Discrete electromagnetism-like mechanism algorithm for assembly sequences planning

Assembly sequence planning (ASP) plays an important role in digital manufacturing. It is a combinatorial optimisation problem with strong constraints aiming to work out a specific sequence to assemble together all components of a product. The connector-based ASP, which uses the connector to simplify the complex assembly problem, is one of the most important and hardest types. In order to solve this problem effectively, a discrete electromagnetism-like mechanism (DEM) algorithm is proposed. A charge formula and a force formula are redefined in DEM algorithm. An adjacency list is applied to handle the precedence relationship and prevent infeasible solutions. Two movements based on path relinking are employed. Moreover, with two different guided mutations, the population diversity can be guaranteed. Five examples are used to test and evaluate the performance of DEM. The comparisons among the proposed DEM, traditional genetic algorithms (GAs), guided GAs, memetic algorithms and artificial immune systems show that DEM outperforms among these algorithms in terms of running time, computation accuracy, convergence speed and parameter robustness.     

Non-productive tool path optimisation for four-axis milling using the simulated annealing algorithm

The paper concerns the development of generic computer aided optimisation techniques for the minimisation of residence time of a multi-component pallet in a horizontal machining centre. A general methodology has been established to take a part program for a multi-faced pallet, that involves many components, typically 20–30, and tool changes, segment it to extract the position and machining conditions embedded in it, automatically re-sequence the machining operations to find the optimum total tool path, and regenerate a new part program with the optimised machining sequence. A range of case studies has been used to: validate the software, and to demonstrate its ability to minimise the total pallet residence time. The techniques developed can be used for semi-automatic part programming of the entire pallet with multi-components, and with an auto-selection multi-tool facility. The software is capable of achieving a large reduction in part programming time, as well as reducing the non-machining time. It is shown that the use of the optimisation package with a range of part programs reduces the total pallet residence time by a factor between 9.5 and 36%, and consequently has the potential to achieve considerable economic gains.     

A discrete electromagnetism-like mechanism for parallel machine scheduling under a grade of service provision

We consider the problem of minimising total weighted tardiness on identical parallel machines with grade of service eligibility. Due to the essential complexity of the problem, we apply an electromagnetism-like mechanism (EM), which is a novel metaheuristic, to solve the problem. In the proposed EM, the particle is redesigned to represent a valid assignment of jobs to machines. A distance measure between particles, called ‘1A2B’ distance, is proposed by the concept of a number guessing game. Then, the new attraction and repulsion operators are developed to move a particle to the new particle. To verify the proposed EM, computational experiments are conducted to make a comparison with a recent genetic algorithm (GA). The results show that the proposed EM has a good performance and outperforms the GA for the considered problem.     

Using the global optimisation methods to minimise the machining path length of the free-form surfaces in three-axis milling

During the machining of free-form surfaces using three-axis numerically controlled machine (NC), several parameters are chosen arbitrary and one of the most important is the feed motion direction. The main objective of this study is to minimise the machining time of complex surfaces while respecting a scallop height criteria. The analytical expression of the machining time is not known and by hypothesis, it is assumed to be proportional to the path length crossed by the cutting tool. This path length depends on the feed direction. To have an optimal feed direction at any point, the surface is divided into zones with low variation of the steepest slope direction. The optimization problem was formulated aiming at minimizing the global path length. Furthermore, a penalty reflecting the time loss due to the movement of the tool from one zone to another one is taken into account. Several heuristics are used to resolve this problem: Clarke and Wrights, Greedy randomized adaptive search procedure, Tabu search and Nearest neighbour search. An example illustrates our work by applying the different heuristics on a test surface. After simulations, the results obtained present a significant saving of paths length of 24% compared to the machining in one zone.     

Tool path optimisation for flank milling ruled surface based on the distance function

This paper deals with optimised tool path generation for five-axis flank milling using signed point-to-surface distance function. The main idea is that the geometrical deviations between the design surface and the machined surface are minimised by fine tuning the cutter locations. Based on the tangency conditions in envelope theory, the analytic representation of the envelope surface of a cutter undergoing five-axis motion is first obtained. Then the geometrical deviations between the envelope surface (i.e. machined surface) and the design surface are calculated. Optimisation of the five-axis tool path is modeled as the fine tuning of the initial cutter locations under the minimum zone criterion recommended by ANSI and ISO, which requires minimisation of the maximum geometrical deviation between the design surface and the envelope surface. Using the signed point-to-surface distance function, tool path optimisation for finish milling is formulated as a constrained optimisation problem. Based on the first-order Taylor approximation of the signed distance function, two sequential approximation algorithms for the Minimax and Least Square optimisations are developed. Numerical examples, in which a conical tool is chosen as a special case of flank machining ruled surface, verify the proposed strategy.     

一种基于多步回溯算法的铣削稳定性预测方法

在加工过程中,由于薄壁件的弱刚性易发生加工颤振,从而对工件表面质量和刀具寿命等造成不良的影响,对铣削过程的稳定性进行预测是至关重要的.通过提出一种多步回溯算法来预测铣削过程的稳定性,将铣削过程离散化成时滞周期方程,在每个时间间隔上采用多步回溯的方法来近似时间周期及时滞项.通过构建状态转移矩阵,根据Floquet理论获得...     

Modelling of carbon utilisation efficiency and its application in milling parameters optimisation

At present, the evaluation index for low carbon manufacturing was mainly focused on the relationship between carbon emission and economic benefits or removal volume. But there was rarely comprehensive evaluation index to evaluate the carbon utilisation level of processing. Based on this, this paper presented the carbon utilisation efficiency as the comprehensive evaluation index in low carbon manufacturing. The carbon utilisation efficiency was defined as the ratio of the carbon emission of materials removal to the whole carbon emission in manufacturing process. A carbon utilisation efficiency model was established in milling process, based on flow characteristics and removal mechanism of carbon emission during milling. Then a multi-objective optimisation model was established based on Particle Swarm Optimisation, and the minimum processing time and high carbon utilisation efficiency were set as the optimisation objectives. And the experiment was performed to confirm the undetermined constant in the optimisation model and verify the effectiveness of the optimisation model. The optimised milling parameters were verified to reduce the processing time and improve the carbon utilisation efficiency.     

Solving the response time variability problem by means of the electromagnetism-like mechanism

The response time variability problem (RTVP) is an NP-hard combinatorial scheduling problem that has been recently formalised in the literature. The RTVP has a wide range of real-life applications such as in the automobile industry, when models to be produced on a mixed-model assembly line have to be sequenced under a just-in-time production. The RTVP occurs whenever products, clients or jobs need to be sequenced so as to minimise variability in the time between the instants at which they receive the necessary resources. In two previous studies, three metaheuristic algorithms (a multi-start, a GRASP and a PSO algorithm) were proposed to solve the RTVP. We propose solving the RTVP by means of the electromagnetism-like mechanism (EM) metaheuristic algorithm. The EM algorithm is based on an analogy with the attraction-repulsion mechanism of the electromagnetism theory, where solutions are moved according to their associated charges. In this paper we compare the proposed EM metaheuristic procedure with the three metaheuristic algorithms aforementioned and it is shown that, on average, the EM procedure improves strongly on the obtained results.     

Product quality improvement method in manufacturing process based on kernel optimisation algorithm

Quality data in manufacture process has the features of mixed type, uneven distribution, dimension curse and data coupling. To apply the massive manufacturing quality data effectively to the quality analysis of the manufacture enterprise, the data pre-processing algorithm based on equivalence relation is employed to select the characteristic of hybrid data and preprocess data. KML-SVM (Optimised kernel-based hybrid manifold learning and support vector machines algorithm) is proposed. KML is adopted to solve the problems of manufacturing process quality data dimension curse. SVM is adopted to classify and predict low-dimensional embedded data, as well as to optimise support vector machine kernel function so that the classification accuracy can be maximised. The actual manufacturing process data of AVIC Shenyang Liming Aero-Engine Group Corporation Ltd is demonstrated to simulate and verify the proposed algorithm.     

Applying ant colony system algorithm in the navigation process for plastic injection mould manufacturing scheduling optimisation

Under the computer-aided design (CAD) software architecture, this study aims to develop navigation processes for plastic injection mould manufacturing scheduling optimisation. Mould manufacturing is a job-shop scheduling problem, with components processing sequence under limited conditions. This study uses the search capabilities of the ant colony system (ACS) to determine a set of optimal schedules, under the condition of not violating the processing sequences, in order to minimise the total processing time and realise makespan minimisation. As the test results suggest, it can save up to 52% of manufacturing time, and also substantially shorten the processing time of the production plan. This study completes the algorithm steps and manufacturing process time estimation by operations on the navigation interface, and uses mould manufacturing scheduling to make optimised arrangements of finished components. The method can comply with the on-site manufacturing processes, improve scheduling prediction accuracy and consistently and efficiently integrate the optimisation scheduling system and mould manufacturing system. Visualised information of the scheduling results can be provided, thus allowing production management personnel to ensure smooth scheduling.     

Cutter size optimisation and interference-free tool path generation for five-axis flank milling of centrifugal impellers

Strategies for cutter size optimisation and interference-free tool path generation are proposed for five-axis flank milling of centrifugal impellers. To increase the material removal rate and provide a stronger tool shank during flank milling, the cutter size is first maximised under a set of geometric constraints. The tool path is then globally optimised in accordance with the minimum zone criterion for the determined optimal cutter size. Aside from the local interference of the cutter with the design surface, the global interferences with the hub surface and the adjacent blade surface are also considered in the optimisation models. Interference is indicated by the signed distance from the sampled point on the blade surface to the tool envelope surface. This distance is calculated without constructing the envelope surface. On the basis of the differential property of the distance function, we choose a sequential linear programming method in implementing the optimisations. This approach applies to generic rotary cutters, such as cylindrical and conical tools. Simulations are conducted to obtain the optimal cutter size and generate an interference-free tool path for a practical impeller. Simultaneously, a software module that can generate tool envelope surfaces and verify geometric errors is used to validate the proposed method.     

Tool path generation via the multi-criteria optimisation for flat-end milling of sculptured surfaces

A method of generating optimal tool paths for sculptured surface machining with flat-end cutters is presented in this paper. The inclination and tilt angles, as well as the feed directions of the cutter at each cutter contact point on a machining path are optimised as a whole so that the machining width of the tool path can be as large as possible, and concerns such as smooth cutter motion, gouging avoidance, scallop height and machining widths overlap are also considered when calculating a path. A multi-criteria tool path optimisation model is introduced, and it is converted into a single objective optimisation with the weighted sum method. The Differential Evolution (DE) algorithm is suitable for solving this highly non-linear problem. However, the searching process of the DE algorithm may be trapped in local minima due to large number of design variables. Therefore, an algorithm combining the DE algorithm and the sequence linear programming algorithm is developed to solve this optimisation model. The proposed method is applied to two freeform surfaces to illustrate its effectiveness.     

Analysis and optimisation of a mixed fluid cascade (MFC) process

A mixed fluid cascade (MFC) process that comprises three refrigeration cycles has great capacity for large-scale LNG production, which consumes a great amount of energy. Therefore, any performance enhancement of the liquefaction process will significantly reduce the energy consumption. The MFC process is simulated and analysed by use of proprietary software, Aspen HYSYS. The effect of feed gas pressure, LNG storage pressure, water-cooler outlet temperature, different pre-cooling regimes, liquefaction, and sub-cooling refrigerant composition on MFC performance are investigated and presented. The characteristics of its excellent numerical calculation ability and the user-friendly interface of MATLAB™ and powerful thermo-physical property package of Aspen HYSYS are combined. A genetic algorithm is then invoked to optimise the MFC process globally. After optimisation, the unit power consumption can be reduced to 4.655 kW h/kmol, or 4.366 kW h/kmol on condition that the compressor adiabatic efficiency is 80%, or 85%, respectively. Additionally, to improve the process further, with regards its thermodynamic efficiency, configuration optimisation is conducted for the MFC process and several configurations are established. By analysing heat transfer and thermodynamic performances, the configuration entailing a pre-cooling cycle with three pressure levels, liquefaction, and a sub-cooling cycle with one pressure level is identified as the most efficient and thus optimal: its unit power consumption is 4.205 kW h/kmol. Additionally, the mechanism responsible for the weak performance of the suggested liquefaction cycle configuration lies in the unbalanced distribution of cold energy in the liquefaction temperature range.     

Development of an integrated model for process planning and parameter selection for machining processes

The concept of ‘do it right the first time’ in the machining industry not only expects the best quality products but also at the best possible cost. The cost of machining depends on intelligent process planning and selection of machining parameters such as speed, feed, and depth of cut. The problem of machining parameter selection has received great attention by researchers and many techniques have been developed. A review of these techniques reveals that the selection of the machine and cutting tool is done before the process of cutting parameter selection and process sequencing, and often the selection is based on experience. The current research is an attempt to develop an integrated model (ExIMPro: Expert system based Integrated model for Machining Processes) which finds the sequence of operations with set of machines, tools, and other process parameters to minimise the cost of machining for a cylindrical part. This system consists of existing expert system Machining Parameter SELection (MPSEL) for machine and tool selection and a Microsoft Excel® and Visual Basic® based parameter selection model. The present model focuses on turning and cylindrical grinding operations but other processes can be incorporated with little modification to the software.     

Solving the blocking flow shop scheduling problem with makespan using a modified fruit fly optimisation algorithm

The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies.     

Open shop scheduling problem with a multi-skills resource constraint: a genetic algorithm and an ant colony optimisation approach

The continuous evolution of manufacturing environments leads to a more efficient production process that controls an increasing number of parameters. Production resources usually represent an important constraint in a manufacturing activity, specially talking about the management of human resources and their skills. In order to study the impact of this subject, this paper considers an open shop scheduling problem based on a mechanical production workshop to minimise the total flow time including a multi-skill resource constraint. Then, we count with a number of workers that have a versatility to carry out different tasks, and according to their assignment a schedule is generated. In that way, we have formulated the problem as a linear as and a non-linear mathematical model which applies the classic scheduling constraints, adding some different resources constraints related to personnel staff competences and their availability to execute one task. In addition, we introduce a genetic algorithm and an ant colony optimisation (ACO) method to solve large size problems. Finally, the best method (ACO) has been used to solve a real industrial case that is presented at the end.     

Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach

Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment. In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed. Based on this model, a modern evolutionary algorithm, i.e. the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem. To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance. To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival. Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria. A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

Ant colony optimisation algorithm for distribution-allocation problem in a two-stage supply chain with a fixed transportation charge

In a fixed charge transportation problem, each route is associated with a fixed charge (or a fixed cost) and a transportation cost per unit transported. The presence of the fixed cost makes the problem difficult to solve, thereby requiring the use of heuristic methods. In this paper, an algorithm based on ant colony optimisation is proposed to solve the distribution-allocation problem in a two-stage supply chain with a fixed transportation cost for a route. A numerical study on benchmark problem instances has been carried out. The results obtained for the proposed algorithm have been compared with that for the genetic algorithm-based heuristic currently available in the literature. It is statistically confirmed that the proposed algorithm provides significantly better solutions.     

Performance of an ant colony optimisation algorithm in dynamic job shop scheduling problems

The goal of the current study is to identify appropriate application domains of Ant Colony Optimisation (ACO) in the area of dynamic job shop scheduling problem. The algorithm is tested in a shop floor scenario with three levels of machine utilisations, three different processing time distributions, and three different performance measures for intermediate scheduling problems. The steady-state performances of ACO in terms of mean flow time, mean tardiness, total throughput on different experimental environments are compared with those from dispatching rules including first-in-first-out, shortest processing time, and minimum slack time. Two series of experiments are carried out to identify the best ACO strategy and the best performing dispatching rule. Those two approaches are thereafter compared with different variations of processing times. The experimental results show that ACO outperforms other approaches when the machine utilisation or the variation of processing times is not high.