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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]

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]

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]

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, 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]

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]

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]

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 considers an interesting topic of recipe qualification during each fabrication step of semiconductor manufacturing. In particular, this type of stochastic optimization scheme within a discrete-event simulation process is discussed and a new optimization method, with the capability to anchor robust recipes for batch processing, is proposed, implemented and evaluated. A particular real-coded genetic algorithm (GA) suitable for resolving continuous optimization problems in noisy environments is developed. Four test functions appearing in the literature are used as a test-bed to assess the proposed genetic algorithm via a variety of experimental studies, showing that the new method can produce much more accurate estimates of the true optimum points than the other two optimization procedures, the Nelder-Mead (NM) simplex search procedure and a well-known variant (RS+S9) of Nelder-Mead suitable for stochastic optimization. As such, the new method could serve as a useful tool for process recipe optimization in noisy semiconductor manufacturing environments. Finally, the chemical mechanical planarization (CMP) process, a turnkey process during semiconductor fabrication, is simulated from batch to batch based on the real-data equipment model and the presented algorithm is employed to seek the optimal recipe profile while processing each batch of wafers sequentially through the CMP tool. [ABSTRACT FROM AUTHOR]

Conventional tolerance analysis is tedious and time consuming, which makes engineers resist doing it. Complex assembly problems are generally beyond the capabilities of most design and manufacturing engineers. In this paper, genetic algorithm, a kind of non-traditional optimization technique is used as the basic foundation for optimal tolerance allocation to help design and manufacturing engineers to overcome the shortcomings in the conventional tolerance stack analysis and allocation system. [ABSTRACT FROM AUTHOR]