2.3 Approaches based on Artificial Intelligence and Soft Computing
2.3.3 Application of Evolutionary Algorithms
Table 2.7. Major Setup planning approaches using artificial neural networks (ANN)
References Type of part Main constraints considered Chen and LeClair [1993, 1994]
Mei et al. [1995]
Chen et al. [1998]
Ming and Mak [2000]
Chang and Angkasith [2001]
Deb and Ghosh [2005]
Amaitik and Kilic [2007]
Prismatic Rotational Prismatic Prismatic Prismatic Rotational Prismatic
TAD, precedence relations, tool capability Datum, tolerance, part geometry
Precedence relations, TAD
Precedence relations, tolerance, fixtures Precedence relations, TAD, tools Precedence relations, TAD, tolerance TAD, tolerance, surface finish, part geometry
The approaches based on neural networks, however, have some shortcomings.
The requirement of huge amount of training and testing data, the presence of the outliers and lack of one well-established standard method of training of neural network poses a challenge for the researchers. Moreover, neural networks provide no explanation of the rationale behind their inference procedure. Their lack of explicitly stated rules and vagueness in knowledge representation leads to a black box nature.
algorithm is applied iteratively to arrive at a near-optimum solution. Evolutionary algorithms are best suited to solve inherently intractable problems called NP-hard problems. NP (Nondeterministic Polynomial-time)-hard problems are combinatorial optimization problems for which no exact solution algorithm is possible that would lead to a simple or rapid solution. Instead, the only way to find an optimal solution is a computationally-intensive, exhaustive analyses in which all possible outcomes are tested. Finding the optimal setup planning and process planning solutions are considered as NP-hard problems.
The first evolutionary-based technique introduced in the literature was the GA.
GA was developed based on the Darwinian principle of the ‘survival of the fittest’
and the natural process of evolution through reproduction. GA is a search strategy ideally suited to parallel computing and most effectively applied to problems in which small change result in nonlinear behaviour in the solution space. GA can search very large solution space using probabilistic transition rules instead of deterministic ones. A solution to a given problem is represented in the form of a string, called ‘chromosome’, consisting of a set of elements, called ‘genes’, that represent the solution variables. GA works with a random population of solutions (chromosomes). The fitness of each chromosome is determined by evaluating it against an objective function. To simulate the natural survival of the fittest process, best chromosomes exchange information (through crossover or mutation operators) to produce offspring chromosomes. The offspring solutions are then evaluated. The process is continued for a large number of generations to obtain a near optimum solution.
Vancza and Markus [1996] proposed a feature based process planning model which can reason using expert’s knowledge over a detailed model of the part, the manufacturing processes and resources. Expert knowledge captured in the form of frames and rules is used for building up a search space of feasible process plans. GA is used to find the optimal process plan based on minimum manufacturing cost by simultaneously considering the feasible process plan alternatives.
Zhang et al. [1997] developed a GA based approach for finding optimal process plan for prismatic parts machined in a conventional job shop. The process planning function for a part is modelled by simultaneously considering the selection of
machining operations, machine tools, cutting tools, and operations sequence.
Fixturing requirement, datum requirement, and good manufacturing practice are considered to determine the precedence relationships between features. The developed GA based approach is able to achieve a near-optimal process plan through specially designed crossover and mutation operators. Flexible criteria are provided for plan evaluation based on minimization of total machining cost.
Dereli and Filiz [1999] developed a process planning system for prismatic parts, called OPPS-PRI (Optimised Process Planning System for Prismatic parts). GA is used for optimising the process plans. The different modules of the system are modelling and feature recogniser module, process planning module, optimization module, design for manufacturing module, and CAPP/CAM interface module. In this work, three GA-based methods are developed for optimizing sequence of operations, tool positions and optimization of cutting parameters. They can be used as stand-alone systems or as integrated optimization modules within OPPS-PRI.
Shunmugam et al. [2000] proposed a GA based methodology to find the optimum process parameters (number of passes, speed, feed, and depth of cut for each pass) in multi-pass face milling operation. Objective set is the minimum total production cost subjected to the constraints of allowable speed and feed, tool wear, dimensional accuracy, surface finish, machine tool capability, etc. Cost of a single pass is the sum of the machining cost, tool cost, tool change cost, and idle tool travel cost. The authors reported that the proposed GA based method yields lower or equal value of total production cost obtained by integer programming and dynamic programming. The developed module can be integrated with CAPP system for selection of optimum process parameters for machining.
For setup planning of rotational parts, Shunmugam et al. [2002] developed a preliminary planning module for operation sequencing at the form feature level.
Feature precedence graph (FPG) is used to represent feature information. A searching strategy that searches through the graph is applied to find the various feasible sequences of machining the features subjected to the constraints of feature precedence and tolerance, locating constraints, accessibility constraints, surface
finish, etc. The machining sequences are further optimised using GA with respect to feature adjacency, datum/reference requirements and manufacturing preferences.
An important contribution by Li et al. [2005] is a web based CAPP optimization module which can optimize the selection of machining resources, setup plans and sequencing of machining operations to achieve optimized process plans for prismatic parts. The system is independent of the operating system, and can be used by geographically distributed manufacturers through Web and Java technology. The Web based system provides a convenient platform for users to view and evaluate a design model effectively. A combined Tabu Search (TS), Simulated Annealing (SA) and GA based approach is used for optimization process. Objective is to minimize total machining cost comprising cost of machines and tools, setup cost, and machine change, tool change and setup change costs. A similar approach is developed by Li et al. [2005] for distributed manufacturing environment. A GA-based methodology for finding optimum process plans in distributed manufacturing environment for geographically dispersed machines and tools is developed. It is capable of performing multi-objective optimization based on minimum production cost or minimum processing time.
Bo et al. [2006] used GA to optimize process route for machining prismatic parts. Ordering (precedence) constraint, clustering constraint (grouping features based on similarities of geometry, tool, and TAD), and minimum machining time are the manufacturing constraints considered. Natural number is adopted in coding strategy, and selection, crossover and mutation operators are executed on the population to search the optimal process route in the global feasible space.
Despite its benefits, GA requires long processing time for a near optimum solution to evolve. In an attempt to reduce processing time and improve the quality of solutions, other evolutionary algorithms have been introduced during the past decades. Particle swarm optimization (PSO) was developed by Kennedy and Eberhart [1995] and PSO is inspired by the social behaviour of a flock of migrating birds trying to reach an unknown destination. In PSO, each solution is a ‘bird’ in the flock and is referred to as a ‘particle’. A particle is analogous to a chromosome in GA. Similar to PSO, another evolutionary method is ant colony optimization (ACO).
ACO is a population based, general search technique which is inspired by the
pheromone trail laying behaviour of real ant colonies for the solution of difficult combinatorial problems. ACO was developed by Dorigo et al. [1996] based on the fact that ants are able to find the shortest route between their nest and a source of food. This is done using pheromone trails, which ants deposit whenever they travel, as a form of indirect communication. Some of the PSO and ACO based setup planning and process planning efforts are discussed hereunder.
Mohemmed et al. [2008] explored the application of PSO to solve shortest path (SP) routing problems. Some important applications of the SP problem include operation sequencing in setup planning in machining, path planning in robotic systems, vehicle routing in transportation systems, and traffic routing in communication networks, etc. The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. It is observed that the performance of the proposed PSO algorithm surpasses those of recently reported genetic algorithm based approaches for this problem.
Guo et al. [2009a] developed an integrated process planning and scheduling (IPPS) methodology using a modified PSO technique. The authors investigated the applications of PSO into the intractable operation sequencing problem in setup planning. Each machining operation is modelled as a particle with information on TAD, machine tool, and cutting tool. Precedence constraints among the features are formed based on fixture interaction, tool interaction, datum interaction, etc. A comparative study among the modified PSO, GA and SA is presented highlighting their different characteristics. A similar methodology is proposed by Guo et al.
[2009b] using PSO for optimization of operation sequences and setup determination for five-axis prismatic parts. Features with different tool access directions can be machined in the same setup with the same datum in five-axis machining. Operation sequencing is modelled as a combinatorial optimization problem and PSO is used as the solution technique.
An optimization technique based on ant colony algorithm was proposed by Vijaykumar et al. [2003] for solving multi-pass turning optimization problems.
Optimum cutting parameters including cutting speed, feed rate, depth of cut and
number of rough cuts are obtained by minimizing the unit production cost. Various constraints considered are parameter bounds, tool life constraint, operating constraints consisting of surface finish, force and power required chip–tool interface temperature constraint, and roughing and finishing parameter relations.
Lv and Zhu [2005] defined process planning as a path searching problem and ACO is used to find the optimal path using the workshop resources with minimum time and cost. In the newly developed ACO, real distances are used to guide the ants in place of local pheromone deposits. The state of a work piece is defined by the states of its features. Each feature can have four states, viz. stock, rough, semi-finish, and finish. Machining process can be defined as a state transition from stock state to product state. Objective function is to search for a path to reach finish state from stock state with minimum time and cost.
Krishna and Rao [2006] used ACO to find the optimum operation sequence in a setup for machining of prismatic parts. A part is described as an assembly of form features with geometric specifications, tolerances and surface finish. Various feasibility constraints such as locating constraint, geometric tolerance, accessibility, datum requirements are considered for operation sequencing in a setup. A precedence cost matrix is generated for any pair of features based on the relative costs corresponding to change of machining parameters, tool change, setup change, and machine change. Table 2.8 shows some setup planning efforts using various evolutionary techniques.
The difficulties associated with using mathematical optimization techniques to large scale engineering problems have contributed to the development of alternative solutions. To overcome these difficulties, researchers have proposed evolutionary- based algorithms for searching near-optimum solutions. Some researchers suggest that multi-objective search and optimization is an area where evolutionary algorithms do better than other optimization methods.
Table 2.8. Major Setup planning approaches using evolutionary algorithms
References Type of part Main constraints considered Vancza and Markus [1996]
Zhang et al. [1997]
Dereli and Filiz [1999]
Shunmugam et al. [2000]
Shunmugam et al. [2002]
Li et al. [2005]
Li et al. [2005]
Bo et al. [2006]
Mohemmed et al. [2008]
Guo et al. [2009]
Guo et al. [2009]
Vijaykumar et al. [2003]
Lv and Zhu [2005]
Krishna and Rao [2006]
Prismatic Prismatic Prismatic Prismatic Rotational
Prismatic Prismatic Prismatic
Both rotational and prismatic Prismatic Prismatic
Rotational Prismatic Prismatic
Feature relations, resources, cost Precedence relations, time, cost
Feature interaction, TAD, part geometry Cutting force, surface finish, tool capacity Tolerance, precedence relations, surface finish
Resources, machining cost Resources, machining cost Precedence relations, optimality
Shortest route
TAD, manufacturing resources, cost TAD, manufacturing resources, time
Process parameters, cost
Workshop resources, time and cost Precedence relations, tolerance