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Reducing machining time in a milling process is one of the important criteria to improve the overall efficiency of the machining process. This paper presents a study on the reduction of machining time, focusing on contour parallel machining to increase the efficiency and performance during the machining process. One method to enhance the performance of contour parallel machining is by defining a tool path interval that is larger than the radius of the cutting tool in a roughing operation because of its capability of reducing the tool path length and machining time. However, this causes the occurrence of an uncut region at the corner and at the centre of a contour parallel. This uncut region can be removed through an additional tool path known as the clear tool path. Therefore, in this paper, a new method based on an optimisation technique is introduced to generate a clear tool path that removes the entire uncut region in contour parallel machining at minimum cutting time. Ant colony algorithm (ACO) is used to optimise the clear tool path length in contour parallel machining time by minimising the movement of cutting tool in removing the entire uncut regions. A new transition rule has been established from the conventional ACO, which adapted the uncut region occurring at the corner of the contour parallel. Then, to validate the optimisation result, a cutting experiment was carried out using computer numerical control (CNC) milling machine. It can be ascertained from this study that the optimisation of the clear tool path gives optimal tool path length whilst reducing the cutting time in the roughing process. [ABSTRACT FROM AUTHOR]

Localization plays an important role in manufacturing the curved thin-walled parts, which can determine the distribution of machining allowance and has great influence on the manufacturing accuracy. The registration algorithm is the most efficient way to locate the billet in the machining coordinate system of the machine tool by computing a transformation matrix. However, the localization of the curved thin-walled parts is complicated and challenging since the billets are individual, the shape and location of which are unknown. This paper attempts to develop an adaptive localization approach with the constraints of the profile and thickness based on on-machine measured data. The framework for constrained adaptive localization approach is illustrated, in which on-machine measurement, registration, isometric mapping and allowance optimization are involved. The details of the on-machine measurement for both the profile inspection and the thickness measurement are presented. An isometric mapping method is employed to build the separate point pairs between the measured points and the nominal shape of the part. A constrained optimization algorithm for the machining allowance is performed iteratively until meeting the constraints of the profile and thickness tolerance ranges. Finally, a case study of constrained adaptive localization was carried out, the results of which confirm the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, a tri-objective optimization problem related to sequencing in multi-model reconfigurable assembly systems (RAS) is addressed. RASs are also designed around product families similar to the more generic reconfigurable manufacturing systems (RMSs). A multi-model assembly line needs reconfiguration after assembling each batch of the products from the family. Further, the changeover of assembly line from one product type to another is also of varying degree of complexity and reconfiguration cost. Two novel indices for commonality and reconfiguration complexity are proposed here to incorporate this feature of RAS. Optimizing the reconfiguration time/cost is taken as the first objective while fulfilling the product due dates and the work load balance among the assembly stations as the second and third objective functions, respectively. This optimization problem is solved using a Multi-objective Self-Organizing Migrating Algorithm (MOSOMA) metaheuristic to find the non-dominated Pareto optimal solutions representing optimum sequencing of products in the family. An approach for identifying the best solution from the multiple Pareto optimal solutions using aggregate fitness is also proposed. A numerical illustration along with computational procedure is presented on a hypothetical RAS model. [ABSTRACT FROM AUTHOR]

The optimum design of a cam mechanism is a very interesting problem in the contact mechanics today, due to the alternative industrial applications as a mechanism of precision. In this paper, a new evolutionary algorithm called modified adaptive differential evolution (MADE) is introduced for multi-objective optimization of a cam mechanism with offset translating roller follower. The optimization procedure is investigated for three objectives among them minimum congestion, maximum efficiency, and maximum strength resistance of the cam. To enhance the design quality of the mechanism in the optimization process, more geometric parameters and more design constraints are included in the problem formulation. In order to validate the developed algorithm, three engineering design problems are solved. The simulation results for the tested problems indicate the effectiveness and the robustness of the proposed algorithm compared to the various existed optimization methods. Finally, the optimal results obtained for the case study example provide very useful decisions for a cam mechanism synthesis. [ABSTRACT FROM AUTHOR]

Building venting system for releasing exhausted air is a very important step in injection mold design. A high-quality venting system can effectively release gas in mold, which will eliminate the defects caused by residual gas and improve the quality of product. However, the surfaces used to create venting system are usually complex, and traditional manual design of venting system is time consuming and difficult to guarantee the design quality on complex surface. To improve the design efficiency and quality of venting system, this paper introduces an automatic approach for designing venting system on complex surfaces of mold. It mainly consists of three steps: (i) original centerline generation, (ii) vent centerline optimization, and (iii) vent channel generation. In step (i), methods of offsetting curves and creating vertical lines on complex surfaces are proposed to generate original vent centerlines. In step (ii), algorithms of connecting curves and curve fairing on complex surfaces are presented to optimize vent centerlines. In step (iii), a hybrid way of extruding and thickening is developed to generate vent features. Based on these three steps, a smooth and continuous venting system can be created on complex surfaces in an automated manner. Finally, a case study with complex surfaces is provided to validate that the design efficiency and quality of venting system can be dramatically improved by using this approach. [ABSTRACT FROM AUTHOR]

A typical interpolation strategy for line segments consists of a transition scheme, a Look-ahead ACC/DEC scheduling, and an interpolation algorithm. In these three parts, the main computation occurs in the first and second part. Some research work has been carried out to decrease the computation in the previous literatures, but these methods occupy a lot of computing resources for the optimization process during the calculation of transition curve parameters and feed rates. Consequently, the computational efficiency of interpolation strategy is greatly reduced. To deal with the issue, a real-time interpolation strategy is proposed in this paper. In the transition scheme, a Bézier curve is utilized to smooth the line segments. Based on the relationship among the approximation error, the approximation radius, and the transition curve, the curve can be directly generated when the approximation error is given. In the ACC/DEC scheduling, a 3-segment feed rate profile with jerk continuity is constructed. Meanwhile, a Look-ahead planning based on Backward Scanning and Forward Revision (BSFR) algorithm is utilized to eliminate redundant computation. Compared with Zhao’s and Shi’s strategy, the proposed strategy has the merits of C2 and G2 continuity for the tool path, jerk continuity for the tool movement, and distinguished real-time performance for interpolation. The experiments of 3D pentagram and 2D butterfly are carried out with different strategies and their results demonstrate that the interpolation efficiency can be greatly improved with the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper presents a research that investigates solutions and algorithms for determining the optimum machine layout served by autonomous material handling system, like mobile robot or automated guided vehicle. Unlike previous works which solved the layout problem by optimizing the distance between facilities, in this paper the machine layout is addressed based on optimizing the travel time of the material handling system. The proposed approach can include boundary kinematic constraints of vehicle while optimizing the objective function such as velocity, acceleration, orientation, and trajectory curvature. The nonlinear constrained model is transformed to unconstrained problem using penalty method. Then, a simulated annealing-based algorithm is used to search for the optimum locations of machines among all possible feasible layouts. The simulation results showed that the proposed approach was efficient enough to use in real factories due to including a vehicle path planner integrated in an overall layout design scheme that involves searching of vehicle control parameters. [ABSTRACT FROM AUTHOR]

Material extrusion-based additive manufacturing (AM) is an effective tool in producing prototypes and final parts without geometrical complexity limitations. Despite having widespread applications and enormous advantages over conventional manufacturing techniques, the proliferation of extrusion-based AM has been limited by the low deposition quality and the poor surface finish of printed parts. To address these issues, an optimized path planning technique is proposed in this paper. The sharp corners and the non-uniform spacing between adjacent path elements in the final planned path are two major causes of unevenness of the deposited surface. The proposed method tries to decrease the number of sharp corners by using an implicit algorithm derived from the level sets of the input contours. The curvature information is used to smooth the generated contour paths. Subsequently, to achieve uniform spacing, local optimization is applied on the smoothed path by adaptively adjusting the locations of points on the path. These optimizations lead to a smoother part surface, when compared to those of typical fill path techniques. The proposed method is validated using several examples of parts, many of which are then constructed using a 3D printer. [ABSTRACT FROM AUTHOR]

Scheduling of jobs and resources on a shop floor is an ever green optimization problem. Job shop scheduling problem (JSSP) is an allocation of 'n' jobs on 'm' machines so as to complete processing of all jobs in a minimum possible time. The JSSP has been addressed by various direct, indirect methods, programs, and algorithms in the last 40 year of literature. This paper presents a new paradigm of invasive weed optimization which mimics the process of weed colonization and distribution to solve JSSPs. The algorithm had shown encouraging and promising outputs on standard benchmarking problems. [ABSTRACT FROM AUTHOR]

A review of the recent development of the machining fixture configuration/layout is presented in this paper. The literature review is mainly focused on the recently developed optimal fixture configuration under the dynamic conditions of the workpiece. In this review paper, the fixture design, fixture analysis, fixture synthesis, fixture layout design, optimization of fixture layout design, various optimization algorithms, and case studies of two- and three-dimensional workpiece geometries under dynamic conditions have been emphasized specially. The further scopes of the research are finally summarized. [ABSTRACT FROM AUTHOR]

With the development of casting process and its numerical simulation technique, many demands of realism and clearness in post-processing have arisen. This paper introduces the implicit surface to post-processing for the results of casting simulation so as to meet these demands. In this paper, a robust and fully automatic algorithm is proposed for extracting the implicit surface in the signed distance function calculated from the import stereolithography files. Then several applications of implicit surface in the post-processing of casting simulation are listed in two categories. The applications in the first category include fast rendering of massive data, section view in arbitrary direction, visualization of residual liquid, and casting deformation, giving clear and pretty visualization of the results in simulation, and making it possible for engineers and process designers to look through the opaque mold, gain insights to casting process, and locate possible defects. The applications in the second category involve thickness distribution and modulus calculation, which offer important characteristics of castings and facilitate the optimization of casting processes. [ABSTRACT FROM AUTHOR]

This paper proposes a heuristic method based on ant colony optimization to determine the suboptimal allocation of dynamic multi-attribute dispatching rules to maximize job shop system performance (four measures were analyzed: mean flow time, max flow time, mean tardiness, and max tardiness). In order to assure high adequacy of the job shop system representation, modeling is carried out using discrete-event simulation. The proposed methodology constitutes a framework of integration of simulation and heuristic optimization. Simulation is used for evaluation of the local fitness function for ants. A case study is used in this paper to illustrate how performance of a job shop production system could be affected by dynamic multi-attribute dispatching rule assignment. [ABSTRACT FROM AUTHOR]

The influence of the cutting edge micro geometry on cutting process and on tool performance is subject of several research projects. Recently published papers focus on optimising the cutting edge rounding. The results are partly inconsistent. Unfortunately, no international standard yet exists to properly describe the cutting edge micro geometry. This is seen as the root cause for detected discrepancies. To develop a common understanding for the influence of rounded cutting edges, it is indispensable to use the same basis to characterise the edge profile. This paper gives a review on existing characterisation methods, analyses the difficulties in their application and discusses different modelling ideas to describe the cutting edge profile. Based hereon, a new algorithm and geometrical parameterisation of the cutting edge is proposed, which reduces uncertainties and difficulties in the application of currently available methods. The proposed method considers measurement uncertainties and is robust against form errors and creates thus the basis required for the study of the influence of rounded cutting edges. [ABSTRACT FROM AUTHOR]

Machining of composite materials is an important and current topic in modern researches on manufacturing processes. Determination of optimal cutting parameters is one of the most important elements in the machinability study of composites. Optimization has significant practical importance particularly for operating the machineries. In order to increase the accuracy of drill holes, the tool must be in good condition always as much as possible. To achieve good condition of tool, the optimization of machining parameters like drill bit diameter, spindle speed, and feed rate are mandatory. The objective of this paper is to study the effect of these process parameters on thrust force, torque, and tool wear in drilling of coir fiber-reinforced composites. The optimal settings of the parameters were determined through experiments planned, conducted, and analyzed using the Box-Behnken design, Nelder-Mead, and genetic algorithm methods. This paper also aimed to increase the cutting condition of tool, i.e., minimization of tool wear by applying the optimized input parameters using Nelder-Mead and genetic algorithm techniques. [ABSTRACT FROM AUTHOR]

In this paper, we consider the job shop scheduling problem (JSS) with non-anticipatory, per-machine, sequence-dependent setup times (SDST). The contributions of this paper are twofold. First, we propose a formulation in the form of a mixed-integer linear programming (MILP) model to modelize the aforementioned problem. Second, we play a pioneering effort for the effective adaptation of a novel metaheuristic known as electromagnetism-like algorithm (EMA) to solve the foregoing problem under the minimization of makespan. Afterwards, we evaluate the performance of the proposed MILP model, the EMA, and other effective metaheuristic algorithms from the literature on two different sets of benchmarks: small-sized and large-sized instances. The rationale behind applying the MILP model and the other algorithms at the small-sized instances is to compare the solutions obtained by the metaheuristic algorithms and the optimal solutions obtained by the MILP model (optimality gap analysis). Subsequently, to demonstrate the competitiveness of the EMA against some effective algorithms in the literature, we conduct an experimental design based on Taillard's benchmark, which is considered as large-sized instances. The purpose of conducting this very experiment is to show whether the acceptable performance of the EMA is transferrable to large-sized instances. The computational evaluations simply manifest the superiority of our proposed algorithm vs the other high-performing algorithms over both small and large instances. [ABSTRACT FROM AUTHOR]

In any machining fixture, the workpiece elastic deformation caused during machining influences the dimensional and form errors of the workpiece. Placing each locator and clamp in an optimal place can minimize the elastic deformation of the workpiece, which in turn minimizes the dimensional and form errors of the workpiece. Design of fixture configuration (layout) is a procedure to establish the workpiece–fixture contact through optimal positioning of clamping and locating elements. In this paper, an ant colony algorithm (ACA) based discrete and continuous optimization methods are applied for optimizing the machining fixture layout so that the workpiece elastic deformation is minimized. The finite element method (FEM) is used for determining the dynamic response of the workpiece caused due to machining and clamping forces. The dynamic response of the workpiece is simulated for all ACA runs. This paper proves that the ACA-based continuous fixture layout optimization method exhibits the better results than that of ACA-based discrete fixture layout optimization method. [ABSTRACT FROM AUTHOR]

In this paper, a real-world test problem is presented and made available for the use of evolutionary multi-objective community. The generation of manipulator trajectories by considering multiple objectives and obstacle avoidance is a non-trivial optimisation problem. In this paper two multi-objective evolutionary algorithms viz., elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE) algorithm are proposed to address this problem. Multiple criteria are optimised to two simultaneous objectives. Simulations results are presented for industrial robots with two degrees of freedom (Cartesian robot (PP) with two prismatic joints) and six degrees of freedom (PUMA 560 robot), by considering two objectives optimisation. Two methods (normalized weighting objective functions and average fuzzy membership function) are used to select the best optimal solution from Pareto optimal fronts. Two multi-objective performance measures namely solution spread measure and ratio of non-dominated individuals are used to evaluate the strength of Pareto optimal fronts. Two more multi-objective performance measures namely optimiser overhead and algorithm effort are used to find computational effort of NSGA-II and MODE algorithms. The Pareto optimal fronts and results obtained from various techniques are compared and analysed. [ABSTRACT FROM AUTHOR]

Injection moulding optimisation seeks to achieve the highest possible moulding quality under the specified constraints. To this end, the factors (design variables) affecting the moulding quality should be adjusted, including those of process parameters, mould design, part geometry, etc. Past work in this aspect is primarily focused on tuning the process parameters and mould design (e.g., gate location, runner and cooling channel layout), with less attention on the part geometry, and none on them all. To address this problem, this paper presents a PSO (particle swarm optimisation) algorithm for the optimisation of multi-class design variables, such as the part thickness, process parameters (melt temperature, mould temperature, injection time) and gate location. The optimisation is targeted at different aspects of moulding quality, including part warpage, weld lines, air traps, and so on. In applying the PSO algorithm, the paper proposes a modified elite archiving method, which can expedite the convergence speed, hence improving the efficiency of the algorithm. A computer program was developed that automates the steps such as adjusting the part thickness, the injection moulding process parameters and the gate location, activating the CAE software to simulate the injection moulding process, retrieving the simulation results, and evaluating the objective functions. The whole procedure iterates a number of generations by following the search process of the algorithm. A case study was also presented to illustrate as well as to test the proposed methodology, which was demonstrated as both effective and efficient. [ABSTRACT FROM AUTHOR]

In this paper, the optimization of cutting parameters for constrained machining operations is reported. Modified genetic algorithm (MGA) is an evolutionary computation technique that has been proposed in this paper to solve the machining problem. Additional constraints have been incorporated to the multipass turning model. The optimization of drilling and facing parameters have also been carried out. To demonstrate the procedure and performance of the approach, an illustrative example is discussed. The results of the proposed algorithm are compared with other traditional and non-traditional techniques such as Newton’s method, hill climbing and ants colony technique. [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]

This paper deals with the tool path optimisation procedure in free form die-cavity pocketing. Many researchers have investigated pocket machining problems, and most of them focused on the tool-path element generation problem, which has been regarded as the key issue in the pocketing tool-path generation. However, free form die-cavity pocketing involves many other problems, such as machining area extraction, uncut removal, local tool path linking and global tool path linking. All these problems should be solved to optimise pocketing tool path and each of them involves its own geometrical and technological issues. The objective of this paper is not to develop a new algorithm, but to achieve the global optimisation of pocket machining by considering all issues and solutions in pocketing. [ABSTRACT FROM AUTHOR]

In CNC cam grinding, the contour error will inevitably exceed the regular range when two axes have different lag caused by the high-speed grinding regardless of the dynamic characteristics of an individual system. To address this problem, a new speed algorithm is proposed in this paper, which is based on a new contour error model using the idea of lag synchronization and can improve the contour accuracy. In this work, a new concept of relative speed that is grinding carriage to the cam is first proposed, and the parametric equations of the relative speed are constructed. In comparison with the constant angular velocity grinding, it demonstrates that significant improvements in contouring accuracy can be realized through such an approach. Furthermore, two practical examples are given to verify the accuracy and reliability. The application results illustrate the cam contour error is less than 0.01 mm. [ABSTRACT FROM AUTHOR]

This paper presents a new enhanced biogeography-based optimization algorithm (EBBO) for a complex variant of vehicle routing problem (VRP) called HVRPMBTW (vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows). This variant is characterized by a limited number of vehicles with various capacities and costs. The vehicles serves two types of customers: linehaul customers and backhaul customers. Each customer must be visited in a specific interval of time. We propose to improve and to adapt the biogeography-based optimization (BBO) approach to the problem by integrating simulated annealing algorithm to enhance solution quality, at each iteration. This new approach was tested on benchmarks and produces very satisfactory results compared to other approaches, PSO and ACO []. [ABSTRACT FROM AUTHOR]

This paper presents a methodology and algorithm of optimizing and smoothing the laser head orientation control for 3D laser cutting. The frequent changes of laser head orientation in 3D laser cutting have a great impact on the cutting efficiency, operating safety and stability. Firstly, a kinematic model of 3D laser cutting machine is presented to obtain optional values of 5-axis motion coordinates at each cutter point when geometric information of cutter points on the cutting path is transformed to the motion coordinates of 5-axis. Considering the mass and inertia of two rotational axes as well as the friction between the moving parts, an energy consumption model of the 3D laser cutting between adjacent cutter points is established. With the objective of minimizing total energy consumption of B- and C-axis motion on the whole cutting path, the optimal cutting path can be found though the Dijkstra shortest path algorithm. The excessively big momentary swing of B and C rotation axes can therefore be avoided and the smoothness of the laser cutting is improved. [ABSTRACT FROM AUTHOR]

In machining large complex parts with critical stock allowance, even small deviations in the blank parts or slight inadequacy in the fixturing may result in local shortage of material (i.e., insufficient stock allowance). This paper presents an optimal localization algorithm that aligns the measured points from a blank part with the nominal model to assure the weakest allowance area with as much material to be cut as possible. Instead of exploiting extra constraints to force the allowance at each point larger than a specific value, which is a popular strategy for allowance assurance in the previous localization algorithms, we formulate the blank localization problem as an unconstrained max-min problem. To deal with the unsmoothness exhibiting in the max-min objective function, a method based on the entropy optimization principle is adopted to convert the non-differential objective function to an unconstrained differential one, which can be efficiently solved using the conventional Quasi-Newton algorithms. The unconstrained optimization result finally gives rise to localization with the maximum allowance margin. For the blank parts that the material shortage is inevitable, the method can still efficiently achieve reasonable localization results, which confine the material shortage to a least extent. The proposed method is easy to be implemented and works well for both sparse sample points and dense-scanned points. Case studies included justify the superiority of the proposed scheme over the existing non-linear constrained optimization solutions. [ABSTRACT FROM AUTHOR]

Automated manufacturing processes such as automotive tandem press lines include time dependent complex control functions. All motions and critical interactions between moving parts must be synchronised to avoid collisions and reach high production rate. It is even for a skilled operator hard to optimise these processes on-line. Therefore, a hardware-in-the-loop simulation including real industrial control systems and its control code establish an essential tool for optimisation. Additionally, an efficient optimisation algorithm is required to reach a useful simulation-based optimisation method. This paper proposes a new optimisation algorithm starting with the Lipschitzian algorithm DIRECT as global search and then switches over to the Nelder–Mead simplex algorithm for local convergence. During the switch over, the new algorithm determines all local candidates of the set of points evaluated by DIRECT and starts multiple Nelder–Mead local searches in each of these. An optimisation study for an automotive press line shows that the proposed algorithm combines the benefits of the Lipschitzian and the simplex algorithms in an efficient way. The importance of multiple local searches from all local candidates found is also shown in the study. Based on the same number of function evaluations, it is also shown that this algorithm reaches improved press line performances compared to the stochastic differential evolution algorithm. [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]

In this paper, the berth allocation problem with stochastic vessel handling times is formulated as a bi-objective problem. To solve the resulting problem, an evolutionary algorithm-based heuristic and a simulation-based Pareto front pruning algorithm is proposed. Computational examples show that the proposed approach provides solutions superior to the ones where the expected value of the vessel handling times is used. [ABSTRACT FROM AUTHOR]

When encountering too many records, each of which has several attributes, clustering of the data is an important issue on mining and classification. Recently many advances on clustering algorithms have been made such that clustering of data is done precisely and quickly. Clustering algorithms use optimization algorithms which simultaneously provide the number of clusters as default. These algorithms cluster the data so that those which belong to a cluster have maximum similarity and those in different clusters have minimum similarity. The k-means algorithm is a traditional algorithm for clustering problems. One of the most important difficulties of clustering algorithms is determining the number of clusters before starting the algorithm. In other words, by having knowledge on distribution of data, the number of clusters should be estimated and then imported to the problem as an input. In this paper, the data collected on quality control of mechanized tunneling are analyzed. They consist of measurements of 16 characteristics for 200 initial installed rings of segments on the tunnel walls inspected by the quality control team. A dynamic validity index is used and combined to the k-means algorithm for clustering the data so that the optimal number of clusters can be determined simultaneously. The application of the algorithm shows that the total installed rings can be clustered into four clusters. These four classes of quality can best describe the total installed rings on the tunnel in comparison of other number of classes (or clusters). Furthermore, this approach helps the quality team to determine the most effective or best performance executive team whom their installed rings have best class and minimum variations. [ABSTRACT FROM AUTHOR]

Due to the continuous growth in the application of networks in manufacturing, quality of service (QoS) has become an important issue. In this paper, the concept of QoS for manufacturing networks is discussed. To provide overall performance assurance for manufacturing networks, a service framework integrating the QoS mechanisms of the networked resource service management function and the communication networks is proposed. The novel framework maps an application to resource services and then to communication networks, adopts an intelligent optimisation algorithm for QoS management of resource services, and provides QoS schemes for data transfer across communication networks. A prototype implementation has been realised and a set of simulation experiments conducted to evaluate the validity of the framework. The results obtained demonstrate the ability of the framework to satisfy the various performance requirements posed by such applications and provide efficient overall performance assurance for manufacturing networks. [ABSTRACT FROM AUTHOR]

The machining process for vertical turning martensitic gray cast iron is of great importance to the automotive industry, mainly in the manufacturing process of piston rings. The aim of this paper is to demonstrate the process of development of coated carbide tools to maximize the productivity of the process, considering the maximum life of the cutting tool and the minimum machining cost per part. Using full-factorial design of experiments, we tested two different geometries: a square tool with special geometry-formed by two edges and two ends simultaneously cutting-and a hexagonal tool. Considering that the special square geometry provided maximum life, full quadratic models for responses of interest were constructed using a central composite design for feed ( f) and rotation ( n). Applying the generalized reduced gradient algorithm, the proposed optimization goals were achieved with feed f = 0.37 mm/v and rotation of 264 rpm for the use of the special square tool. Confirmation experiments prove the effectiveness of this solution. [ABSTRACT FROM AUTHOR]

In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost. [ABSTRACT FROM AUTHOR]

This paper focuses on studying and adapting modeling techniques using the finite element method to simulate the rigid die compaction of metal powders. First, it presents the implementation of the cap constitutive model into ABAQUS FE software using the closest point projection algorithm. Then, an inverse modeling procedure was proposed to alleviate the problems raised by the interpretation of the experimental tests and to more accurately determine the material parameters. The objective function is formed, based on the discrepancy in density data between the numerical model prediction and the experiment. Minimization of the objective function with respect to the material parameters was performed using an in-house optimization software shell built on a modified Levenberg-Marquardt method. Thus, an integrated simulation module consisting of an inverse optimization method and a finite element method was developed for modeling the powder compaction process as a whole. The simulation and identification module developed was applied to simulate the compaction of some industrial parts. The results reveal that the maximum absolute error between densities is 2.3%. It corresponds to the precision of the experimental method. [ABSTRACT FROM AUTHOR]

In the real world, production scheduling systems, usually optimal job scheduling, requires an explicit consideration of sequence-dependent setup times. One of the most important scheduling criteria in practical systems is makespan. In this paper, the author presents an ant colony optimization (ACO) algorithm for the sequence-dependent permutation flowshop scheduling problem. The proposed ACO algorithm benefits from a new approach for computing the initial pheromone values and a local search. The proposed algorithm is tested on randomly generated problem instances and results indicate that it is very competitive with the existing best metaheuristics. [ABSTRACT FROM AUTHOR]

This paper presents an optimized path generation algorithm for direction parallel milling, which is commonly used in the roughing and finishing stages. First, a geometrically efficient tool path generation algorithm using an intersection points graph is introduced. Second, the generated tool path is modified as an optimized tool path that maintains a constant material removal rate to achieve a constant cutting force and avoid chatter vibration, and the results are verified. Additional tool path segments are appended to the basic tool path through a pixel-based simulation technique. The algorithm is implemented for two-dimensional contiguous end milling operations with flat end mills, and cutting tests are conducted by measuring the spindle current, which reflects the changing machining situations, to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an integrated algorithm for efficiency assessment of wireless communication sectors with imprecise data. It is based on standard indicators as defined by the International Telecommunication Union (ITU). It uses data envelopment analysis (DEA) and fuzzy data envelopment analysis (FDEA) for efficiency assessment and optimization. The DEA models are verified and validated by principal component analysis and Spearman correlation technique. Eight standard indicators pertaining to wireless communication sector were identified from the ITU database. To present the usability of the algorithm, data for 42 countries with respect to three inputs and five outputs were collected through ITU. The results show weak and strong points of each country identifying inputs or outputs having a major impact on performance. Finally, FDEA is used for the performance assessment of wireless communication industry in seven different regions, namely, Central and South Africa, Central America, Central Asia, East Asia, European Union, Middle East, and North Africa. This is the first study that uses DEA and FDEA for the performance assessment and optimization of the wireless communication sector. [ABSTRACT FROM AUTHOR]

With the advent use of sophisticated and high-cost machines coupled with higher labor costs, concurrent optimization of machining process parameters and tolerance allocation plays a vital role in producing the parts economically. In this paper, an effort is made to concurrently optimize the manufacturing cost of piston and cylinder components by optimizing the operating parameters of the machining processes. Design of experiments (DoE) is adopted to investigate systematically the machining process parameters that influence product quality. In addition, tolerance plays a vital role in assembly of parts in manufacturing industries. For the selected piston and cylinder component, improvements efforts are made to reduce the total manufacturing cost of the components. By making use of central composite rotatable design method, a module of DoE, a mathematical model is developed for predicting the standard deviation of the tolerance achieved by grinding process. This mathematical model, which gives 93.3% accuracy, is used to calculate the quality loss cost. The intent of concurrent optimization problem is to minimize total manufacturing cost and quality loss function. Genetic algorithm is followed for optimizing the parameters. The results prove that there is a considerable reduction in manufacturing cost without violating the required tolerance, cutting force, and power. [ABSTRACT FROM AUTHOR]

Grouping the machines and parts in a cellular manufacturing system based on similarities is known as the cell formation problem. It has been shown that cell formation problem is a NP-hard problem. In this paper, the ant colony optimization (ACO) method is used as an evolutionary approach to solve the cell formation problem. This model uses a P = [ P] pheromone matrix in which C, M, and P are the number of cells, machines, and parts, respectively. In order to represent the sequence of operations, the machine-part incidence matrix entries are considered as positive integers. Performance of the proposed algorithm is tested on some benchmark problems existing in the literature to show the applicability and effectiveness of the proposed model. Comparison of the solutions obtained by the proposed algorithm with those reported in the literature indicates that application of the proposed algorithm has resulted in 5.73% improvement in the total number of intercellular movements and voids on average. [ABSTRACT FROM AUTHOR]

This paper addresses the unrelated parallel machine scheduling problem with job sequence- and machine-dependent setup times. The preemption of jobs is not permitted, and the optimization criteria are to simultaneously minimize total weighted flow time and total weighted tardiness. The problem has applications in industries such as TFT-LCD, automobile, and textile manufactures. In this study, a Pareto evolutionary approach is proposed to solve the bi-objective scheduling problem. The performance of this approach using different encoding and decoding schemes is evaluated and is compared with that of two multi-objective simulated annealing algorithms via a set of instances generated by a method in the literature. The experimental results indicate that the Pareto evolutionary approach using random key representation and weighted bipartite matching optimization method outperforms the other algorithms in terms of closeness metric, based on similar computation times. Additionally, although the proposed method does not provide the best distribution in terms of diversity metric, it found most of the reference solutions. [ABSTRACT FROM AUTHOR]

Considering alternative machines for operations, forbidden intervals during which machines cannot be available and a job’s batch size greater than one in the real manufacturing environment, this paper studies the batch splitting scheduling problem on alternative machines with forbidden intervals, based on the objective to minimize the makespan. A scheduling model is established, taking before-arrival set-up, processing, and transfer time into account. And a new hybrid parallel algorithm, based on differential evolution and genetic algorithm, is brought forward to solve both the batch splitting problem and the batch scheduling problem by assuming a common number of sub-batches in advance. A solution consists of the actual optimum number of sub-batches for each job, the optimum batch size for each sub-batch, and the optimum sequence of operations for these sub-batches. Experiments on the performance of the proposed algorithm under different common numbers of sub-batches are carried out. The results of simulations indicate that the algorithm is feasible and efficient. [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]

The berth scheduling problem deals with the assignment of vessels to berth space in a container terminal. Defining berth schedules in container terminal operations translates in meeting different objectives that are often non-commensurable and gaining an improvement on one objective often causes degrading performance on the others. In this paper the discrete space and dynamic arrival berth scheduling problem is studied and formulated for the first time via a hierarchical optimization framework, using two levels of hierarchy that differentiate between two conflicting objectives terminal operators face when defining vessel to berth assignments. For the resolution of this problem an interactive algorithm is developed based on the k-th best algorithm for the case where multi-objective functions are considered in the upper level. Computational examples showed that the proposed algorithm gives optimal or near optimal solutions that are comparable to the ones obtained by its single level formulation counterpart. [ABSTRACT FROM AUTHOR]

This paper presents a simplified algorithm for the profiling of the rack gear tool to generate the active profile associated with rolling centrodes. The algorithm is based on the enveloping surfaces principles and reduces the computational problem by proposing an approximate representation of tool’s profile using only three or four points on the profile to be generated by using Bezier representations of the surfaces. The algorithm computes the coefficients of a Bezier substitution polynomial instead of computing hundreds of points on the active profile. This simplifies the solution methodology and requires only a limited number of points along the profile to be generated. The algorithm is computationally efficient and can also be used in the case when the active profile to be generated is physically measured at a few points. Examples include mill tools, splined shafts, and gears. [ABSTRACT FROM AUTHOR]

This paper presents the development of a parameter optimization system that integrates mold flow analysis, the Taguchi method, analysis of variance (ANOVA), back-propagation neural networks (BPNNs), genetic algorithms (GAs), and the Davidon–Fletcher–Powell (DFP) method to generate optimal process parameter settings for multiple-input single-output plastic injection molding. In the computer-aided engineering simulations, Moldex3D software was employed to determine the preliminary process parameter settings. For process parameter optimization, an L25 orthogonal array experiment was conducted to arrange the number of experimental runs. The injection time, velocity pressure switch position, packing pressure, and injection velocity were employed as process control parameters, with product weight as the target quality. The significant process parameters influencing the product weight and the signal to noise (S/N) ratio were determined using experimental data based on the ANOVA method. Experimental data from the Taguchi method were used to train and test the BPNNs. Then, the BPNN was combined with the DFP method and the GAs to determine the final optimal parameter settings. Three confirmation experiments were performed to verify the effectiveness of the proposed system. Experimental results show that the proposed system not only avoids shortcomings inherent in the commonly used Taguchi method but also produced significant quality and cost advantages. [ABSTRACT FROM AUTHOR]

The facility layout design problem is an extensively studied research problem and belongs to nonpolynomial hard (NP-hard) combinatorial optimization problem. Quadratic assignment problem (QAP) is one of the formulations that is investigated for facility layout design because of its wide applicability. Ant colony optimization (ACO), a biologically inspired heuristic has centered on solving the QAP by achieving approximation as good as possible. This paper presents a population-based hybrid ant system (PHAS), which is an extension of the hybrid ant system (HAS) in which the size of the ant colony has been fixed. The performance of the proposed ant algorithm for QAP is compared with the existing metaheuristic implementations such as tabu search, reactive tabu search, simulated annealing, genetic hybrid method, HAS, and max–min ant system. The experimental results show that the proposed PHAS perform significantly better than the other existing algorithms of QAP. [ABSTRACT FROM AUTHOR]

In manufacturing practice for each machining operation, transfer techniques are extensively used for the allocation of manufacturing specification tolerance type and value. This paper focuses more on tolerance type which is the basis for a coherent and complete tolerancing process. We developed an algorithmic method called “DISMP” in order to generate the necessary types of manufacturing specifications that guarantee the respect of the functional requirements. A geometric variation model, based on the invariants degrees of freedom (DOFs) of the datum reference frames (DRF) and the toleranced surfaces serves to specify the two extremities of the tolerance chain. The identification of the controlled DOFs (Cont-DOFs) of the positioning reference frames in each machining phases and also the constrained DOFs (Cons-DOFs) of each machined surface contributes to the development of the way linking between the functional toleranced surface and its functional DRF. To generate the appropriate manufacturing geometric specifications, our method can be divided in five steps. We started by the translation of the ISO functional specification using the topologically and technologically related surfaces rules “TTRS” (Step 1: M1). Mapping the manufacturing process (Step 2: M2 and M3) requires the identification of the Cons-DOFs of the machined surfaces and those controlled by the positioning surfaces “Cont-DOFs” in each phase. The search of all chain links, which constitute the tolerance chain, is realized on Step3 (M4 and M5). Then, in step 4 (M6), we generate ISO standardized manufacturing specification. The fifth step (M7) draft technological constraints related to the manufacturing process, i.e., the case where there are inversion in the order between the toleranced surfaces and its DRF. Finally, we present the manufacturing process phases with ISO standards requirements. [ABSTRACT FROM AUTHOR]

The academic approach of single-objective flowshop scheduling has been extended to multiple objectives to meet the requirements of realistic manufacturing systems. Many algorithms have been developed to search for optimal or near-optimal solutions due to the computational cost of determining exact solutions. This paper provides a particle swarm optimization-based multi-objective algorithm for flowshop scheduling. The proposed evolutionary algorithm searches the Pareto optimal solution for objectives by considering the makespan, mean flow time, and machine idle time. The algorithm was tested on benchmark problems to evaluate its performance. The results show that the modified particle swarm optimization algorithm performed better in terms of searching quality and efficiency than other traditional heuristics. [ABSTRACT FROM AUTHOR]

This paper proposes a neurogenetic-based optimization scheme for predicting localized stable cutting parameters in inward turning operation. A set of cutting experiments are performed in inward orthogonal turning operation. The cutting forces, surface roughness, and critical chatter locations are predicted as a function of operating variables including tool overhang length. Radial basis function neural network is employed to develop the generalization models. Optimum cutting parameters are predicted from the model using binary-coded genetic algorithms. Results are illustrated with the data corresponding to four work materials, i.e., EN8 steel, EN24 steel, mild steel, and aluminum operated over a high speed steel tool. [ABSTRACT FROM AUTHOR]

Using automatic localization to ensure sufficient machining allowance or derive reliable evaluation results of machining quality, it is possible to cope with the case where the coordinate system in a part design specification is different from that for machining or measuring the part. To achieve workpiece localization and machining quality evaluation, it is essential to find the optimum Euclidean transformation that aligns the nominal mode to the sample points from a workpiece. This paper describes a unified algorithm for workpiece localization and quality evaluation. The optimum alignment model is firstly established with minimax criteria, and then sequential quadratic programming (SQP) is proposed to solve the optimization problem. A computational algorithm of point-to-surface distance and a linear differential motion model of the objective function with respect to alignment parameters are subsequently provided. The results show that the SQP-based workpiece localization and quality evaluation method is computationally more efficient than those based on direct search algorithms. It is found that the proposed method is effective in machining localization and quality evaluation even with a large set of sample points. [ABSTRACT FROM AUTHOR]

A novel hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed in this paper to solve the no-idle permutation flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, two simple approaches are presented to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the whole insert neighborhood of a job permutation with ( n−1)2 neighbors in time O( mn 2), where n and m denote the number of jobs and machines, respectively. Thirdly, a discrete particle swarm optimization (DPSO) algorithm based on permutation representation and a local search algorithm based on the insert neighborhood are fused to enhance the searching ability and to balance the exploration and exploitation. Then, computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed HDPSO algorithm is not only superior to two recently published heuristics, the improved greedy (IG) heuristic and Kalczynski–Kamburowski (KK) heuristic, in terms of searching quality, but also superior to the single DPSO algorithm and the PSO algorithm with variable neighborhood search (PSOvns) in terms of searching quality, robustness and efficiency. [ABSTRACT FROM AUTHOR]