Design,refinement, implementation and prototype testing of a reconfigurable lathe-mill

This article documents the design, refinement, and implementation of a reconfigurable machine tool that provides a flexible platform for turning and milling. Advances in the design and capabilities of machine tools drive modern industry. In fact, the expanding capabilities of the machines permit novel and economic changes in the manufacturing and design of products. After the flexible manufacturing (FMS) boom, a more recent, but no less significant, area of machine development involves the reconfiguration of milling machines. This reconfigurability can also be applied to lathe-mills. The present work demonstrates satisfactorily the reconfiguration characteristics of modularity, integrability, and convertibility. We prototype and test a completely functional bench top reconfigurable lathe mill machine implemented using a $10,000 USD budget and developed following a synthesized machine tool product development methodology that begins with concept design and cutting forces to end in prototyping. In addition to reasonable results in runout, positioning, and surface roughness, the machine demonstrates the capability to produce parts with a Cpk of 1.009, thereby demonstrating the ability to fabricate this type of machine in Mexico for the local jewelry industry.     

Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model

The companies need to rapid response to new product introduction, mix and demand changes to stay competitive. A reconfigurable manufacturing system can quickly react to changes in products and market. The control method to reconfigure the machines of a reconfigurable manufacturing system is crucial for the performance level. This paper proposes a reconfiguration decision-making method based on a Game-Theory algorithm, and in particular the Gale-Shapley model. A periodic review strategy is used to create two sets: one set of machine over-loaded and one set under-loaded. The Gale-Shapley model forms a coupled of over-loaded and under-loaded machines. The reconfiguration concerns the under-loaded machine of the coupled adding also the task performed by the over-loaded machine. This paper presents a simulation environment developed to evaluate the proposed method and highlight the main topics. The simulation results highlight how the game-theory approach developed improves all the performance measures with controlled number of machines’ reconfigurations.     

Design of robust cellular manufacturing system for dynamic part population considering multiple processing routes using genetic algorithm

In this paper, a comprehensive mathematical model is proposed for designing robust machine cells for dynamic part production. The proposed model incorporates machine cell configuration design problem bridged with the machines allocation problem, the dynamic production problem and the part routing problem. Multiple process plans for each part and alternatives process routes for each of those plans are considered. The design of robust cell configurations is based on the selected best part process route from user specified multiple process routes for each part type considering average product demand during the planning horizon. The dynamic part demand can be satisfied from internal production having limited capacity and/or through subcontracting part operation without affecting the machine cell configuration in successive period segments of the planning horizon. A genetic algorithm based heuristic is proposed to solve the model for minimization of the overall cost considering various manufacturing aspects such as production volume, multiple process route, machine capacity, material handling and subcontracting part operation.     

Maintenance scheduling and production control of multiple-machine manufacturing systems

This paper deals with the production and preventive maintenance control problem for a multiple-machine manufacturing system. The objective of such a problem is to find the production and preventive maintenance rates for the machines so as to minimize the total cost of inventory/backlog, repair and preventive maintenance. A two-level hierarchical control model is presented, and the structure of the control policy for both identical and non-identical manufacturing systems is described using parameters, referred to here as input factors. By combining analytical formalism with simulation-based statistical tools such as experimental design and response surface methodology, an approximation of the optimal control policies and values of input factors are determined. The results obtained extend those available in existing literature to cover non-identical machine manufacturing systems. A numerical example and a sensitivity analysis are presented in order to illustrate the robustness of the proposed approach. The extension of the proposed production and preventive maintenance policies to cover large systems (multiple machines, multiple products) is discussed.     

Optimum loading of machines in a flexible manufacturing system using a mixed-integer linear mathematical programming model and genetic algorithm

Machine loading problem in a flexible manufacturing system (FMS) encompasses various types of flexibility aspects pertaining to part selection and operation assignments. The evolution of flexible manufacturing systems offers great potential for increasing flexibility by ensuring both cost-effectiveness and customized manufacturing at the same time. This paper proposes a linear mathematical programming model with both continuous and zero-one variables for job selection and operation allocation problems in an FMS to maximize profitability and utilization of system. The proposed model assigns operations to different machines considering capacity of machines, batch-sizes, processing time of operations, machine costs, tool requirements, and capacity of tool magazine. A genetic algorithm (GA) is then proposed to solve the formulated problem. Performance of the proposed GA is evaluated based on some benchmark problems adopted from the literature. A statistical test is conducted which implies that the proposed algorithm is robust in finding near-optimal solutions. Comparison of the results with those published in the literature indicates supremacy of the solutions obtained by the proposed algorithm for attempted model.     

Drilling reconfigurable machine tool selection and process parameters optimization as a function of product demand

Special purpose machines (SPMs) are customized machine tools that perform specific machining operations in a variety of production contexts, including drilling-related operations. This research investigates the effect of optimal process parameters and SPM configuration on the machine tool selection problem versus product demand changes. A review of previous studies suggests that the application of optimization in the feasibility analysis stage of machine tool selection has received less attention by researchers. In this study, a simulated model using genetic algorithm is proposed to find the optimal process parameters and machine tool configuration. During the decision-making phase of machine tool selection, unit profit is targeted as high as possible and is given by the value of the following variables: SPM configuration selection, machining unit assignment to each operation group, and feed and cutting speed of all operations. The newly developed model generates any random chromosome characterized by feasible values for process parameters. Having shown how the problem is formulated, the research presents a case study which exemplifies the operation of the proposed model. The results show that the optimization results can provide critical information for making logical, accurate, and reliable decisions when selecting SPMs.     

Application of ant colony optimization algorithm in process planning optimization

One objective of process planning optimization is to cut down the total cost for machining process, and the ant colony optimization (ACO) algorithm is used for the optimization in this paper. Firstly, the process planning problem, considering the selection of machining resources, operations sequence optimization and the manufacturing constraints, is mapped to a weighted graph and is converted to a constraint-based traveling salesman problem. The operation sets for each manufacturing features are mapped to city groups, the costs for machining processes (including machine cost and tool cost) are converted to the weights of the cities; the costs for preparing processes (including machine changing, tool changing and set-up changing) are converted to the ‘distance’ between cities. Then, the mathematical model for process planning problem is constructed by considering the machining constraints and goal of optimization. The ACO algorithm has been employed to solve the proposed mathematical model. In order to ensure the feasibility of the process plans, the Constraint Matrix and State Matrix are used in this algorithm to show the state of the operations and the searching range of the candidate operations. Two prismatic parts are used to compare the ACO algorithm with tabu search, simulated annealing and genetic algorithm. The computing results show that the ACO algorithm performs well in process planning optimization than other three algorithms.     

An adapted NSGA-2 algorithm based dynamic process plan generation for a reconfigurable manufacturing system

With burgeoning global markets and increasing customer demand, it is imperative for companies to respond quickly and cost effectively to be present and to take the lead among the competitors. Overall, this requires a changeable structure of the organization to cater to a wide product variety. It can be attained through adoption of the concept of reconfigurable manufacturing system (RMS) that comprises of reconfigurable machines, controllers and software support systems. In this paper, we propose a new approach to generate the dynamic process plan for reconfigurable manufacturing system. Initially, the requirements of the parts/products are assessed which are then compared with the functionality offered by machines comprising manufacturing system. If the production is feasible an optimal process plan is generated, otherwise the system shows an error message showing lack of functionality. Using an adapted NSGA-2 algorithm, a multi-objective scenario is considered with the aim of reducing the manufacturing cost and time. With the help of a numerical example, the efficacy of the proposed approach is demonstrated.     

An optimization model and multiple matching heuristics for quality planning in manufacturing systems

Planning quality control operations for parts assigned to a manufacturing system is an important decision-making process. It involves the selection of quality tools and assignment of the tools and quality operations to machine centers or inspection stations. The problem is complicated by multiple variables involved and the interactions between the variables. Trade-off must be made to minimize the total cost and satisfy demands and resource constraints. Mathematical formulation can be used to integrate the decisions and characterize the problem. More efficient heuristic algorithms are developed based on a tripartite graph representation of the original problem. Experimental results showed that the algorithms proposed are effective and efficient in their computational performance.     

Design and analysis of a reconfigurable discrete pin tooling system for molding of three-dimensional free-form objects

This paper presents the design and analysis of a new reconfigurable tooling for the fabrication of three-dimensional (3D) free-form objects. The proposed reconfigurable tooling system comprises a set of matrices of a closely stacked discrete elements (i.e., pins) arranged to form a cavity in which a free-form object can be molded. By reconfiguring the pins, a single tool can be used in the place of multiple tools to produce different parts with the involvement of much lesser time and cost. The structural behavior of a reconfigurable mold tool under process conditions of thermoplastic molding is studied using a finite element method (FEM) based methodology. Various factors that would affect the tool behavior are identified and their effects are analyzed to optimally design a reconfigurable mold tool for a given set of process conditions. A prototype, open reconfigurable mold tool is developed to present the feasibility of the proposed tooling system. Several case studies and sample parts are also presented in this paper.     

Development of a new variable remote center compliance (VRCC) with modified elastomer shear pad (ESP) for robot assembly

In this paper, a noble variable remote center compliance (VRCC) is developed. The VRCC has a simple structure to change the position of compliance center and to lock the VRCC. The VRCC consists of a top plate, a bottom plate and two modified elastomer shear pads (ESP). The modified ESP has a hole in its stiffness axis, and a stiffness adjusting rod (SAR) is inserted into the hole of the ESP. The stiffness of the modified ESP can be adjusted according to the insertion depth of the SAR so that the compliance center of the VRCC, which depends on the stiffness of the ESP, can be adjusted freely. Furthermore, by passing the SAR through the modified ESP, the VRCC can be locked easily. To verify the effectiveness of the proposed VRCC, a prototype VRCC is made and the variation of the compliance center is measured according to the insertion depth of the SAR. Note to Practitioners-This paper was motivated to reduce the number of machine tools which were used in connecting electric plugs in factory. But, this work is also applied to assembly machines which require inserting a round peg into a round hole. The existing machine tool can connect only one electric peg with fixed length, so various machine tools are needed according to the lengths of plugs. The proposed idea shows the way to solve this problem. By extending working capacity of the machine tool, it is possible that electric plugs with various lengths are connected by one machine tool and, consequently, the changing time and cost of machine tools can be reduced. The developed method removes changing of the machine tool. However, manual adjustment of the proposed machine tool is still needed to connect electric plugs with various lengths. Therefore, in the future research, motorized adjusting structure will be added to the proposed idea to improve manual adjustment of the machine tool.     

A group decision making framework based on fuzzy VIKOR approach for machine tool selection with linguistic information

Computer numerical control (CNC) machines are used for repetitive, difficult and unsafe manufacturing tasks that require a high degree of accuracy. However, when selecting an appropriate CNC machine, multiple criteria need to be considered by multiple decision makers. In this study, a multi-criteria group decision making (MCGDM) technique based on the fuzzy VIKOR method is developed to solve a CNC machine tool selection problem. Linguistic variables represented by triangular fuzzy numbers are used to reflect decision maker preferences for the criteria importance weights and the performance ratings. After the individual preferences are aggregated or after the separation values are computed, they are then defuzzified. In this paper, two algorithms based on a fuzzy linguistic approach are developed. Based on these two algorithms and the VIKOR method, a general MCGDM framework is proposed. A CNC machine tool selection example illustrates the application of the proposed approach. A comparative study of the two algorithms using the above case study information highlighted the need to combine the ranking results, as both algorithms have distinct characteristics.     

A cellular manufacturing system based on new similarity coefficient which considers alternative routes during machine failure

A two-phase procedure for configuring a cellular manufacturing system is proposed. In Phase I, a new similarity coefficient which considers the number of alternative routes when available during machine failure is proposed. The objective of Phase I is to identify part families based on the proposed new similarity coefficient. In Phase II, a new methodology which simultaneously considers scheduling and operational aspects in the cell design during machine failure for a manufacturing environment is proposed. Phase II shows how the scheduling and operational aspects influence the resource utilization during machine failure. The objective of the proposed methodology is to minimize the total sum of inventory holding cost, early/late finish penalty cost for each part in a given period, operating cost and machine investment cost by grouping machines into cells.     

A hybrid fuzzy MCDM approach to machine tool selection

The selection of the appropriate machine tools for a manufacturing company is one of the important points to achieving high competitiveness in the market. Besides, an appropriate choice of machine tools is very important as it helps to realize full production quickly. Today’s market offers many more choices for machine tool alternatives. There are also many factors one should consider as part of the appropriate machine tool selection process, including productivity, flexibility, compatibility, safety, cost, etc. Consequently evaluation procedures involve several objectives and it is often necessary to compromise among possibly conflicting tangible and intangible factors. For these reasons, multiple criteria decision making (MCDM) has been found to be a useful approach to solve this kind of problem. Most of the MCDM models are basically mathematical and ignore qualitative and often subjective considerations. The use of fuzzy set theory allows incorporating qualitative and partially known information into the decision model. This paper describes a fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based methodology for evaluation and selection of vertical CNC machining centers for a manufacturing company in Istanbul, Turkey. The criteria weights are calculated by using the fuzzy AHP (analytical hierarchy process).     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers’ demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity.     

利用决策支持系统分析可重构制造系统中的问题

可重构制造系统是面向新世纪的先进制造模式 .本文提出利用决策支持系统解决可重构制造系统所面临的重构决策问题 ,在系统决策、加工单元布局、以及可重构产品的生产计划问题上采用智能化算法 ,可以得到满意的结果     

An enhanced model for the integrated production and transportation problem in a multiple vehicles environment

Solving an integrated production and transportation problem (IPTP) is a very challenging task in semiconductor manufacturing with turnkey service. A wafer fabricator needs to coordinate with outsourcing factories in the processes including circuit probing testing, integrated circuit assembly, and final testing for buyers. The jobs are clustered by their product types, and they must be processed by groups of outsourcing factories in various stages in the manufacturing process. Furthermore, the job production cost depends on various product types and different outsourcing factories. Since the IPTP involves constraints on job clusters, job-cluster dependent production cost, factory setup cost, process capabilities, and transportation cost with multiple vehicles, it is very difficult to solve when the problem size becomes large. Therefore, heuristic tools may be necessary to solve the problem. In this paper, we first formulate the IPTP as a mixed integer linear programming problem to minimize the total production and transportation cost. An efficient genetic algorithm (GA) is proposed next to tackle the problem when it becomes too complicated. The objectives are to minimize total costs, where the costs include production cost and transportation cost, under the environment with backup capacities and multiple vehicles, and to determine an appropriate production and distribution plan. The results demonstrate that the proposed GA model is an effective and accurate tool.     

A machine tool matching method in cloud manufacturing using Markov Decision Process and cross-entropy

Cloud Manufacturing (CMfg) is a state-of-the-art manufacturing paradigm implementing the concept of service-oriented manufacturing. Machine tools are one kind of the critical manufacturing resources in Cloud Manufacturing, however machine tool matching is still immature owning to customization manufacturing service demands from users and various disturbing factors in production. This paper proposes a machine tool matching method for dealing with a single Cloud Manufacturing task with complex machine tool application demands. In this method, the demands of machine tools and themselves are described and evaluated based on a universal framework to obtain candidate resource groups satisfying local requirements of sub-demands. Then, a series of Markov Decision Processes (MDP) is established, which take the minimal service cost as optimal object to meet global requirements, and a cross-entropy based algorithm is used to solve the optimal object. Finally, simulation experiments are conducted to validate the usability and superiority in efficiency of the proposed method.