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Increasing the cutter size to enhance its stiffness is of great significance for improving the machining efficiency and precision of deep and narrow channel parts. In this paper, the concept of cutter size critical sphere (CSCS) is presented, which is a specified sphere and tangent to several geometric constraints of tool accessible regions. With this concept, the complex multi-constraints of the workpiece can be transformed into several simple but precise geometric conditions to optimize the size and tool orientation of a conical cutter. The conical cutter generated by CSCS can reach a critical state with multi-constraints simultaneously. Firstly, the mathematical principle of the conical cutter generated by a cylindrical cutter is deduced based on the motion envelope theory. Secondly, the concept of CSCS is proposed based on the deduced mathematical principle and the calculation method of the CSCS with the general situation is provided. Thirdly, the CSCS with special situations is discussed and the calculation method is proposed. Fourthly, the mathematical model and solving algorithm for the max-size CSCS is given. The solving algorithm is designed based on the 3D Medial Axis Transformation (3D MAT) theory. Finally, the proposed method is verified by three simulation experiments and a comparison experiment. The results show that the proposed method can efficiently and accurately calculate the max size and tool orientation of the conical cutter simultaneously. The optimized conical cutter can be tangent to the multi-constraints of the workpiece but do no interfer with them. [ABSTRACT FROM AUTHOR]

Slicing is an important step for all layer-based additive manufacturing (AM) processes. This paper proposes an improved and robust slicing algorithm with efficient contour construction to reduce the slicing time and be able to slice a complex STereoLithography (STL) model with millions of triangles (resulting from high-accuracy STL files). Also, another important feature of the proposed method is it can identify outer and inner contours automatically. Test results to demonstrate the improvements in execution time and comparisons with results from published papers have been given to illustrate the algorithm efficiency. The robustness of this slicing algorithm is demonstrated by several complex models with large numbers of nested contours on each slice. [ABSTRACT FROM AUTHOR]

In this paper, a multiproduct multiperiod capacitated production system that has been organized as a flow shop is considered. The main novelty of the paper is proposing a more efficient mathematical model for the problem of integrating lot sizing and scheduling with sequence-dependent setups. In comparison to the former model, because of fewer continuous and binary decision variables and constraints in the proposed model, this model is very easier to solve. Comparison between two models proves the superiority of the proposed model. Two mixed integer programming-based approaches with rolling horizon framework have been used to solve this model. To evaluate the performance of the proposed model and solution method, problems of different scales have been studied. The used algorithms search the solution space for both lot-sizing and scheduling problems and find a combination of production planning and scheduling that is feasible and close to optimum. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates a near admissible heuristic search strategy and its application to a kind of flexible manufacturing system (FMS) scheduling in a Petri net framework. Petri nets can concisely model the strict precedence constraint, multiple kinds of resources, and concurrent activities. To cope with the complexities for scheduling of FMS with alternative routings, this paper proposes an admissible heuristic function based on the execution of P-timed Petri nets and presents an improved dynamic weighting A* strategy using the proposed heuristic function. The search scheme does not need to predict the depth of solution in advance and the quality of the search result is also controllable. Some numerical experiments are carried out to demonstrate usefulness of the algorithm. [ABSTRACT FROM AUTHOR]

In order to generate efficient tool path with given precision requirements, scallop height should be kept under a given limit, while the tool path should be as short as possible to reduce machining time. Traditional methods generate CC curves one by one, which makes the final tool path far from being globally optimal. This paper presents an optimal tool path generation model for a ball-end tool which strives to globally optimize a tool path with various objectives and constraints. Two scalar functions are constructed over the part surface to represent the path intervals and the feedrate (with directions). Using the finite element method (FEM), the tool path length minimization model and the machining time minimization model are solved numerically. The proposed method is also suitable for tool path generation on mesh surfaces. Simulation results show that the generated tool path can be direction parallel or contour parallel with different boundary conditions. Compared to most of the conventional tool path generation methods, the proposed method is able to generate more effective tool paths due to the global optimization strategy. [ABSTRACT FROM AUTHOR]

In a recent paper by Fattahi et al. (Int J Adv Manuf Technol 65:787-802, ), a mathematical model is presented for a two-stage assembly scheduling problem containing a hybrid flow shop and an assembly stage. Some constraints in the model are not valid. This note shows that some new constraints are proposed for the mathematical model. An example is proposed to present and prove this new model by executing mathematical software. [ABSTRACT FROM AUTHOR]

In this study, an integrated vendor-buyer inventory model is proposed. The vendor produces a product in a batch environment and delivers it to a buyer facing normally distributed demand. In the current study, several assumptions are used that the combination of these assumptions has not been studied in previous papers. Here is the list of these assumptions: service-level constraint instead of shortage cost is used; ordering cost reduction is allowed, and decreasing lead time by a cost dependent on two factors including shortened lead time and ordering quantity is considered. An algorithm is developed to obtain the optimal solution of the proposed model. A numerical example is included to illustrate the results of the model. Also, the effects of assumptions used in this paper are evaluated and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a tabu search algorithm that solves the flexible job shop scheduling problem to minimize the makespan time. As a context for solving sequencing and scheduling problems, the flexible job shop model is highly complicated. Alternative operation sequences and sequence-dependent setups are two important factors that frequently appear in various manufacturing environments and in project scheduling. In this paper, we present a model for a flexible job shop scheduling problem while considering those factors simultaneously. The purpose of this paper is to minimize the makespan time and to find the best sequence of operations and the best choice of machine alternatives, simultaneously. The proposed tabu search algorithm is composed of two parts: a procedure that searches for the best sequence of job operations, and a procedure that finds the best choice of machine alternatives. Randomly generated test problems are used to evaluate the performance of the proposed algorithm. Results of the algorithm are compared with the optimal solution using a mathematical model solved by the traditional optimization technique (the branch and bound method). After modeling the scheduling problem, the model is verified and validated. Then the computational results are presented. Computational results indicate that the proposed algorithm can produce optimal solutions in a short computational time for small and medium sized problems. Moreover, it can be applied easily in real factory conditions and for large size problems. The proposed algorithm should thus be useful to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

In this paper, the joint problem of project selection and project scheduling under uncertain environment is formulated, analyzed, and solved by multistage stochastic programs. First of all, a general mathematical formulation which can address several versions of the problem is presented. A multi-period project selection and scheduling problem is introduced and modeled by multistage stochastic programs, which are effective for solving long-term planning problems under uncertainty. A set of scenarios and corresponding probabilities is applied to model the multivariate random data process (costs or revenues, available budget, chance of success). Then, due to computational complexity, a scenario tree of the resulted scenarios is constructed by scenario reduction algorithms. Finally, assuming resources of the projects to be limited and renewable, and present worth of profit of projects as the objective, the objective of the problem is maximized. Eventually, a case study is introduced and solved, and the results are presented. The effectiveness of the proposed algorithm is shown by the numerical results. [ABSTRACT FROM AUTHOR]

This paper aims to investigate a CAD model simplification method with appearance and assembly feature preservation in order to demonstrate and interact with the virtual assembly process of complex product efficiently. Firstly, the invisible features of current-assembled-state assembly model are detected and removed by pre-rendering the assembly model from multiple view directions to reduce data volume and preserve the appearance. Subsequently, the notion of 'conjugation' is incorporated into the definition of assembly features to guide assembly feature recognition. The attributed adjacency graphs (AAGs) of the simplified current-assembled-state assembly model and to-be-assembled components are established. Then, the assembly features on currently selected to-be-assembled component are automatically recognized by searching conjugated subgraphs between the AAG corresponding to currently selected to-be-assembled component and other AAGs based on improved subgraph isomorphism algorithm. After that, simplified CAD model of the to-be-assembled component with its assembly features preserved is constructed by suppressing the common form features, so that it can complete assembly process fluently with the current-assembled-state assembly model. Finally, this method is applied successfully to several cases and the result shows its feasibility and superiority. [ABSTRACT FROM AUTHOR]

Minimum quantity lubrication (MQL) has been proposed as a promising alternative to conventional flood cooling to substantially reduce the lubrication usage while maintaining high surface quality. Residual stress induced by grinding process directly affects the surface quality of the final product. An analytical relationship between residual stresses and process conditions such as process parameters, material properties, and lubrication conditions could support process planning and optimization of MQL grinding. This paper has presented a physics-based model to predict residual stresses in grinding with consideration of the lubrication and cooling effects of MQL. Grinding force and temperature distribution in the workpiece are used to calculate the loading stresses imparted by MQL grinding. The loading stresses are then coupled into a rolling/sliding contact algorithm to solve for residual stresses. Experimental measurements of residual stress profile under and flood cooling conditions have been pursued to calibrate and validate the predicted results. [ABSTRACT FROM AUTHOR]

In a free-state condition, deformable bodies (or compliant parts) have a significantly different shape than their nominal geometry (CAD model) due to gravity loads and residual stress. Typically, the dimensional metrology of such parts requires a particular approach where expensive and specialized jigs are needed to constrain and follow the component during inspection. This paper proposes a new method to fixtureless inspect deformable bodies by adapting the coherent point drift (CPD) algorithm. This new approach combines the optimization of the smoothness regularization parameters of the CPD powerful non-rigid registration method alongside the Thompson-Biweight statistical test as an identification method to distinguish profile and localization process manufacturing defects from a part's deformation. In other words, a new 'flexible' registration approach that meets the specifications of compliant parts is proposed. The CAD model is smoothly modified to fit the scanned part by minimizing two criteria that belong to deformable bodies. The first criterion is the conservation of the curvilinear distance (isometric transformation). That is to say, the condition that the stretch difference between the original CAD model and the modified one should be very small. The second criterion is the usual minimization of the Euclidean distance between the modified CAD model and its corresponding scanned part. Many case studies were performed on a typical industrial sheet metal which presented profile and localization defects. The estimated values of the defects, as well as their positions, are much closer to their reference ones in most cases, thus reflecting the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper reports on research, the aim of which has been to investigate the use of virtual manufacturing and data mining techniques to automate the identification of manufacturing process time estimating relationships that form the basis of product and process cost models. Such models provide information that is critical to all stages of the product development process and to ensuring that development of cost-effective product design and production methods. Use of virtual manufacturing models and data mining techniques enables the majority of the activities involved in the cost model development process to be automated. Hence, reducing the time and effort required, reducing the current high level of reliance on expert judgment and enabling higher levels of cost detail to be estimated. The research reported, therefore, focuses on the use of virtual manufacturing to generate datasets which are then analysed using data mining to identify suitable cost models for manufacturing processes. Part I, of this two-part paper, describes the development of a model development methodology that makes use of virtual manufacturing models and data mining techniques and uses case study data to validate this methodology. Part II will then examine in detail the effectiveness of alternative data mining algorithms in terms of their ability to develop relationships that are (1) representative of the real causal relationships that exist and (2) provide a high level of estimating accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a robust design for a multi-product, multi-echelon, closed-loop logistic network model in an uncertain environment. The model includes a general network structure considering both forward and reverse processes that can be used in various industries, such as electronics, digital equipment, and vehicles. Because logistic network design is a time consuming and costly project as well as a strategic and sensitive decision (i.e. the change of such decision is difficult in the future), a robust optimisation approach is adopted to cope with the uncertainty of demand and the return rate described by a finite set of possible scenarios. Hence, to obtain robust solutions with better time, the scenario relaxation algorithm is employed for the proposed model. Numerical examples and a sensitivity analysis are presented to demonstrate the significance and applicability of the presented model. It is shown that solutions resulted from the suggested approach insure more situations, especially in worst case ones. The results show that although the profit values of the robust configuration are less than the deterministic configuration, the robust configuration is more reliable than the deterministic one because the deterministic configuration is infeasible under some demand and return rates (i.e. in the worst cases). Moreover, the results show the computing time superiority of the algorithm compared to the extensive form model as well as optimality of the resulted solutions. [ABSTRACT FROM AUTHOR]

Aiming at the deficiencies of the vision technology that canot be used in the height tracking of the 3D weld seam during teaching-playback robot gas tungsten argon welding (GTAW) process, this paper designed a set of arc sensor system for height tracking of weld seam, which can effectively acquire arc voltage signals. The characteristic values of arc voltage signals are extracted and a linear relational model between arc voltage and arc length was established by using appropriate denoising algorithm. The experimental results demonstrate that the error between the arc length calculated by linear model and the real arc length was very small, which is accurate enough to meet the requirements of the follow-up height tracking and controlling during the welding robot GTAW process. [ABSTRACT FROM AUTHOR]

In this article, different approaches of the use of black box modelling techniques for piezoelectric actuators are particularly addressed, regardless of the employed technique/algorithm. A modelling approach in this paper refers to two matters: the first, the role of black box techniques in modelling (i.e. if physics-based techniques are also involved in modelling; if so, how and to what extent). From this aspect, the spectrum of approaches ranges from those merged with/inspired by classical phenomenological models to an approach based on purely system identification-based techniques. The second aspect of modelling approaches, in this article, is the input variables to the model. Current and previous values of input voltage, previous values of the output (displacement), derivatives and extremum values of the system's input/output have been used as the inputs to the model so far. Both aforementioned aspects of modelling approaches are addressed appropriately in this article, and various modelling approaches in the literature are categorized and presented in a uniform and comparable manner, so that readers can easily identify research trends in this area and the gaps in the literature. One of the identified unanswered questions in the literature is whether the extremum values of the system's input/output should/should not be used as an input to black box models of piezoelectric actuators. There are works in the literature which have/have not used the aforementioned input variables, but there is no published investigation to evidently answer the proposed question. This article, in the last section, answers this question by reporting and discussing an experimental study. [ABSTRACT FROM AUTHOR]

A mixed-model assembly line is a type of production line where different models of a product are assembled on. Mixed-model assembly lines can respond to unanticipated changes in product demands quickly without keeping so many inventories. Designing mixed-model assembly line involves solving the traditional problems of the assembly line design (consists of balancing problem, determining cycle time, and the number and sequence of stations) in addition of determining the sequence of products in assembly line. The main goal of this paper is presenting a method in order to determine the sequence of products in mixed-model assembly line by considering Just-in-Time systems. Moreover, supplying some required components from feeding lines is considered. A mathematical model is presented which is capable of specifying the sequence of products in the mixed-model assembly line by considering main criteria and keeping feeding lines balanced. Mathematical model can be used for solving small-size problems. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of 'real world' size, the search heuristics of simulated annealing and ant colony algorithms are presented and used to find solutions for several problem sets. Experimentations show that the simulated annealing approach outperforms the ant colony approach in objective function performance. [ABSTRACT FROM AUTHOR]

Presenting a more efficient mathematical model for the problem of integrating lot sizing, loading, and scheduling in capacitated flexible flow shops with sequence-dependent setups is the main novelty of this paper. In comparison to the former model, fewer variables and constraints of the current model makes it easier to be solved. Comparison between this model and the former one shows the superiority of this model. To validate the new model, an efficient lower bound has been provided. Mixed integer programming-based algorithms have been used to solve this model. [ABSTRACT FROM AUTHOR]

In this paper, we consider single-machine scheduling problem with controllable processing times and learning effect, i.e., processing times of jobs are controllable variables with linear costs and also are defined as functions of positions in a schedule. We concentrate on two goals separately, namely minimizing a cost function containing makespan, total completion time, total absolute differences in completion times, and total compression cost and minimizing a cost function containing makespan, total waiting time, total absolute differences in waiting times, and total compression cost. The problem is modeled as an assignment problem and thus can be solved with the well-known algorithms. [ABSTRACT FROM AUTHOR]

Traveling salesman problem with time windows (TSPTW) is a well-known problem in logistics management. It seeks to find a minimum cost tour among customers while all customers are visited at their time windows. To better reflect the real situations of logistics management, this paper concentrates on customers' satisfaction aspect of TSPTW, extends the basic mathematical modeling of the problem to fuzzy traveling salesman problem with time windows (FTSPTW), and presents an ant colony system algorithm to solve the problem. One advantage of this new modeling is its flexibility which enables us to optimize the delivery process in the network of customers with different satisfaction patterns and different priorities. The other advantage is that by the use of fuzzy time windows, not only customers' preference for tolerable interval of time for service but also desirable time for service can be considered. Numerical results for a well-known benchmark problem and a real case study both confirm that optimization of the customers' satisfaction is completely different from traditionally minimizing the total traveling time as well as the proposed algorithm which considers that customers' favorites is completely superior to the algorithm that only pays attention to minimizing total travel time. These results provide an opportunity for future comparisons of different solution methods for the FTSPTW. [ABSTRACT FROM AUTHOR]

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing. [ABSTRACT FROM AUTHOR]

This paper describes the comparison of the burr size predictive models based on artificial neural networks (ANN) and response surface methodology (RSM). The models were developed based on three-level full factorial design of experiments conducted on AISI 316L stainless steel work material with cutting speed, feed, and point angle as the process parameters. The ANN predictive models of burr height and burr thickness were developed using a multilayer feed forward neural network, trained using an error back propagation learning algorithm (EBPA), which is based on the generalized delta rule. The performance of the developed ANN models were compared with the second-order RSM mathematical models of burr height and thickness. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. The details of experimentation, model development, testing, and performance comparison are presented in the paper. [ABSTRACT FROM AUTHOR]

This paper presents an approach based on the constraints network to support collaborative design. Firstly, from the point of view of collaborative design, the model of the constraints network for coordination and optimization is studied. Secondly, a general consistency algorithm is designed to predict conflict and refine the intervals for parameter coordination, which verify the design stage early in the process and assist the designers in determining design variables to reduce the multidisciplinary iterations in collaborative design. Finally, a constraints-network-based parameter optimization model is established, and the multiobjective optimization approach is presented. The method is demonstrated by a bogie parameter design problem. [ABSTRACT FROM AUTHOR]

Flattening 3D surfaces is a challenging problem in the product design community. This paper presents an efficient flattening algorithm using a mass-spring model. In the mass spring model, crossed-springs are introduced to reduce the distortion of the resulting surface. The process of planar development is made faster by using triangle strips. To solve the problem of overlapping, the paper proposes a constrained flattening iteration method and a local correction method. Experimental results show that the algorithm can deal with complex surfaces efficiently. [ABSTRACT FROM AUTHOR]

Teamwork is one of the important features of concurrent engineering (CE). Some qualitative and conceptual models of cross-functional teams in CE and their organisation patterns have been proposed in the literature. A quantitative approach to solving the team organisation problem is needed in practice, but little work has been done on this. In this paper, a mathematical model based on integer programming is proposed to formulate the team organisation problem. In the mathematical model, the following requirements for team organisation are considered: 1. The similarities between the current task and the completed tasks. 2. The technical abilities, creation abilities, management abilities, work achievements, and cooperation abilities of the developers. 3. The numbers, workloads, and utilisation rates of the developers. The team organisation problem is decomposed into a series of independent subproblems. A tabu search algorithm is proposed to solve the subproblems. And an example derived from a research institute of the Ministry of Information Industry of China is presented to illustrate the team organisation approach proposed in this paper. The computed result shows that the approach is feasible and the algorithm effective. By solving the subproblems one by one, we can obtain the solution to the whole team organisation problem. [ABSTRACT FROM AUTHOR]

As a simple meaningful external illustration, a group of boundary contours is commonly used for representing the physical appearance of an object of interest on a 2D plane (e.g., a part drawing, CT scan image, a sliced layer, tile pattern). The illustration is understood from the composition of the contours and their attributes that include their shapes, sizes, locations and relationships. Therefore, not only is geometrical information important for contours to be useful in subsequent operations but their topological hierarchy relationships also are essential. Presented in this paper is a topological hierarchy-contour tracing algorithm that is capable of tracing nests of interconnected contours often found in practice and of identifying their topological hierarchy relationships automatically during contour tracing. The algorithm organizes contours by family rather than by group or as individual. However, contours are traced across families generation by generation instead of going deep with one family branch at a time. The outputs of the algorithm are ordered sequences of boundary pixels representing all closed contours and their topological hierarchy relationships. The algorithm has been implemented on LabVIEW, and tested with several samples to illustrate its applications. [ABSTRACT FROM AUTHOR]

An accurate approach of the instantaneous undeformed chip thickness is a key issue for analyzing milling process. The presented models for approximating instantaneous undeformed chip thickness are oversimplified and complex for use in academic and manufacture practice. In this paper, a new model for determining the undeformed chip thickness in milling process with higher accuracy is proposed. According to the true trochoid path of tool tooth, the true instantaneous undeformed chip thickness can be derived by solving the deduced transcendental equation. An iterational algorithm is used to approach the value in the transcendental equation with infinitesimal calculation error. Our main contributions include that the accurate positions of the geometric points of the undeformed chip profile with different level of width of cut are analyzed and derived, as well as the accurate undeformed chip thicknesses of the chip cross-section profile are approached by iterative algorithm. Case studies with different parameter combinations show that the new model has a higher accuracy compared with the presented models. With this innovative approach, a method of accurate prediction of the geometric profile and thickness of the undeformed chip profile in milling process is provided. [ABSTRACT FROM AUTHOR]

Multiple-route job shop scheduling problem (MRJSP) is a generalization of job shop scheduling problem in which each job may have more than one route for its production and the numbers of operations associated to the alternative routes of the job are not necessarily equal. MRJSP is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem, necessitating the use of heuristic approaches or dispatching rules for its solution. In this paper, mathematical formulation of MRJSP is first presented. Then, a two-phase algorithm is proposed to deal with the two major sub-problems in MRJSP, namely, the route selection and the sequencing sub-problems. In order to evaluate the effectiveness of the proposed algorithm, several problems (in small, medium, and large sizes) are designed and solved using the proposed algorithm. The problems are also solved using some other dispatching rules, and comparisons are provided. The computational results show that the proposed algorithm generates high-quality schedules in a timely fashion. [ABSTRACT FROM AUTHOR]

This paper analyses different objective functions for the parameter identification of parallel mechanisms and studies the influence in the position and orientation errors to improve their accuracy. A new objective function considering deviation terms is presented. This function is compared with other widely used functions and the advantages and disadvantages of each function are presented. The geometric parameter identification is performed by external calibration by means of the direct kinematic model. First, the objective functions are defined considering error position, error orientation and deviations in measurement. These functions compare the measured and calculated moving platform coordinates in order to obtain the identified model parameters that minimize this difference. The measured coordinates are obtained by measuring three sphere centres, fixed to the moving platform of a parallel mechanism, and the computed coordinates are given by the kinematic model. Second, the model is solved by the Levenberg-Marquardt algorithm for a number of identification positions. Finally, the calibration is verified in test positions. The results obtained show that the consideration of the deviations in measurement in the objective function with respect to classical approaches allows us to better identify those kinematic parameters corresponding with passive joints that cannot be measured. These findings confirm that a suitable objective function can improve the mechanism accuracy by more than one order of magnitude in both position and orientation errors. [ABSTRACT FROM AUTHOR]

To guarantee the successful transformation from product functional requirement to geometry constraints and finally to dimension constraints between components in a product, an evolutionary tolerance design strategy is proposed on the basis of automation technology in product structure design. In the first part of this paper, the theory of the growth design and the process of tolerance evolutionary design are introduced. Following the evolution of product structure, product tolerance grows from its initial state, defined by accuracy requirements, to its final state, defined as dimension tolerance and geometric tolerance. In this growing process, the basic units in the product growth design, known as functional surfaces and their nominal features, are used as evolutionary carriers. With the help of these basic units, the method for the construction of a two-layer correlation network is proposed. In the second part, the tolerance assertions to assist tolerance evolutionary design are given, based on which the basic process for an evolutionary design of dimensions chain and geometric tolerance are presented. In order to optimize the allocation of the dimension tolerance, a mathematical model is developed in which a correlated sensitivity function between the cost and the tolerance is created. In the model, the design cost, the manufacturing cost, the usage cost, and the depreciation cost of the product are used as constraints to the tolerance allocation. Considering these costs, a multifactor cost function to express quality loss of the product is developed and is applied into the model. The minimum cost is used as the objective function, and the depreciation cost in the objective function is expressed by the discount rate-terminology in economics. The aim was to achieve a final and ideal balance around assembly, manufacturing, and usage through the control of product precision. In the last part, the successful usage of the proposed tolerance evolutionary strategy in the incremental growth product design is demonstrated through a design example. [ABSTRACT FROM AUTHOR]

In this paper, research on developing adaptive rapid prototyping/manufacturing (RP/M) algorithms for functionally graded material (FGM)-based biomedical models is reported. Non-uniform rational B-spline and FGM-based features have been proposed to present the models. Innovative algorithms for tool path optimization in RP/M have been developed, including: (1) a mixed tool path algorithm to generate contour/offset tool paths to represent varying material composition along the boundary of each sliced layer of a model and zigzag tool paths to present the internal area of a single material to simplify computing and processing and (2) adaptive algorithms to control the RP/M nozzle/print head to minimize the build time of each sliced layer according to the geometrical characteristics of the model. Case studies of FGM-based biomedical models have been used to verify and demonstrate the performance of the research in terms of algorithm effectiveness. [ABSTRACT FROM AUTHOR]

This paper addresses the multiple-route job shop scheduling problem to minimize makespan. The problem is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem. The goal is, thus, to obtain near-optimal schedules in a computationally efficient manner. Mathematical formulation of the problem is first presented. Then, an approach based on artificial immune algorithm is proposed. In order to evaluate the effectiveness of the proposed approach, 30 problems in small, medium, and large size are designed and solved using the proposed approach. Problems are also solved using Lingo software and the results are compared. The computational results show that the proposed approach generates high-quality schedules in a timely fashion. [ABSTRACT FROM AUTHOR]

In this paper, we focus on real-life settings that require the development of new models of flowshop scheduling problems, where job processing times can increase with the number of processed jobs due to the aging effect and decrease by the allocation of additional resource. We analyse the makespan minimization flowshop problem with such model and also with the aging effect only. We prove that the considered problems and their special cases are still polynomially solvable under given conditions, and on their basis, we provide optimal polynomial time solution algorithms. [ABSTRACT FROM AUTHOR]

Part selection and machine loading are two major problems in the production planning phase of the flexible manufacturing systems. The problems have a combinatorial structure and usually, in practice, it is difficult to deal with this kind of problems using a mathematical programming model. In this paper, the above problems are formulated as a mixed-integer programming model which is handled sequentially and solved by a diversification-strategy-added version of the hybrid tabu search/simulated annealing algorithm of Zhang et al. ( Comput Oper Res 35:282-294, 2008) presented in 2008. The performance of the algorithm is tested on eight random-generated problems with different sizes. The results are compared with those of the mathematical programming model, the original version of Zhang et al.'s ( Comput Oper Res 35:282-294, 2008) algorithm and also a tabu search algorithm developed earlier by the authors. [ABSTRACT FROM AUTHOR]

In this paper, we analyze some single machine scheduling problems with the aging effect. We extend the sum-of-processing-time-based aging model such that the fatigue caused by each job to the machine is equal to a non-increasing function dependent on the normal processing time of a job and the aging effect is job dependent. Although the proposed model is more general and describes more precisely the real-life settings, we show that the special cases of the maximum completion time and the maximum lateness minimization problems with this model are still polynomially solvable. However, we prove the maximum completion time minimization problem with the sum-of-processing-time-based aging model is strongly NP-hard if some jobs have deadlines and constant processing times. On this basis, we show that the maximum lateness minimization problem with this aging model is also strongly NP-hard. [ABSTRACT FROM AUTHOR]

Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Multi-period project portfolio selection problems can be modeled by multistage stochastic programs. These models utilize a set of scenarios and corresponding probabilities to model the multivariate random data process (costs or revenues, available budget, chance of success). For most practical problems, the optimization problem that contains all possible scenarios is too large. Due to computational complexity, this program is often approximated by a model involving a (much) smaller number of scenarios. The scenario reduction algorithms determine a subset of the initial scenario set and assign new probabilities to the preserved scenarios such that optimal values and approximate solution sets remain close to each other. This paper presents a stochastic optimization model of pharmaceutical multi-period project selection problem using a real-option valuation method. Subsequently, it reduces scenarios via existing algorithms to generate a new (smaller) scenario tree. Model with reduced scenarios has been solved in this research. Numerical results indicate improved portfolios and show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new mathematical model is proposed for inter- and intra-cell layout problem in cellular manufacturing system. A binary particle swarm optimization algorithm with a new heuristic approach for satisfying the constraints of model is implemented to solve the proposed model. Also, a variable neighborhood search is applied for finding better local optimal solutions. Some sensitivity analyses are carried out to find the best values of PSO parameters. To prove the efficiency of the proposed algorithm, various numerical examples in small, medium, and large sizes in both perfect geometric shapes and random placements are generated and solved. [ABSTRACT FROM AUTHOR]

Many factors affect machining quality such as feedrate fluctuations, chord errors, machining time, machine characteristics, and unreasonable planning. Compared with traditional interpolations, parametric interpolations perform better at reducing feedrate fluctuations, chord errors, and it can save interpolation time. Reasonable feedrate planning and dynamic motion control are also necessary for precision improving. In this paper, NURBS interpolation is employed and feedrate is planned on basis of curve geometry and machine dynamic characters. Furthermore, a synthesized control algorithm is applied in control system to refine following accuracy. Simulation and experimental results demonstrate that the proposed approach can significantly reduce the machining error for biaxial following task. [ABSTRACT FROM AUTHOR]

Numerous analytical methods ranging from simple weighted scoring to complex mathematical programming approaches have been proposed to solve supplier selection and order allocation problems. However, the traditional methods too often fail to consider: (1) situations in which goods are transported from a supplier to a receiver using different transportation alternatives (TAs) and (2) a finite planning horizon consisting of multiple discrete time periods. We present a structured framework with two separate but dependent phases. In the selection phase, we use a data envelopment analysis model to determine the relative efficiency of the suppliers and the TAs. In the allocation phase, we use a multi-objective mixed integer programming model with two objectives for minimizing the total costs and maximizing the overall efficiencies. The contribution of this paper is threefold: (1) It provides a comprehensive and systematic framework that embraces both quantitative and qualitative criteria; (2) it addresses the need in the supplier evaluation literature for methods that considers different TAs in the supplier selection and order allocation decisions encompassing multiple discrete time periods; and (3) it uses a real-world case study to demonstrate the applicability of the proposed framework and exhibit the efficacy of the procedures and algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new formalized reasoning method for assembly sequences generation which reduces the solution space dramatically. Firstly, a polychromatic sets (PS) matrix is introduced to describe the relations between the part-match and freedom; Secondly, the polychromatic sets matrix of entirety and element are used to describe the relations between the parts and assembly constraints; assembly constraint models and connector assembly models are constructed in this phase; then, the solution method for constraint equations is given; and the reasoning algorithm of feasible sequence is presented. Finally, an application case is studied to demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In shoe-last high-speed numerical machining, the key step is to generate CNC machining tool path by data sampled from the shoe last rapidly and accurately. By analyzing the characteristics of helical line and the model of the shoe last expressed in cylindrical-polar coordination system, an algorithm for generating spiral tool path of shoe last with varying pitch was presented. By sample data interpolation, mathematic models with different pitch of helical line were built, and the tool paths with different machining parameters were determined. The effectiveness is verified by the emulation and experimental results represented in the paper. [ABSTRACT FROM AUTHOR]

Many surfaces used in practice consist of combinations of various features that are sufficiently smooth or highly detailed. In the presence of highly detailed features, it is obvious that non-feature-sensitive sampling is unable to preserve small features after significant data reduction. The higher the sensibility of point sampling to small features, the more intensive sample points in detailed features we have. When feature sensitivity is exaggerated, however, it makes the distribution of sample points locally greedy and unbalanced, which is undesirable for many point-based applications. Therefore, it is important for feature-sensitive sampling to blend feature sensitivity with balanced distribution of sample points to generate high-quality 3D models through point sampling. This paper presents a novel 3D point-sampling algorithm that produces balanced feature-sensitive sample points from unorganized point clouds. The key ideas of our point-sampling algorithm are that we set scalable feature sensitivity based on local feature size and balance the distribution of sample points by normalizing the number of sample points over localized regions. Then, sample points are placed at feature-sensitively weighted positions by a fuzzy clustering technique. The proposed sampling method enables us to produce a variety of well-balanced sample points from sparse to dense and from regular to feature-sensitive. We demonstrate the versatility of our sampling method by providing experimental results and comparisons with other point sampling methods. [ABSTRACT FROM AUTHOR]

Simulated annealing (SA) is a general purpose optimization technique capable of finding optimal or near optimal solutions in various applications. The major disadvantage of this technique is its slow convergence making it not suitable for solving many complex optimization problems. This limitation may be alleviated by parallel computing using a multiprocessor computer or a cluster of workstations. In this paper, we present an integer programming model for solving a multi-period cell formation problem in cellular manufacturing system. In order to solve the mathematical model efficiently, we developed a multiple Markov chain simulated annealing algorithm which allows multiple search directions to be traced simultaneously. Our computational results on a single processor machine showed that multiple Markov chain SA is much more efficient than a conventional single Markov chain SA. The parallel implementation of the multiple Markov chain SA further improves its computational efficiency in terms of solution quality and execution time. [ABSTRACT FROM AUTHOR]

This paper gives a brief introduction to establishing a knowledge-based system for intelligent stamping process planning. Studies on the system’s framework, knowledge model and reasoning mode are introduced. Some key technologies such as the feasibility of the stamping process, an optimal algorithm for blank layout, intelligent strip layout and force calculation are researched. The efficiency of stamping process planning can be improved by the system. [ABSTRACT FROM AUTHOR]

In recent years, many developments in logistics were connected to the need for information in an efficient supply chain flow. The supply chain is often represented as a network called a supply chain network (SCN) that is comprised of nodes that represent facilities (suppliers, plants, distribution centers and customers). Arcs connect these nodes along with the production flow. A multistage SCN (MSCN) is a sequence of multiple SCN stages. The flow can only be transferred between two consecutive stages. The MSCN problem involves the choice of facilities (plants and distribution centers) to be opened and the distribution network design must satisfy the demand with minimum cost. In this paper, a revised mathematical model is first proposed to correct the fatal error appearing in the existing models. An efficient hybrid heuristic algorithm (HHA) was developed by combining a greedy method (GM), the linear programming technique (LP) and three local search methods (LSMs) (always used in solving the scheduling problem). The pair-wise exchange procedure (XP), the insert procedure (IP) and the remove procedure (RP) to solve the MSCN problem. Preliminary computational experiments demonstrate the efficiency and performance of the proposed HHA. [ABSTRACT FROM AUTHOR]

Adaptable design has the potential to improve product competitiveness through meeting customers' needs in the product life cycle. Design of open-architecture products has been proposed using the adaptable design method with open and adaptable as well as standard interfaces. Effective tools are required for engineers to apply the concept and approaches of the adaptable design in their daily design processes. This research proposes an optimization method of the adaptable design for open-architecture products. Mathematical models and algorithms considering both adaptability and openness have been developed to identify an optimal design of the adaptable product configuration considering the existing modules and products. An integrated system framework and optimization methods have been implemented as a cyber-enabled tool for the adaptable design of open-architecture products to enable users and vendors to participate in the product development processes using open, adaptable, and standard interfaces. Design of an industrial product was carried out to verify the proposed methods and tools. The system usability, effectiveness, and efficiency were also evaluated. [ABSTRACT FROM AUTHOR]

A method for boundary extraction of cutting area is presented. With this method, the compound surfaces are approximated by the Z-map model to address the issues regarding cutting area generation. The run-length code is constructed to represent the object area in each cutting layer. A boundary tracing algorithm extracts boundaries by sequentially linking nodes according to the connectivity information of runs. The formed boundaries are sorted and arranged in a boundary representation tree representing the topological structure of the cutting area in the cutting layer. The presented method, simple and effective, has the capability of automatic recognition of contours and islands. The experimental NC machining results demonstrate the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

The main purpose of the vehicle routing problem (VRP) is to deliver a set of customers with known demands on minimum-travel routes and terminating at the same depot. The vehicle routing problem with time windows (VRPTW) requires the delivery made in a specific time window for every customer and returning to the depot before a due time. Contrary to current research, an estimation of distribution-algorithm-based approach coupled with a simulation model is proposed and developed to solve the problem and implement the solution. The approach mentioned makes use of a Bayesian network as a probability model to describe the distribution of the solution space. Furthermore, the approach taken in this study combines the key advantages of both estimation of distribution algorithms (EDA) and simulation. The simulation is used to model the VRPTW environment, while the EDA is used to guide the overall search process to identify the best performing ones. Solomon's (Oper Res 35:254-265, ) instances served as input and test parameters in order to show that there exists a relationship and interaction between vertices and positions on the sequence of the VRPTW solution. A better position for each vertex on the sequence can be estimated through a Bayesian network. Experimental results show that the EDA performance was better in 70 % of the cases, as average, for the number of vehicles used in all the trails with respect the other algorithms proposed as a benchmark for comparison with the EDA scheme. [ABSTRACT FROM AUTHOR]

Disassembly systems obtain valuable parts from end-of-life products to remanufacture, reuse, or recycle them. This study deals with disassembly scheduling and presents a novel mixed integer programming model. Disassembly scheduling is the problem of determining quantity and schedule of items disassembled, held in inventory, sold, and disposed on capacitated resources over a planning horizon while satisfying at least a given service level. The model presented here includes a number of novelties including assignment of disassembly operations to parallel capacitated resources, consideration of costs to environment and demand for each level of items. We also present a problem testbed to be used for comparison of different algorithms and the generation mechanism. Results from a large experimental design using problem testbed are presented. [ABSTRACT FROM AUTHOR]

In gas tungsten arc welding (GTAW), the weld pool is the major source of information that can be used to assure the production of the desired weld penetration-the most critical factor determining the weld integrity. To meet this challenge, various sensing technologies, modeling methods, and control strategies have been studied, and artificial intelligence technologies were applied to improve system intelligence. The GTAW process analysis is given first. Then, a short introduction on weld pool sensing technologies is presented, where three-dimensional (3D) vision sensing is a very active orientation. Furthermore, weld pool description model and characteristic parameter model are also discussed, where intelligent algorithms were used generally. Besides, dynamic modeling and penetration control strategies, especially intelligent control strategies are presented. At last, the discussion about development of the GTAW penetration control product is analyzed briefly. [ABSTRACT FROM AUTHOR]