Papers by Alexander Churchill
Metaheuristic approaches to tool selection optimisation
In this paper we discuss our approach to solving the tool selection problem, specifically applied... more In this paper we discuss our approach to solving the tool selection problem, specifically applied to rough machining. A simulation is used to evaluate tool sequences, which provides accurate values for tool paths and a D model of the final machined part. This allows for a largely unrestricted search using different tool types, making this approach more useful for real world applications than previous attempts at solving the problem. An exhaustive search of every valid tool sequence is executed and shows that assumptions present in related research can prevent the optimal solution from being discovered. Metaheuristic algorithms are used to traverse the search space because of its complex combinatorial properties. Four algorithms are tested - Genetic Algorithm, Stochastic Hill Climbing, Hybrid Genetic Algorithm and Random Restart Stochastic Hill Climbing. Evaluating their performance at coping with two competing demands, finding optimal solutions and keeping the number of potentially ...
A cognitive architecture is presented for modelling some properties of sensorimotor learning in i... more A cognitive architecture is presented for modelling some properties of sensorimotor learning in infants, namely the ability to accumulate adaptations and skills over multiple tasks in a manner which allows recombination and re-use of task specific competences. The control architecture invented consists of a population of compartments (units of neuroevolution) each containing networks capable of controlling a robot with many degrees of free-dom. The nodes of the network undergo internal mutations, and the networks undergo stochastic structural modifications, constrained by a mutational and recombinational grammar. The nodes used consist of dynamical systems such as dynamic movement primitives, continuous time recurrent neural networks and high-level supervised and unsupervised learning algorithms. Edges in the network represent the passing of information from a sending node to a receiving node. The networks in a compartment operate in parallel and encode a space of possible subsumption-like architectures that are used to successfully evolve a variety of behaviours for a NAO H25 humanoid robot.
In this paper a new, evolutionary multi-objective approach is introduced to tool sequence optimiz... more In this paper a new, evolutionary multi-objective approach is introduced to tool sequence optimization in rough milling. Previous research has focused on the optimization of either the tool sequence or associated cutting parameters. Here, the tool sequence and a machining parameter, the cutting speeds of the individual tools, are simultaneously optimized, producing a Pareto front with both discrete and continuous properties. This is the first time that a multiple-tool multi-objective approach has been taken to tool selection, offering a set of solutions to the process planner. Three objectives are considered, thickness of excess stock, machining time and tooling costs. Unconstrained NSGA-II is used as the base algorithm but several preferential search strategies are tested to attempt to deal with constraints and guide search towards the Pareto optimal front. These include the established reference point (R-NSGA-ii) and weighted objective (WO) methods, as well as two novel techniques -"Guided Elitism" (GE) and "Precedential Objective Order Ranking" (PR). While WO performs best on average when assessed using the hypervolume indicator, the algorithms behave differently in terms of the quality and diversity of solutions found. A hybrid method using GE for exploration and PR for exploitation is shown to outperform the other techniques across all performance measures.
In this paper we discuss our approach to solving the tool selection problem, specifically applied... more In this paper we discuss our approach to solving the tool selection problem, specifically applied to rough machining. A simulation is used to evaluate tool sequences, which provides accurate values for tool paths and a 3D model of the final machined part. This allows for a largely unrestricted search using different tool types, making this approach more useful for real world applications than previous attempts at solving the problem. An exhaustive search of every valid tool sequence is executed and shows that assumptions present in related research can prevent the optimal solution from being discovered. Metaheuristic algorithms are used to traverse the search space because of its complex combinatorial properties. Four algorithms are tested -Genetic Algorithm, Stochastic Hill Climbing, Hybrid Genetic Algorithm and Random Restart Stochastic Hill Climbing. Evaluating their performance at coping with two competing demands, finding optimal solutions and keeping the number of potentially expensive evaluations low, it is shown that RRSHC performs best in terms of solution accuracy but at the greatest computational cost. SHC finds the optimum sequence less frequently but needs far fewer evaluations and the HGA lies somewhere in between, making it a good choice if the problem domain is not well-specified.
Tool selection for roughing components is a complex problem. Attempts to automate the process are... more Tool selection for roughing components is a complex problem. Attempts to automate the process are further complicated by computationally expensive evaluations. In previous work we assessed the performance of several single-objective metaheuristic algorithms on the tool selection problem in rough machining and found them to successfully return optimal solutions using a low number of evaluations, on simple components. However, experimenting on a more complex component proved less effective. Here we show how search success can be improved by multi-objectivizing the problem through constraint relaxation. Operating under strict evaluation budgets, a multiobjective algorithm (NSGA-II) is shown to perform better than single-objective techniques. Further improvements are gained by the use of guided search. A novel method for guidance, "Guided Elitism", is introduced and compared to the Reference Point method. In addition, we also present a modified version of NSGA-II that promotes more diversity and better performance with small population sizes.
Selecting the sequence of tools to use for the rough machining of components is an important task... more Selecting the sequence of tools to use for the rough machining of components is an important task in manufacturing, which greatly affects the overall machining time and cost of the process. In this paper a multi-objective approach is presented, which supports the use of tools with different geometrical properties and offers the process planner a set of Pareto optimal solutions. An industrial simulator is employed, which allows important information to be captured in the model but has the disadvantage of being computationally expensive. A master/slave approach to parallelization is implemented, which can be used on existing grid or cloud computing infrastructures. Synchronous generational and asynchronous steady-state multi-objective algorithms are compared on their search performance and runtimes on two components. Particular attention is paid to potential problems faced by asynchronous search caused by heterogeneous evaluation times due to characteristics present in individual tool sequences. Results show that the algorithms achieve a similar search performance, with the synchronous algorithm occasionally finding a slightly more diverse spread of solutions. However, the asynchronous algorithm is considerably faster, and provides good solutions in a short runtime that means this approach could be easily and inexpensively implemented in an industrial setting.
The accumulation of adaptations in an open-ended manner during lifetime learning is a holy grail ... more The accumulation of adaptations in an open-ended manner during lifetime learning is a holy grail in reinforcement learning, intrinsic motivation, artificial curiosity, and developmental robotics. We present a design for a cognitive architecture that is capable of specifying an unlimited range of behaviors. We then give examples of how it can stochastically explore an interesting space of adjacent possible behaviors. There are two main novelties; the first is a proper definition of the fitness of self-generated games such that interesting games are expected to evolve. The second is a modular and evolvable behavior language that has systematicity, productivity, and compositionality, i.e. it is a physical symbol system. A part of the architecture has already been implemented on a humanoid robot.
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Papers by Alexander Churchill