Consecutive Machines Research Articles

Influence of Machine Hammer Peening on the Tribology of Sheet Forming

The recently developed machine hammer peening process is used at the die shop of the Mercedes-Benz plant in Sindelfingen in order to replace manual surface finish of deep drawing dies. The goal of the process is surface roughness reduction after milling to ensure the tribological properties, which are necessary for the sheet metal forming process. Using machine hammer peening it is also possible to create defined surface structures that may be employed to influence local friction conditions and therewith overcome current limitations of the forming process. To take advantage of the surface structuring capabilities it is necessary to understand how to create defined surface structures using machine hammer peening and how the created structures affect friction and material flow behavior. In this work an approach is presented to describe the interaction of milling and machine hammer peening parameters on the created topography by wave theory. Especially the influence of tool path parameters of milling and consecutive machine hammer peening is investigated. The results, which are calculated using wave theory, are verified by FEM simulations and real experiments. In addition, suitable process parameters for machine hammer peening are derived from the obtained results, as they are used at the Mercedes-Benz die shop today.

Scheduling Two-machine Flowshop with Limited Waiting Times to Minimize Makespan

There are numerous instances of flowshop in the production process of process industry. When such characteristics as continuous production resulted from high-temperature environment or deteriorate intermediate products are took into consideration, it should be ensured that the waiting time of any job between two consecutive machines is not greater than a given value, which results in the flowshop scheduling problem with limited waiting time constraints. The problem with two-machine environment to minimize makespan is studied. Based on the discussion of the lower bound of the minimal makespan and some properties of the optimal schedule, a two-stage search algorithm is proposed, in which the initial schedule is generated by a modified LK heuristic in the first stage and the excellent solution can be obtained by constructing inserting neighborhood in the second stage. The numerical results demonstrate the effectiveness of the algorithm. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4783

Neighborhood Search Heuristic for 2-Machine Flowshop Scheduling Problem with Limited Waiting Times

The flowshop scheduling problem with limited waiting time constraints widely exists in the production process featured by high temperature and continuity. The constraints require that the waiting time of any job between two consecutive machines is not greater than a given upper bound. In this paper, the problem with two-machine settings and the objective of makespan is studied. First, a lower bound and some characters of minimum makespan are analyzed. Further, a solving idea is suggested by a transformation into an asymmetry TSP. Based on these characteristics and the solving idea, a neighborhood search algorithm embedding a modified Lin-Kernighan heuristic is presented for the problem. Numerical results demonstrated the effectiveness and efficiency of the algorithm.

A combined robot selection and scheduling problem for flow-shops with no-wait restrictions

This paper addresses a bicriteria no-wait flow-shop scheduling problem with multiple robots transferring jobs between pairs of consecutive machines. The robots share an identical track positioned alongside the machine transfer line. Each robot is assigned to a portion of the tract from which it performs job transfers between all reachable machines. We assume that job processing times are both machine and job independent, that jobs are not allowed to wait between two consecutive machines and that machine idle times are not allowed. We define a combined robot selection and scheduling problem (RSSP) for a set of Q non-identical robots characterized by different costs and job transfer and empty movement times. A solution to the RSSP problem is defined by (i) selecting a set of robots, (ii) assigning each robot to a portion of the track, and (iii) scheduling the robot moves. We define a robot schedule as feasible if all the jobs satisfy the no-wait condition and there are no machine idle times. The quality of the solutions are measured by two criteria (performance measures): makespan and robot selection cost. We study four different variations of the RSSP,one which is shown to be solvable in polynomial time while the other three turn out to be NP-hard. For the NP-hard, we show that a pseudo-polynomial time algorithm and a fully polynomial approximation scheme exists, and derive three important special cases which are solvable in polynomial time. The RSSP has aspects of robot selection, machine-robot assignment and robot movement scheduling. We believe this is the first time that this type of problem has been treated in the literature, and addresses a very important problem in multiple robotic systems operation. Our contribution lies in the formulation, methodology, algorithms for solution and complexity results which jointly treats all aspects of the problem simultaneously without the need to defer to heuristic decomposition methods.

An improved particle swarm optimisation with a linearly decreasing disturbance term for flow shop scheduling with limited buffers

The flow shop scheduling problem with limited buffers is a typical combinational optimisation problem that is NP-hard. In this article, an improved particle swarm optimisation with a linearly decreasing disturbance term (LDPSO) is presented for permutation flow shop scheduling with limited buffers between consecutive machines to minimise the maximum completion time (i.e. the makespan). A linearly decreasing disturbance term was added to the velocity, updating formula of the standard particle swarm optimisation algorithm. The decision probability of the linearly decreasing disturbance term was used to control the utilisation of the global exploration operation and the local exploitation search based on problem-specific information so as to prevent premature convergence and concentrate computing efforts on promising neighbour solutions. Theoretical analysis based on previous studies showed that the improved algorithm converged to the global optimum at a probability of 1. The ranked-order-value encoded method transferred the continuous particle position of the LDPSO to the order sequence. Furthermore, the neighbour search strategy based on block guaranteed that the entire order sequence could be searched. Simulation results and comparisons based on benchmarks demonstrate the effectiveness of the LDPSO. The effects of buffer size and decision probability on optimisation performance are discussed in this article.

Integrated lot-sizing and scheduling with overlapping for multi-level capacitated production system

In this paper, multi-product multi-period integrated lot-sizing and scheduling problem with a new practical condition, overlapping in operations, is discussed in a capacitated multi-stage production system. In many real situations, products are carried using pallets or conveyers, so there is no constraint to complete the entire process of products lots and then transfer them to the next stage. To consider this issue in the problem an approach, named overlapping in operations, is developed in this paper. For each product, embedded operations on the two consecutive machines can be performed due to overlapping consideration in which each operation of certain product may be overlapped with the another operation of the same product on the next machine. A novel mixed-integer programming (MIP) model is proposed for the problem to minimise the maximum makespan over the planning horizon for the corresponding production plan. The mathematical model is impractical to solve large-scale problems in reasonable time. So, two MIP-based heuristics using reduced size MIP models and simulated annealing algorithm is proposed to solve non-small instances of the problem. Moreover, a MIP method is used to validate the proposed solution approaches. The presented algorithms explore the solution space for both lot-sizing and scheduling problems. A combination of production plan and sequence that is feasible and close to optimum can be found using these algorithms. Computational experiences show that the proposed algorithms can find good quality solution for the problem in a reasonable time. The computational experiences also confirm that the makespan measure and machines utilisation rate can be improved by overlapping consideration.

An effective hybrid discrete differential evolution algorithm for the flow shop scheduling with intermediate buffers

In this paper, an effective hybrid discrete differential evolution (HDDE) algorithm is proposed to minimize the maximum completion time (makespan) for a flow shop scheduling problem with intermediate buffers located between two consecutive machines. Different from traditional differential evolution algorithms, the proposed HDDE algorithm adopted job permutation to represent individuals and applies job-permutation-based mutation and crossover operations to generate new candidate solutions. Moreover, a one-to-one selection scheme with probabilistic jumping is used to determine whether the candidates will become members of the target population in next generation. In addition, an efficient local search algorithm based on both insert and swap neighborhood structures is presented and embedded in the HDDE algorithm to enhance the algorithm’s local searching ability. Computational simulations and comparisons based on the well-known benchmark instances are provided. It shows that the proposed HDDE algorithm is not only capable to generate better results than the existing hybrid genetic algorithm and hybrid particle swarm optimization algorithm, but outperforms two recently proposed discrete differential evolution (DDE) algorithms as well. Especially, the HDDE algorithm is able to achieve excellent results for large-scale problems with up to 500 jobs and 20 machines.

Tool breakage detection in CNC high-speed milling based in feed-motor current signals

Tool condition monitoring, mainly tool breakage detection for high-speed machining (HSM), is an important problem to solve; however, the techniques or types of sensors applied in other research projects present certain inconveniences. In order to improve tool breakage monitoring systems, a simple, effective, and fast method is presented herein. This method is based on the discrete wavelet transform (DWT) and statistical methodologies. The effectiveness of the method is based on the measurements of the feed-motor current signals using inexpensive sensors. It is well-known that during the cutting process, the motor current is related to the tool condition. The current consumption changes when the tool is broken as compared to when the tool is in normal cutting condition. This difference can be obtained from the waveform variances between the signals in order to ascertain the tool condition. The algorithms of this research project consist of obtaining compressed signals from the Irms feed-motor current signals applying the DWT. Then from these compressed signals, we detect the asymmetries between them. The arithmetic mean value is applied to asymmetries of consecutive machining lengths to reduce noise in the data having a mean value of a series of asymmetries; also, a normal cutting threshold is set up in order to make decisions regarding the tool conditions so as to detect tool breakage. Therefore, this research project shows a low-cost monitoring system that is simple to implement.

Approximation Algorithms for Scheduling Parallel Jobs

In this paper we study variants of the nonpreemptive parallel job scheduling problem in which the number of machines is polynomially bounded in the number of jobs. For this problem we show that a schedule with length at most $(1+\varepsilon)\,\mathrm{OPT}$ can be calculated in polynomial time. Unless $P=NP$, this is the best possible result (in the sense of approximation ratio), since the problem is strongly NP-hard. For the case where all jobs must be allotted to a subset of consecutive machines, a schedule with length at most $(1.5+\varepsilon)\,\mathrm{OPT}$ can be calculated in polynomial time. The previously best known results are algorithms with absolute approximation ratio 2. Furthermore, we extend both algorithms to the case of malleable jobs with the same approximation ratios.

An effective hybrid DE-based algorithm for flow shop scheduling with limited buffers

The permutation flow-shop scheduling problem (PFSSP) is a typical combinational optimization problem, which is of wide engineering background and has been proved to be strongly NP-hard. In this paper, an effective hybrid algorithm based on differential evolution (DE), namely HDE, is proposed for permutation flow-shop scheduling with limited buffers between consecutive machines to minimize the maximum completion time (i.e. makespan). First, to make DE suitable for solving PFSSP, a largest-order-value (LOV) rule is presented to convert the continuous values of individuals in DE to job permutations. Second, after the DE-based exploration, an efficient local search, which is designed according to the landscape of PFSSP and the generalization of the block elimination properties, is applied to emphasize exploitation. Thus, not only does the HDE apply the parallel evolution mechanism of DE to perform effective exploration (global search), but it also adopts problem-dependent local search to perform exploitation well (local search). Furthermore, the convergence property of HDE is analyzed based on the theory of finite Markov chain. Simulations and comparisons based on benchmarks demonstrate the effectiveness and robustness of the proposed HDE, and the effect of one key parameter on the performance of HDE is investigated as well.

An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers

This paper proposes an effective hybrid algorithm based on differential evolution (DE), namely HDE, to solve multi-objective permutation flow shop scheduling problem (MPFSSP) with limited buffers between consecutive machines, which is a typical NP-hard combinatorial optimization problem with strong engineering background. Firstly, to make DE suitable for solving scheduling problems, a largest-order-value (LOV) rule is presented to convert the continuous values of individuals in DE to job permutations. Secondly, after the DE-based exploration, an efficient local search, which is designed based on the landscape of MPFSSP with limited buffers, is applied to emphasize exploitation. Thus, not only does the HDE apply the parallel evolution mechanism of DE to perform effective exploration (global search) in the whole solution space, but it also adopts problem-dependent local search to perform thorough exploitation (local search) in the promising sub-regions. In addition, the concept of Pareto dominance is used to handle the updating of solutions in sense of multi-objective optimization. Moreover, the convergence property of HDE is analyzed by using the theory of finite Markov chain. Finally, simulations and comparisons based on benchmarks demonstrate the effectiveness and efficiency of the proposed HDE.

An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers

In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to job permutations. Second, an efficient population initialization based on the famous Nawaz–Enscore–Ham (NEH) heuristic is proposed to generate an initial population with certain quality and diversity. Third, a local search strategy based on the generalization of the block elimination properties, named block-based local search, is probabilistically applied to some good particles. Moreover, simulated annealing (SA) with multi-neighborhood guided by an adaptive meta-Lamarckian learning strategy is designed to prevent the premature convergence and concentrate computing effort on promising solutions. Simulation results and comparisons demonstrate the effectiveness of the proposed HPSO. Furthermore, the effects of some parameters are discussed.

RNS multiplication/sum-of-squares units

Digital signal processing and multimedia applications often profit from the use of a residue number system. Among the most commonly used moduli, in such systems, are those of 2n−1 and 2n+1 forms and among the most commonly used operations are multiplication and sum-of-squares. These operations are currently performed using distinct design units and/or consecutive machine cycles. Novel architectures for combined units that perform modulo 2n−1/diminished-1 modulo 2n+1 multiplication or sum-of-squares depending on the value of a control signal are proposed.

An effective hybrid particle swarm optimization for no-wait flow shop scheduling

The no-wait flow shop scheduling that requires jobs to be processed without interruption between consecutive machines is a typical NP-hard combinatorial optimization problem, and represents an important area in production scheduling. This paper proposes an effective hybrid algorithm based on particle swarm optimization (PSO) for no-wait flow shop scheduling with the criterion to minimize the maximum completion time (makespan). In the algorithm, a novel encoding scheme based on random key representation is developed, and an efficient population initialization, an effective local search based on the Nawaz-Enscore-Ham (NEH) heuristic, as well as a local search based on simulated annealing (SA) with an adaptive meta-Lamarckian learning strategy are proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm.

Enhanced toughening of poly(propylene) with reclaimed‐tire rubber

AbstractThe dynamic vulcanization of reclaimed‐tire rubber (RTR) and homopolypropylene (PP) was performed by melt‐mixing using either a sulfur crosslinking agent, maleic anhydride (MA), dicumyl peroxide (DCP), or the combination of MA and DCP, in two consecutive machines, first a two‐roll mill and then a counterrotating twin‐screw extruder. In the case of applying a sulfur crosslinking agent, it was demonstrated that the RTR/PP blend at the ratio of 30/70 had the highest impact strength. This could be attributed to the limitation of carbon black in the blend. When the combination of MA and DCP was applied, the result was higher impact strength of the blend at the same ratio. This could be attributed to not only the cohesion between the polymer chains in each phase, PP phase and rubber phase, but also the interfacial adhesion between PP and RTR chains in these two phases. For comparison, the GRT/PP blends with and without sulfur crosslinking agent were prepared as well. All these blends showed low impact strength, which was nearly the same as that of PP. The effects of different crosslinking agents on dispersion and distribution of rubber domain size, viscosity, and percentage crystallinity were also studied. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 510–515, 2004

Wear progression of diamond mills

The increasing request, both in industrial field and in urbanistic furniture, of complex-shaped products in natural stone makes necessary the use of flexible and completely automatic machines, such as NC machining centres. These machines mainly use versatile tools such as synthesised diamond mills. The aim of this work is to characterise the wear of the diamond mills by variables that are easy to measure, and effective to describe wear mechanism. An optic microscope and an electronic balance have allowed both to observe the mill shape and to measure the diameter and weight changes of the mill after consecutive machining steps. An original test protocol has been developed to carry out all experimental tests. The results show how a set of variables, that are related to the macro-geometry of the diamond mill and are called macro-geometric wear variables, may be used to foresee the tool wear in a synergic way and, therefore, to improve usage of the tool.

A Cutter Orientation Modification Method for the Reduction of Non‐linearity Errors in Five‐Axis CNC Machining

In the machining of sculptured surfaces, five‐axis CNC machine tools provide more flexibility to realize the cutter position as its axis orientation spatially changes. Conventional five‐axis machining uses straight line segments to connect consecutive machining data points, and uses linear interpolation to generate command signals for positions between end points. Due to five‐axis simultaneous and coupled rotary and linear movements, the actual machining motion trajectory is a non‐linear path. The non‐linear curve segments deviate from the linearly interpolated straight line segments, resulting in a non‐linearity machining error in each machining step. These non‐linearity errors, in addition to the linearity error, commonly create obstacles to the assurance of high machining precision. In this paper, a novel methodology for solving the non‐linearity errors problem in five‐axis CNC machining is presented. The proposed method is based on the machine type‐specific kinematics and the machining motion trajectory. Non‐linearity errors are reduced by modifying the cutter orientations without inserting additional machining data points. An off‐line processing of a set of tool path data for machining a sculptured surface illustrates that the proposed method increases machining precision.

New heuristics for no-wait flowshops to minimize makespan

This paper addresses the m-machine no-wait flowshop scheduling problem to minimize makespan. We propose two heuristics that are based on simulated annealing and Genetic Algorithm techniques. We also propose improvement procedures to these heuristics. Extensive computational experiments show that the simulated annealing heuristic outperforms the best two existing heuristics. They also show that the improvement procedures, applied to the Simulated Annealing and Genetic Algorithm, result in significant reduction in error, about 750% error reduction to Simulated Annealing and about 1960% to Genetic Algorithm. Scope and purpose An important class of scheduling problems is characterized by a no-wait constraint where jobs have to be processed continuously without waiting between or on consecutive machines. This constraint of no-waiting arises from characteristics of the processing technology itself. This paper proposes efficient Simulated Annealing and Genetic Algorithm heuristics for the m-machine no-wait flowshop scheduling problem to minimize makespan.

A new heuristic and dominance relations for no-wait flowshops with setups

The two-machine no-wait flowshop problem, where setup times are considered separate from processing times and sequence independent, is addressed with respect to minimizing total flowtime. A local and a global dominance relation are developed and a new heuristic is provided. Furthermore, a lower bound is obtained and used along with the dominance relations in a branch-and-bound algorithm in order to evaluate the efficiency of the heuristic. Computational experience demonstrates the superiority of the local dominance relation and the new heuristic. Scope and purpose No-wait flowshop problems, where jobs have to be processed without interruption between consecutive machines, represent an important area in scheduling. There are several industries where the no-wait flowshop problem applies including the metal, plastic, and chemical industries. For instance, in the case of steel production, the heated metal must continuously go through a sequence of operations before it is cooled in order to prevent defects in the composition of the material. Another important area arises when setup time is considered separate from processing time. Such a consideration is particularly justified when the ratio of setup to processing time is non-negligible. Many applications warrant separate consideration of setup; examples include the re-tooling of multi-tool equipment. Other applications can be found in textile, plastic, chemical, and semi-conductor industries. This paper develops a new heuristic and dominance relations for the two-machine no-wait separate setup flowshop problem, where the performance criterion is total flowtime.

Total flowtime in no-wait flowshops with separated setup times

An important class of scheduling problems is characterized by a no-wait constraint where the jobs have to be processed continuously without waiting between or on consecutive machines. This constraint of no-waiting arises from the characteristics of the processing technology itself. Considering setup times separate from processing times of the jobs forms another important class of scheduling problems. This is particularly important when the ratio of the setup time to the processing time is non-negligible. This paper addresses a scheduling problem which falls in the combined category of no-wait and separate setup times. The performance measure considered is the total flowtime. This paper addresses the two-machine no-wait flowshop problem where the setup time of a job is separated from its processing time. The performance measure considered is the total flowtime. An elimination criterion is developed and optimal solutions are obtained for two special cases. For the generic case, a heuristic algorithm is provided. Computational experience shows that the algorithm yields good solutions.