An Integrated Intelligent CAD/CAPP Platform: Part II - Operation Sequencing Based on Genetic Algorithm

We present a platform for integrated CAD/CAPP part design based on Elementary Machining Features (EMF) and intelligent approach for setup planning and operation sequencing based on a genetic algorithm through two papers. In this paper, as Part II of this platform, CAD/CAPP integration was realized via information from the enriched EMF, as well as production rules and a genetic algorithm. This is done for the purpose of the automated machining operation sequencing. Operation sequencing was conducted by using the improved genetic algorithm (GA).The improved GA uses integer representation for operations and implements modified genetic operators, enabling the achievement of high results in a reasonable computational time. In the paper we present a comprehensive case study applied to some existing and one new industrial example, confirming a high level of usability of the proposed GA and overall platform. Experimental results show that the improved GA algorithm gives slightly better results than similar algorithms in literature. For industrial example, we use body of the hydraulics cylinder which consists of 52 EMF. After implementation of the proposed methodology, the optimal machining operation sequence was identified, as well as the total machining cost of 142.49 BAM

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Dividing Complex Parts into Multiple Pieces for Advanced Joining and Additive Manufacturing

Poor material utilization is inherent to conventional manufacturing processes, leading to high material waste and machining times. Additive manufacturing processes attempt to solve this issue by allowing production of near-net shapes, but the processes may be too expensive or infeasible. By leveraging both processes in a single part, the waste and cost of manufacturing can be reduced. For example, a complex part could have the main body produced by bar stock and machining, while small protruding features are joined onto the main body by additive manufacturing. This thesis presents a method to divide complex parts into smaller pieces to be built up through advanced joining and additive manufacturing. A beam search algorithm is applied to consider the vast number of manufacturing and joining options and converge on the lowest cost solutions. The algorithm runs by intelligently identifying cutting planes and iteratively applying them to solid geometry to create possible manufacturing alternatives. Each manufacturing alternative is then evaluated based on cost, and the lowest cost alternatives are presented to the designer to aid in determining better manufacturing plans. Application of this method will reduce material waste and machining to offset the added joining costs. This thesis presents the development, implementation, and testing of this approach

Automated decomposition of complex parts for manufacturing with advanced joining processes

Recent advancements in joining operations and additive manufacturing now allow complex metal parts to be built up from raw materials, as opposed to being machined down from solid blocks. This not only opens up the design space, but also allows for much more efficient manufacturing. By decomposing a complex part into simpler subparts, material waste and machining time can be reduced. This can significantly lower the production cost. However, if decomposed into too many pieces, the cost of the additional required processing steps will outweigh these savings. The primary challenge in designing with advanced joining is in deciding how to divide complex parts into subparts to be cost effective and feasible. It can be difficult to intuitively decide how to split up a part, and currently it requires hours of an engineer’s time to make comparisons between different combinations of stock materials and joining operations. The methods presented in this dissertation employ artificial intelligence (AI) search methods to optimize the decomposition of these complex parts and automatically generate manufacturing plans. In addition, since closed-die forging is a common solution for metal aerospace parts, an automated closed-die forging design method is presented. This approach allows engineers to compare traditional and new manufacturing methods to make better manufacturing decisions early in the design phase

Evolutionary approaches to optimisation in rough machining

This thesis concerns the use of Evolutionary Computation to optimise the sequence and selection of tools and machining parameters in rough milling applications. These processes are not automated in current Computer-Aided Manufacturing (CAM) software and this work, undertaken in collaboration with an industrial partner, aims to address this. Related research has mainly approached tool sequence optimisation using only a single tool type, and machining parameter optimisation of a single-tool sequence. In a real world industrial setting, tools with different geometrical profiles are commonly used in combination on rough machining tasks in order to produce components with complex sculptured surfaces. This work introduces a new representation scheme and search operators to support the use of the three most commonly used tool types: end mill, ball nose and toroidal. Using these operators, single-objective metaheuristic algorithms are shown to find near-optimal solutions, while surveying only a small number of tool sequences. For the first time, a multi-objective approach is taken to tool sequence optimisation. The process of ‘multi objectivisation’ is shown to offer two benefits: escaping local optima on deceptive multimodal search spaces and providing a selection of tool sequence alternatives to a machinist. The multi-objective approach is also used to produce a varied set of near-Pareto optimal solutions, offering different trade-offs between total machining time and total tooling costs, simultaneously optimising tool sequences and the cutting speeds of individual tools. A challenge for using computationally expensive CAM software, important for real world machining, is the time cost of evaluations. An asynchronous parallel evolutionary optimisation system is presented that can provide a significant speed up, even in the presence of heterogeneous evaluation times produced by variable length tool sequences. This system uses a distributed network of processors that could be easily and inexpensively implemented on existing commercial hardware, and accessible to even small workshops

Application of modern metaheuristic algorithms in optimization ofprocess planning

Optimizacija tehnoloških procesa pripada grupi kompleksnih problema kod kojih je akcenat stavljen na određivanje redosleda zahvata obrade i optimalnu selekciju varijanti tehnoloških resursa među kojima se izdvajaju mašine alatke, rezni alati i smerovi prilaza reznih alata. Optimizacija se vrši minimiziranjem funkcije cilja koja je formulisana na bazi troškova, odnosno vremena realizacije zahvata obrade delova prizmatičnog i rotacionog oblika. Pored toga, pravila i odnosi prethođenja među tipskim tehnološkim oblicima i zahvatima obrade formiraju tzv. ograničenja prethođenja koja omogućavaju pronalaženje izvodljivih rešenja usklađenih sa tehnološkim zahtevima razmatranih mašinskih delova. Predloženi metaheuristički algoritmi za rešavanje ovog problema su algoritmi vrane, sivog vuka i grbavog kita. Pored teorijske analize ovih metoda izvršena je verifikacija njihovih performansi na šest različitih eksperimentalnih studija.Optimization of process planning belongs to the group of complex problems in which the emphasis is placed on determining the sequence of machining operations and the optimal selection of variants of technological resources such as machines, cutting tools and tool approach directions. Optimization is achieved by minimizing the objective function which is formulated on the basis of cost and time required for performing all the operations for prismatic or rotational parts. In addition, precedence rules and relationships among features and machining operations define so called precedence constraints which aid in finding feasible solutions that are complied with technological requirements of considered mechanical parts. The proposed metaheuristic algorithms for solving this problem are crow search optimization, grey wolf optimizer and whale optimization algorithm. Beside the theoretical analysis of these methods, verification of their performances was done on six different experimental studies

Design of intelligent manufacturing systems by using artificial intelligence

Интензиван развој крајем осамдесетих, током деведесетих година 20. века и посебно после 2000. година, а који је дефинитивно успоставио и нову област истраживања у производном инжењерству под називом интелигентни технолошки системи, указује на позитиван тренд у правцу остваривања нових производних технологија у 21. веку. У времену када је аутоматизација производње готово достигла свој тренутни максимум, технолошка миграција од флексибилних ка интелигентним технолошким системима и задовољавање све већих потреба глобалног тржишта остварује се новим, мултидисциплинарним приступом, базираним на примени напредних биолошки инспирисаних парадигми 21. века. Пројектовање технолошких процеса, терминирање технолошких процеса, као и терминирање транспортних средстава су међу три најважније функције интелигентних технолошких система. Варијантност у погледу технолошких операција обраде дела, као и у погледу алтернативних производних ресурса (машина алатки, алата, оријентација алата), утиче на то да највећи број делова у савременим технолошким системима може имати више алтернативних технолошких процеса обраде. Из тог разлога, одређивање оптималних технолошких процеса обраде делова представља један од најважнијих циљева у овој научној области истраживања. Као и пројектовање технолошких процеса, терминирање флексибилних технолошких процеса са терминирањем транспортних средстава припада класи недетерминистичких полиномних проблема, па је стога фокус истраживања у оквиру предметне докторске дисертације усмерен на развој биолошки инспирисаних техника вештачке интелигенције у оптимизацији функција интелигентних технолошких система, а у циљу повећања њихове производности, ефикасности и економичности...At the beginning of the 21st century, a methodology that provides technological migration from flexible manufacturing systems to intelligent manufacturing systems is definitely based on biologically inspired methods. Process planning, scheduling and scheduling of internal transport systems (mobile robot) belong to most important functions of intelligent manufacturing systems. A variety of manufacturing operations together with a variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) provide that most jobs in the modern manufacturing systems may have a large number of alternative process plans. For that reason, obtaining an optimal process plan according to all alternative manufacturing resources as well as alternative operations has become a very important task in flexible process planning problem research. As process planning, scheduling function also belongs to NP hard (non deterministic polynomial problem) which means that time exponentially increases with the increase of alternative machine tools, tools and TADs. Therefore, development of biologically inspired algorithms for optimization of proposed functions is the main focus of research efforts in this thesis. The doctoral dissertation is related to the implementation of three methodologies for conceptual design of intelligent manufacturing systems: axiomatic design theory is used for conceptual design of material transport which includes transport of raw material, goods and parts; inventive principles of TRIZ methodology are used as effective tool to define, analyze and solve integration problems at the conceptual design phase and multi-agent methodology is used to facilitate integration of manufacturing functions. The doctoral dissertation is related to the development and experimental verification of 6 novel optimization algorithms for process planning: (i) genetic algorithms - GA algorithm (section 6.1), (ii) simulated annealing - SA (section 6.2), (iii) hybrid GA-SA (section 6.3), (iv) modified particle swarm optimization algorithm - mPSO (section 6.4), (v) chaotic particle swarm optimization algorithm - cPSO..