methodology by to show and highlight its practical applicability, as the prototype manufacturers have been enthusiastic about this new approach. Research limitations/implications The research was limited to the design methodology for the RGS&BPM, the machine which has been designed to completion using this methodology, with prototype being manufactured. Practical implications This study presents critical steps and considerations in the development of reconfigurable machines. The main thrust being to explore the best possibility of developing the machines with dual functionality that will assist in availing the technology to manufacturer. As the machine has been development, the success of the design can be directly attributed to the FODA methodology, among other contributing factors. It also highlights the significance of the principles of RMS in reconfigurable machine design. Social implications The RGS&BM machine is an answer for the small-to-medium enterprises (SMEs), as the machine replaces two machines with one, and the methodology ensures its affordable design. It contributes immensely to the machine availability by eliminating trial and error approaches. Originality/value This study presents a new approach to the design of reconfigurable dual machines using principles of RMS. As the targeted market is the SME, it is not limited to that as any entrepreneur may use the machine to their advantage. The design methodology presented contributes to the body of knowledge in dual reconfigurable machine tool design.

Algorithm for Designing Reconfigurable Equipment to Enable Industry 4.0 and Circular Economy-Driven Manufacturing Systems

In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise’s business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment’s lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm.

High-Efficient Calculation Method for Sensitive PDGEs of Five-Axis Reconfigurable Machine Tool

Sensitive geometric errors of a machine tool have significant influence on machining accuracy, and it is important to identify them. Complex modeling and analysis must be carried out to identify the sensitive geometric errors of a five-axis machine tool by using the traditional method. Once the configuration structure of the machine tools is reconstructed, repetitive error modeling and analysis are required, and the identification cycle of sensitive geometric errors is long. Therefore, this paper proposes a high-efficient calculation method for sensitive position-dependent geometric error (PDGEs) identification of a five-axis reconfigurable machine tool. According to the results of sensitive geometric errors of the RTTTR-type and TTTRR-type five-axis machine tools, the mapping expressions between sensitive PDGEs and the configuration structure of machine tools was established. In this method, sensitive PDGEs can be calculated directly according to the mapping expression, which eliminates the process of error modeling and analysis. Taking a RTTTR-type five-axis machine tool as an example, the sensitive PDGEs were calculated according to the presented mapping expressions without error modeling and analysis. A series of analysis points in the machining area were selected to compare the machining errors before and after sensitive PDGE compensation. The results show that this calculation method is accurate.

A digital and structure-adaptive geometric error definition and modeling method of reconfigurable machine tool

Reconfigurable machine tool (RMT) is designed for mass customization machining and the structure is reconfigured unpredictably due to the wide range of requirements. After reconstruction, accuracy of the new machine tool must be guaranteed firstly, which puts forward higher requirements of flexibility for error definition and modeling. Therefore, a digital and structure-adaptive geometric error definition and modeling method is presented to quickly respond to the structure changes of RMT. The highlight of this method is that the definition and modeling of geometric errors can be realized automatically. Firstly, a coding method is proposed to express the machine tool component with structure and motion information so that the geometric error definition and modeling can be computerized. Then, common expression of the geometric errors considered kinematic and structural attributes is presented. The identification coefficient matrix of geometric error is defined and calculated by using an assignment algorithm according to the configuration tree. At last, geometric error modeling modules are defined and sequential multiplication calculation is presented to establish the geometric error model automatically. Three typical examples are illustrated to verify the correction of digital method. It is the basis of intelligent error compensation of RMT under the market environment of changing demands.

An optimisation model for the dynamic management of cellular reconfigurable manufacturing systems under auxiliary module availability constraints

In the last decade, traditional industrial and market features were replaced by emerging factors, such as the variable market demand, the need for flexibility, the shorter product life cycles and the mass personalisation, which drastically modified the production environment, pressing industrial companies to embrace and implement new types of production paradigms. Reconfigurable Manufacturing Systems (RMSs) rose as effective systems able to meet the current challenges rapidly changing their hardware, i.e. physical, and software, i.e. logical, structures to address changes in market needs. The manufacturing environment is usually characterised by dynamic cells, i.e. Reconfigurable Machine Cells (RMCs), including intelligent machines called Reconfigurable Machine Tools (RMTs). Such machines consist of fixed parts, i.e. basic modules, and dynamic parts, i.e. auxiliary modules, which allow performing different tasks, i.e. operations. This paper aims at proposing an optimisation linear programming model for the dynamic management of RMSs best balancing the RMTs reconfiguration, considering the auxiliary modules availability, i.e. the efforts to install and disassemble the auxiliary modules on/from the machines, and the part flows among the RMTs. The application to an operative case study widens the model discussion and a multi-scenario analysis concludes the study analysing how the overall system performances change varying the available auxiliary modules. Globally, results show the joint presence of multiple parts on the same RMT in each period allow concluding about the key role of the auxiliary modules to create useful and flexible structures suitable for multiple part processing.