Optimization Methodologies in Intelligent Machining Systems - A Review

13th IFAC Workshop on Intelligent Manufacturing Systems 13th IFAC Workshop on Manufacturing 13th IFAC Workshop on Intelligent Intelligent Manufacturing Systems Systems August 12-14, 2019. Oshawa, Canada August 12-14, 2019. Oshawa, Oshawa, Canada 13th IFAC Workshop on Intelligent Manufacturing Systems Available online at www.sciencedirect.com August 12-14, 2019. Canada 13th IFAC Workshop on Intelligent Manufacturing Systems August 12-14, 2019. Oshawa, Canada August 12-14, 2019. Oshawa, Canada

ScienceDirect

IFAC PapersOnLine 52-10 (2019) 282–287

Optimization Methodologies in Intelligent Machining Systems –– A Review Optimization Optimization Methodologies Methodologies in in Intelligent Intelligent Machining Machining Systems Systems – A A Review Review Optimization Methodologies in Intelligent Machining Systems – A Review Optimization Methodologies in Intelligent Machining Systems – A Review M. M. Imad*, Imad*, A. A. Hosseini*, Hosseini*, H.A. H.A. Kishawy* Kishawy*

M. Imad*, A. Hosseini*, H.A. Kishawy* M. Imad*, A. Hosseini*, H.A. Kishawy* M. Imad*, A. Hosseini*, H.A. Kishawy* *Machining Research Laboratory, Faculty of Engineering and Applied Science, Ontario Tech University, *Machining Research Research Laboratory, Laboratory, Faculty Faculty of of Engineering Engineering and and Applied Applied Science, Science, Ontario Ontario Tech Tech University, University, *Machining Oshawa, Ontario, Canada, (e-mail: [email protected], [email protected], and [email protected]) *Machining Research Laboratory, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, Ontario, Canada, Canada, (e-mail: [email protected], [email protected], and [email protected]) [email protected]) Oshawa, Ontario, (e-mail: [email protected], [email protected], and  *Machining Research Laboratory, Faculty of Engineering and Applied Science, Ontario Tech University, Oshawa, Ontario, Canada, (e-mail: [email protected], [email protected], and [email protected])  Oshawa, Ontario, Canada, (e-mail: [email protected], [email protected], and [email protected])  Abstract: Manufacturing industries are are always pressured by market demands to produce produce low-cost, high Abstract: Manufacturing industries pressured by demands to highAbstract: Manufacturing industries tend are always always pressured and by market market demands to especially produce low-cost, low-cost, highquality products. Market demands to be complex, full of challenges in fast-paced fast-paced Abstract: Manufacturing industries are always pressured by market demands to produce highquality products. products. Market Market demands demands tend tend to to be be complex, complex, and and full full of of challenges challenges especially especiallylow-cost, in quality in fast-paced Abstract: Manufacturing industries are always pressured by market demands to produce low-cost, highindustrial settings. In order to satisfy rising demands, traditional manufacturing techniques are forced to quality products. Market demands tend to be complex, and full of challenges especially in fast-paced industrial settings. settings. In In order order to to satisfy satisfy rising rising demands, demands, traditional traditional manufacturing manufacturing techniques techniques are are forced forced to to industrial quality products. Market demands tend to be complex, and full of challenges especially in fast-paced develop and adapt new practices and methods of planning, control, and parameter optimization. The industrial settings. order to satisfy demands, traditional manufacturing techniques are forcedThe to develop and and adapt Innew new practices andrising methods of planning, planning, control, and parameter parameter optimization. The develop adapt practices and methods of control, and optimization. industrial settings. Innew order to satisfy rising demands, traditional manufacturing techniques are forcedThe to research and and development of optimization methodologies have transformed traditional manufacturing develop adapt practices and methods of planning, control, and parameter optimization. research and development of optimization methodologies have transformed traditional manufacturing research and intelligent development of optimization methodologies have transformed traditional manufacturing develop and adapt new practices and methods of planning, control, and parameter optimization. The methods into manufacturing techniques that are able to manufacture high quality products, at a research and intelligent development of optimization methodologies have transformed high traditional manufacturing methods into manufacturing techniques that able to manufacture quality products, at methods into intelligent manufacturing techniques that are are able to manufacture high quality products, at aa research and development of optimization methodologies have transformed traditional manufacturing lower cost, and a faster production rate. This work’s aim is to review and discuss recent trends and methods intoand intelligent techniques that are able to high quality products, at a lower cost, aa faster production This work’s aim is review and recent trends and lower cost, and fastermanufacturing production inrate. rate. work’s aim isintelligent to manufacture reviewmachining. and discuss discuss recent trends and methods into intelligent manufacturing techniques thatfield are able to manufacture high quality products, at a of optimization optimization techniques the This emerging of The main discussion lower cost, and a faster production rate. This work’s aim is to review and discuss recent trends and methods of techniques in the emerging field of intelligent machining. The main discussion methods of optimization techniques inrate. thetechniques emerging field intelligent machining. Therecent main discussion lower cost, and a faster of production This work’s aimof is to review and discuss trends and includes the application optimization in three areas: the intelligent process planning methods of optimization techniques in the emerging field of intelligent machining. The main discussion includes application of optimization techniques in three areas: the intelligent process planning and includes the the application of optimization infield three the machining. intelligent process planning and methods of intelligent optimization techniques in thetechniques emergingand of areas: intelligent The main discussion scheduling, process control/management, process parameter selection. includes the application of optimization techniques in three areas: the intelligent scheduling, intelligent process control/management, and process parameter selection.process planning and scheduling, intelligent process control/management, and process parameter selection. includes the application of optimization techniques in three areas: the intelligent process planning and scheduling, intelligent process control/management, and process parameter selection. © 2019, IFAC (International Federation ofintelligent Automatic machining, Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: intelligent manufacturing, intelligent process planning, intelligent scheduling, intelligent process control/management, and process parameter selection. Keywords: intelligent intelligent manufacturing, manufacturing, intelligent intelligent machining, machining, intelligent intelligent process process planning, planning, intelligent intelligent Keywords: process parameters optimization, optimization Keywords: intelligentprocess manufacturing, intelligent machining, intelligent process planning, intelligent process management, management, process parameters optimization, optimization process management, process parameters optimization, optimization Keywords: intelligentprocess manufacturing, intelligent machining, intelligent process planning, intelligent process management, parameters optimization, optimization  process management, process parameters optimization, optimization  instance, in a traditional machining operation, the cutting  instance, in aa traditional machining operation, the 1. INTRODUCTION instance, traditional machining the cutting cutting 1.  parametersin and system parameters are operation, normally chosen prior 1. INTRODUCTION INTRODUCTION instance, in a traditional machining operation, the cutting parameters and system parameters are normally chosen prior parameters and systemParameter parameters are operation, normally chosen prior 1. INTRODUCTION instance, in a traditional machining the cutting to the cutting process. selection is usually based on parameters and system parameters are normally chosen prior to the cutting process. Parameter selection is based on 1. INTRODUCTION Traditional manufacturing manufacturing systems are always under market to the cutting process. Parameter selection is usually usually based on parameters and system parameters are normally chosen prior Traditional systems are always under market the operator’s experience, repeated variable attempts (trial Traditional manufacturing systemsproducts are always under market to the cutting process. Parameter selection is usually based on the operator’s experience, repeated variable attempts (trial pressure to deliver high quality at a lower cost, the operator’s experience, repeated variable attempts (trial Traditional systemsproducts are always the cutting Parameter selection is usually on pressure to to manufacturing deliver high high quality quality products at aaunder lowermarket cost, to and error), orprocess. available instructional handbooks. Allbased abovepressure deliver at lower cost, the operator’s experience, repeated variable attempts (trial and error), or available instructional handbooks. All aboveTraditional manufacturing systems areanalways under market while increasing production rates. In ideal manufacturing and error), sources or available instructional handbooks. All abovepressure to deliver high quality products at a lower cost, the operator’s experience, repeated variable attempts (trial while increasing production rates. In an ideal manufacturing mentioned tend to be conservative, which limits the while increasing production rates. products In anset ideal and error), sources or available instructional handbooks. abovetend to be which limits the pressure to deliver high quality at manufacturing a lower cost, mentioned system, each must quality standards sources tend (MRR) to be conservative, conservative, whichAll limits the while production rates. Inthe ideal manufacturing and error), or available instructional All system,increasing each product product must satisfy satisfy theanset set quality standards mentioned material removal rate of thehandbooks. operation, andabovethus system, each product must satisfy the quality standards mentioned sources tend to be conservative, which limits the material removal rate (MRR) of the operation, and thus while increasing production rates. In an ideal manufacturing (Deshayes et al., 2006). This requirement creates an urgent material removal rate (MRR) of the operation, and thus system, each product must satisfy the set quality standards tendin(MRR) toefficiency. be conservative, which even limits the results inremoval asources decrease In operation, addition, with (Deshayes et et al., al., 2006). 2006). This This requirement requirement creates creates an an urgent urgent mentioned (Deshayes material rate of the and thus results in a decrease in efficiency. In addition, even with system, each product must satisfy the set quality standards need to utilize current state of the art engineering results inremoval a decrease in(MRR) efficiency. Instill addition, even with (Deshayes et al., 2006). requirement an urgent material rate of the operation, and thus years of experience, machinists may encounter issues need to to utilize utilize currentThis state of the the creates art engineering engineering need current state of art results a decrease machinists in efficiency. Instill addition, evenissues with of experience, may encounter (Deshayes et which al., 2006). This requirement an urgent years technologies, allows the of years ofin machinists may encounter need to utilize state of the creates art engineering results inexperience, a decrease in efficiency. Instill addition, evenissues with technologies, whichcurrent allows for for the integration integration of optimization optimization that they have not encountered before. Overcoming these technologies, which allows for the integration of optimization years of experience, machinists may still encounter issues that they have not encountered before. Overcoming these need to utilize current state of the art engineering techniques within traditional manufacturing systems. This that they have not encountered before. Overcoming these technologies, which allows for the integration of optimization years of experience, machinists may still encounter issues techniques within traditional manufacturing systems. This challenges can result in long periods of downtime for techniques within traditional manufacturing This challenges that they have not encountered before. Overcoming can result in of for technologies, allows for the integration ofsystems. optimization integration iswhich a key parameter that transfers traditional challenges canoperations. result in long long periods periods of downtime downtimethese for techniques within traditional manufacturing systems. This that they have not encountered before. Overcoming these integration is a key parameter that transfers traditional manufacturing integration is a systems key parameter that transfers traditional challenges canoperations. result in long periods of downtime for manufacturing techniques within traditional manufacturing systems. This manufacturing into intelligent manufacturing manufacturing operations. integration is a systems key parameter that transfers traditional challenges can result in long periods of downtime for manufacturing systems into intelligent intelligent manufacturing manufacturing into manufacturing operations. integration is a systems key at parameter that transfers traditional systems, especially the current industrial stage. manufacturing manufacturing into intelligent manufacturing manufacturing operations. systems, especially at the current industrial stage. Within the context of intelligent manufacturing, the research systems, especially at the current industrial stage. manufacturing systems into intelligent manufacturing Within the context of manufacturing, the Manufacturing systems experienced four industrial Within the context of intelligent intelligent manufacturing, the research research systems, especially at the current industrial stage. Manufacturing systems experienced four industrial spotlight and attention of many researchers has been directed Manufacturing systems experienced four industrial Within the context of intelligent manufacturing, the research systems, especially at first the current revolution industrial stage. and attention of many researchers has been directed revolutionary stages. The industrial started at spotlight spotlight and attention of many researchers has been directed Manufacturing systems experienced four industrial Within the context of intelligent manufacturing, the research revolutionary stages. The first industrial revolution started at towards intelligent machining (Chen et al., 2018). Currently, revolutionary industrial revolution started of at towards th The first spotlight and attention of many(Chen researchers has been directed Manufacturing experienced fourutilization industrial intelligent machining et al., 2018). Currently, the end of thestages. 18systems century, it witnessed the th towards intelligent machining (Chen et al., 2018). Currently, revolutionary stages. The first industrial revolution started at andoperations attention of researchers has been directed century, it it witnessed witnessed the the utilization utilization of of spotlight the end end of of the the 18 18th century, machining aremany being pushed to increase their the towards intelligent machining (Chen et al., 2018). Currently, revolutionary stages. industrial revolution started at machining operations are being pushed to increase their th The first steam powered mechanical systems in industrial like settings. machining operations areknown being pushed to increase their the end of the mechanical 18th century, it witnessed the utilization of towards intelligent machining (Chen et al., 2018). Currently, steam powered mechanical systems in industrial industrial like settings. settings. efficiencies, beyond their limitations. This progress is steam powered systems in like machining operations areknown beinglimitations. pushed toThis increase their the end the mechanical 18 years century, it witnessed the utilization of efficiencies, beyond their progress is Around aaof hundred later, the world experienced the efficiencies, beyond their known limitations. This progress is steam powered systems in industrial like settings. machining operations are being pushed to increase their Around hundred years later, the world experienced the mainly due to the development of analytical models and the Around a hundred years later, the world experienced the efficiencies, beyond their known limitations. This progress is steam powered mechanical systems inworld industrial like settings. mainly the of analytical models and second industrial revolution, which introduced mass mainly due due to to the development development oflimitations. analytical This models and the the Around a hundred years later, the experienced the efficiencies, beyond their known progress is second industrial revolution, which introduced mass development of optimization techniques in machining. second industrial revolution, which introduced mass mainly due to the development of analytical models and the Around aindustrial hundred years later,thethe world experienced of optimization techniques in machining. production/manufacturing and usage ofintroduced electricity, mass astheaa development development of optimization techniques in formulate machining. second revolution, which mainly due to the development of analytical models and the production/manufacturing and the usage of electricity, as Analytical models are able to mathematically production/manufacturing and the which usage ofintroduced electricity, as a Analytical development of optimization techniques in formulate machining.aaa second revolution, mass models are to source ofindustrial energy, instead instead of the the previously used sources. sources. Analytical models areitsable able to mathematically mathematically formulate production/manufacturing and usage of electricity, as a development of optimization techniques in machining. source of energy, of previously used system and describe behaviour using a specific set ofa source of energy, instead and of technologies the previously used sources. Analytical areits to mathematically formulate production/manufacturing the usage of and electricity, as a system and describe behaviour using aa specific set of Development of information controllable system andmodels describe itsable behaviour usingExamples specific setsuch ofa source of energy, instead of the previously used sources. Analytical models are able to mathematically formulate Development of information technologies and controllable parameters within machining systems. of Development of information technologies and controllable system and describe its behaviour using a specific set of source of energy, instead of the previously used sources. parameters within machining systems. Examples of such electronics paved the way fortechnologies birth of theand third industrial parameters within machining systems. Examples of such Development of information controllable and within describe its (Kishawy behaviour a specific set of electronics paved paved the the way way for for birth birth of of the the third third industrial industrial system models can be found in etusing al., Examples 2005, Imani etsuch al., electronics parameters machining systems. of Development of information technologies and controllable models can be found in (Kishawy et al., 2005, Imani et al., revolution during of the end of the 1960s. Currently the can within be found in 2018, (Kishawy et al., and 2005, Imani et al., electronics paved the wayend for of birth the third industrial parameters machining systems. Examples of such revolution during during of the the end of the of1960s. 1960s. Currently the models 2007, Yussefian et al., Astakhov Outeiro, 2005, revolution of the Currently the models can be found (Kishawy et al., and 2005, Imani et al., electronics paved the wayend for of birth thethat third industrial Yussefian et 2018, Astakhov Outeiro, 2005, world is at during its fourth revolution is known as 2007, 2007, Yussefian et al., al.,in 2018, Astakhov Outeiro, 2005, revolution of industrial the the of1960s. can found in1995, (Kishawy et al., and 2005, Imani 2008). et al., world is is at at its its fourth fourth industrial revolution thatCurrently is known knownthe as models Altintaş andbe Budak, Astakhov and Xiao, world industrial revolution that is as 2007, Yussefian et al., 2018, and Outeiro, 2005, revolution during of the end of the 1960s. Currently the Altintaş and Budak, 1995, Astakhov and Xiao, 2008). Industry 4.0, which became possible by the advancements of Altintaş and Budak, 1995, Astakhov and Xiao, 2008). world is at its fourth industrial revolution that is known as 2007, Yussefian et al., 2018, Astakhov and Outeiro, 2005, Industry 4.0, which became possible by the advancements of Analyticaland models integrated with sensor-equipped traditional Industry became possible by systems the that advancements of Analytical Altintaş Budak, 1995,with Astakhov and Xiao, 2008). world is 4.0, at itswhich fourth industrial revolution isallowed known for as models integrated sensor-equipped traditional cyber-physical systems (CPS). These Analytical models integrated with sensor-equipped traditional Industry 4.0, which became possible by systems the advancements of Altintaş and Budak, 1995, Astakhov anddevelopment Xiao, 2008). cyber-physical systems (CPS). These allowed for machining systems form the basis for the of cyber-physical systems (CPS). These systems allowed for Analytical models integrated with sensor-equipped traditional Industry 4.0,sharing, which became by systems the advancements of machining systems form basis for the of information and (CPS). thepossible integration of software systems machining systemssystems. form the the basis formerging the development development of cyber-physical systems These allowed for Analytical models integrated with sensor-equipped traditional information sharing, and the integration of software systems smart machining Similarly, optimization information sharing, and (CPS). the integration of software systems machining systemssystems. form the basis formerging the development of cyber-physical systems These systems allowed for smart machining Similarly, optimization in mechanical components (Liang et al., 2018). smart machining systems. Similarly, merging optimization information sharing, and the integration of software systems machining systems form the basis formerging the development of in components (Liang et techniques withinsystems. traditional machining systems also in mechanical mechanical components (Liang et al., al., 2018). 2018). smart machining Similarly, optimization information sharing, and the integration of software systems techniques within traditional machining systems also techniques within traditional machining systems also in mechanical components (Liang et al., 2018). smart machining systems. Similarly, merging optimization contributes to the formation of intelligent machining systems. techniques traditional machining systems also in mechanical components (Liang al.,integration 2018). to the of intelligent machining systems. These advancements of CPS madeetthe of systems contributes contributes towithin the formation formation intelligent systems. techniques within machining systems also Smart machines, or traditional in a of larger scalemachining smart machining These advancements of CPS made the integration of systems These advancements of CPS made the integration of systems contributes to the formation of intelligent machining systems. Smart machines, or in a larger scale smart machining over the Internet possible. This minimized downtime and Smart machines, or with in asensors larger scale smartapplications, machining These advancements of CPS made the integration of systems contributes to the formation of intelligent machining systems. systems, are armed of various over the Internet possible. This minimized downtime and over the Internet possible. This minimized downtime and Smart machines, or with in asensors larger of scale smartapplications, machining are armed various These advancements of CPS made(Zheng the integration of systems undesirable production losses et al., 2018) by systems, systems, are the armed of various applications, over the Internet possible. This minimized downtime Smart machines, or with in or asensors larger to scale smart machining undesirable production losses (Zheng et 2018) by which allow machine system capture information in undesirable production losses (Zheng et al., al., 2018) and by which systems, are armed with sensors of various applications, allow the machine or system to capture information in over the Internet possible. This minimized downtime and allowing machines to change certain parameters. Traditional which allow the machine or system to capture information undesirable production losses (Zheng et al., 2018) by systems, are armed with sensors of various applications, allowing machines to change certain parameters. Traditional real time. Thethe information collected can be transferred intoin a allowing machines to change certain parameters. Traditional which allow machine or system to capture information time. The information collected can be into a undesirable production losses (Zheng et limitations. al., Traditional 2018) For by real manufacturing systems impose various real time. Thethe information collected can be transferred transferred intoin allowing machines to change certain parameters. which allow machine or system equipped to capture information inaa manufacturing systems impose various limitations. For shared online central system with various manufacturing systems impose various limitations. For real time. The information collected can be transferred into online central equipped with allowing machines to change certain parameters. Traditional shared central system system with various various manufacturing systems impose various limitations. For shared real time.online The information collectedequipped can be transferred into a shared online central system equipped with various manufacturing systems impose various limitations. For 2405-8963 © 2019, IFAC (International Federation of Automatic Control) Elsevier central Ltd. All rights reserved. shared byonline system equipped with various Copyright@ 2019 IFAC 282Hosting Copyright@ 2019 IFAC IFAC 282Control. Peer review under responsibility of International Federation of Automatic Copyright@ 2019 282 Copyright@ 2019 IFAC 282 10.1016/j.ifacol.2019.10.043 Copyright@ 2019 IFAC 282

2019 IFAC IMS August 12-14, 2019. Oshawa, Canada

M. Imad et al. / IFAC PapersOnLine 52-10 (2019) 282–287

algorithms and services that allow for an optimized solution for each specific scenario (Zheng et al., 2018).

283

Neurons are placed in multiple parallel layers: input layers, hidden layers, and output layers. When information flows between the layers it changes the layers structure until a pattern between the inputs and the outputs is modelled (Markopoulos et al., 2008).

The objective of this paper is to briefly summarize recent trends in the utilization of optimization methodologies within traditional machining systems, which contributes to the development of intelligent machining systems. This paper is categorized into three main sections. The first section is an introductory section, the second section include the main discussions, and the last section concludes the work of the paper. The main discussions section is organized into three sections. The first section reviews the latest developments in the usage of optimization techniques in intelligent planning and process management in intelligent machining systems. Intelligent Process planning and process management are defined as modifications to traditional scheduling practices. Such modifications require the usage of optimization techniques, which greatly improves processes. The second section discusses intelligent process control and management. Intelligent Process control is defined as the usage of superior techniques within control systems. This formula, ensures a knowable, balanced, and steady machining operations. Lastly, the third section discusses the trends researchers have been focusing on regarding the manipulation of the process parameters in machining. Process parameters, are categorized into cutting and geometrical parameters. Cutting parameters are the cutting speed, feed rate, depth of cut, etc. Geometrical parameters are the angles of the used cutting tool. Cutting tool angles are rake angle, clearance angle, etc. Optimization techniques are implemented to find a user satisfactory combination between all parameters from both categories.

The second category is the optimization techniques category. The systems input parameters are optimized to achieve satisfactory output parameters. The latter category is divided into conventional and non-conventional techniques. Conventional techniques include the Design of Experiments (DOE) techniques and mathematical iterative search techniques. The non-conventional techniques include the metaheuristic search algorithms and the problem specific heuristic search algorithms. From the optimization techniques category, the most commonly utilized algorithms are Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE). Generally, GA simulates natural evolution and it is based on random solution establishment technique. A solution is established from an evolving population. The evolving population gradually adapt to the problem’s constrains. The evolved population would eventually become an optimum population with an optimum solution (Kaya, 2006). According to Yusup et al. (Yusup et al., 2012) PSO, is a technique that was motivated by natural swarms. The algorithm that makes up the technique, finds an optimum solution from the moving particles that make up the swarm. Commonly, DE is also a population based optimization technique. However, it uses a vectors comparison approach to find an optimum vector solution. The main operators of DE are mutation, crossover and selection, these operators are utilized to find an optimum solution (Yildiz, 2013).

2. OPTIMIZATION TECHNIQUES IN INTELLIGENT MACHINING SYSTEMS

For this review paper, the investigation is mainly focused on research that utilized algorithms that falls under the umbrella of Evolutionary Computation (EC) family. EC, is part of the meta-heuristic search algorithms of the optimization techniques category. However, this does not mean other techniques are excluded from this work. Majority of the reviewed papers in this work, combine their utilization of algorithms from the EC family with other modelling or optimization techniques.

Intelligent planning, intelligent process control, and intelligent management of machining operations is best conducted prior to the execution of a required job. This includes the selection and control of all required parameters for a successful job completion. The required parameters generally include the proper selection of cutting tools, workpiece material, and cutting conditions. According to Mukherjee et al. (Mukherjee and Ray, 2006) intelligent machining problem solving methodologies are categorized into two categories. The first category is referred to as the modelling of process parameters category. This category is responsible for the modelling and modifications of the machining systems. The first category is broken down into regression modelling techniques, Artificial Neural Networks (ANN) modelling techniques fuzzy theory (FT) modelling technique, and a combination of the mentioned modelling techniques. The modelling techniques, are developed and applied to create a relationship between the parameters of the input and the output data. Within the modelling of process parameters categories, ANN is the most commonly utilized approach. ANN are networks that are represented mathematically and designed to solve problems in a similar fashion as the human brain. They consist of neurons that are connected together by synapses (links).

2.1 Intelligent Planning The planning of steps in metal removal operations is also referred to as “scheduling”. Traditionally, scheduling depends on the experience of process planners or schedulers. This approach can be efficient in small-scale custom shops. However, on a mass production scale, this approach is deemed ineffective especially when multiple jobs must be completed during a specific time period (Liang et al., 2018). Therefore, intelligent process planning is gradually being adapted by factories and extensive amounts of research has been done in this area. In their research Zhang et al. (Zhang et al., 2009) investigated the integration of mechanistic and geometrical models in a 283

2019 IFAC IMS 284 August 12-14, 2019. Oshawa, Canada

M. Imad et al. / IFAC PapersOnLine 52-10 (2019) 282–287

five-axis Computer Numerical Control (CNC) machine. Their goal was to predict the cutting forces and feed rate scheduling during machining of free-form surfaces. Their work was then tested under various cutting conditions. The comparison was then made between the constant and variable scheduled feed rates. The performance measure was material removal time. Their results indicated a 35% time reduction when the scheduled variable feed rates were used.

Traditionally, the selection of control parameters is based on inefficient approaches that result in undesirable production losses. In order to face these challenges, researchers invest their resources in the research and development of optimization methodologies. Such algorithms contribute to the development of intelligent process controllers. Optimization algorithms combine the knowledge from experienced operators and experienced engineers to achieve optimal process control settings. In addition, optimization algorithms can be utilized to improve machining processes monitoring. Machining processes require monitoring due to challenges experienced during the execution of these processes. Challenges within machining processes can be presented as excessive tool wear, catastrophic tool failure, finished surface damage, etc. Monitoring such challenges minimizes economical losses and maximize production rates (Kishawy et al., 2018).

Wang et al. (Wang et al., 2015) presented a systematic optimized approach for the planning and scheduling of milling operations on the shop floor. Their approach consisted of two phases: the process phase and the system phase. In the process phase, certain variables of the desired process were optimized without sacrificing the quality of the remaining variables. The researchers optimized the energy consumption without sacrificing the finished surface quality. In the system phase, a decrease in the shop floor’s makespan was achieved. This methodology resulted in an increase in the operation’s efficiency. The concept of an ANN was used to establish a nonlinear relationship between key parameters. Following this process, Simulated Annealing (SA) and GA were utilized to present the optimum machining schedule for energy conservation.

Industrial machining settings tend to be dynamic, which favours application of adaptive machining models within processes control systems. Liu et al. (Liu et al., 2015), focused on complex part machining in dynamic settings. A closed-loop machining process control method in an online system was proposed. The method utilized a memory sharing approach to enable real-time communication between the used software and CNC operating system. The process control method, was designed to provide the possibility of bidirectional information flow and autonomous decision making. Their paper’s main contribution was the representation of a control related information for machining process’ that can operate in online and offline settings. This development of online and offline platforms between the used software and the machine allowed for the implementation of the model. The authors were able to integrate four model parameters during the implementation of the proposed model. The parameters were: the planning parameter, the machining parameter, the process monitoring parameter, and the inspection parameter. Finally, a relationship was formed between the monitoring and inspection of the process parameters and the available interim features.

Generally, scheduling conflicts can be broken down into two main types of problems: flow shop problems and job shop problems. According to Šeda (Šeda, 2007), in flow shop scheduling, jobs share the same execution order of going through machines. Job shop scheduling is more complicated, because jobs can pass through the machines in different orders. Researchers have proposed methods and algorithms to overcome these scheduling problems during the machining operations. Yan et al. (Yan et al., 2016) focused on analysing energy related flow shop scheduling problems by proposing a multi-optimization method. The method examines the possibility of shop floor scheduling optimization that allows for the decrease of the overall makespan and the minimization of energy consumption. This optimization was carried out on two levels. The researchers referred to the first level as the machine tool level where the cutting parameters were optimized based on Grey Relational Analysis (GRA). The second level was referred to as the shop floor level, which dealt with optimizing the schedule of the shop floor. In the shop floor level, a GA optimizer was utilized to optimize the main objectives of the study, which were the minimization of energy consumption and overall makespan simultaneously. Nouiri et al. (Nouiri et al., 2017) proposed the usage of a Two Stage Particle Swarm Optimization (2SPSO) technique that considers the realistic uncertainties of machine breakdown during a flexible job shop scheduling problem. The goal was to provide a stable scheduling approach that is able to reduce the negative impact of a machine’s unavailability on the overall efficiency of the system.

Zuperl et al. (Zuperl et al., 2005) developed an Adaptive Fuzzy (AF) logic controller that was integrated with a CNC controller system. The controller was designed to adapt to variable feed rates while maintaining the required material removal rate. The controller was also developed to limit tool damage and potential tool catastrophic failure. To justify their analysis, the authors experimentally tested their method against a traditional controller that was programmed to operate under constant feed rates in an end-milling operation. The developed AF logic controller had a 27% higher MRR. This concluded that the proposed controller had greater robustness and reliability compared to traditional controllers. Saikumar and Shunmugam (Saikumar and Shunmugam, 2012) proposed an adaptive feed rate control system by integrating multiple requirements for the roughing and finishing of EN24 steel during an end-milling operation. The feed rate control system was designed to be implemented in a shop floor environment. The control system had a three way

2.2 Intelligent Process Control/Management Traditional machining controllers tend to be programmed offline, prior to the start of the machining operation. 284

2019 IFAC IMS August 12-14, 2019. Oshawa, Canada

M. Imad et al. / IFAC PapersOnLine 52-10 (2019) 282–287

configuration setup, a Personal Computer “PC”, LabVIEW platform, and a dynamometer to record the measured cutting forces. The control system was designed to adapt to two different objectives of roughing and finishing operations. The aim of the roughing operation was to maximize the MRR. The finishing operation’s aim to maximize the surface quality. DE algorithm was utilized for identification of the main cutting parameters for both operations. Then an ANN models were used to generate a reference control values for the operations. As a conclusion, it was determined that the proposed control system could be adapted successfully in a shop floor like environment.

285

stress relieve on the spinal nerves. The milling operation in such a setting must be conducted with extra care and precision. Impreciseness can lead to significant patients’ damages that include: nerve injury, infections, and blood clots. Researchers, investigated potential challenges that may occur during the implementation of machining systems, within the medical field. The challenges include: milling process control, milling process motion analysis, and patients’ safety during the milling process. Deng et al. (Deng et al., 2016) presented a fuzzy force controlling strategy based on the main parameters that impact the milling process. The parameters investigated were: the motion of the milling tool, and the structure of the vertebral lamina which is treated as the workpiece in the investigation. The motion of the milling tool was broken down into transverse motion and longitudinal motion. The motions were used to manipulate the feed rate and the depth of cut receptively. After the implementation of the proposed controlling strategy, the milling time was reduced and high loads were avoided.

Huang (Huang, 2016), proposed an intelligent monitoring system during end milling operations. The developed model was a Neural-Fuzzy (NF) model that joint the advantages of neural networks and fuzzy logic. The model was designed to monitor and analyse surface roughness and cutting forces to achieve high precision accuracy in the monitoring of the surface roughness. The developed system was then called INF-SRM, which stands for Intelligent Neural-Fuzzy Surface Roughness Monitoring. The accuracy of the model was then compared to that of neural network model. It was shown that the integrated model yielded better results in terms of surface roughness monitoring. A major part of the characteristics of a CNC milling operation is the requirement for quick optimum reactions for the sensed actions that limit the potential downtime. Traditionally experienced machinists can have slow responses and can be uncertain in certain situations. This can slow the process and even result in an avoidable workpiece damages which leads material and time waste. This lead to the interest of Moreira et al. (Moreira et al., 2019) that challenged the traditional ways of machining, which mainly depends on slow operator’s experience instead of fast response stored machine logic. A real-time supervision controller that is based on the integration of NF, fuzzy logic controllers, and classical control theory approaches was proposed. The developed intelligent supervision controller was designed for real-time responses, which is the required case in majority of industrial applications. This approach is desirable especially, when operating on a complex shaped component that is made of expensive materials and requires a fine surface quality. The purpose of the research was to present a multi-variable controller that is able to predict surface roughness and ensures surface quality improvements in a CNC machining system in real-time. To validate the results, EN24T steel alloy was milled during the comparison of the two controller methodologies. The first methodology was the proposed smart controller. The second methodology a traditional controller, based on the knowledge of experienced operators. The investigation results indicated that the proposed smart controller had a higher operation efficiency.

Huang et al. (Huang et al., 2019) discussed the re-usage of Numerical Control (NC) processes. NC machining process reuse that is designed to be used for complex pockets was proposed. The approach consisted of two levels. The first being for machining features, where the cutting tool and the cutting depth were optimized by ensuring that the tool is being fully utilized. The second level for the actual part, where the cutting order of machined regions were optimized. Effectiveness of the proposed approach was verified using a prototype system based on CATIA’s services. 2.3 Machining Processes Parameters Manipulation The sector of machining parameters optimization is a sector of great interests. Due to the fact that researchers are able to utilize and modify an already established optimization algorithms within machining operations. Also, academic results can straightforwardly be implemented in industrial like settings by the manipulation of the process parameters. Gupta et al. (Gupta et al., 2016) studied the impact of input parameters on the output parameters. Input parameters were: tool approach angle, feed rate, and cutting speed. Output parameters were: tool wear, surface roughness, cutting forces, and cutting temperatures. The workpiece material was titanium (grade-2) alloy the experiments were conducted under a Nano-fluid based minimum quaintly lubrication setting. Analysis of Variance (ANOVA) was used to analyse the statistical impact of the machining parameters. Afterwards, the optimization algorithms of Bacteria Forging Optimization (BFO) and PSO was applied to optimize the machining parameters. Finally, the results of the optimization algorithms were compared against the desirability function method. The findings concluded that during the machining operation cutting speed had the highest statistical impact followed by the feed rate and the cooling conditions. The optimization techniques presented better results compared to the desirability function method. D’Mello et al. (D’Mello et al., 2017) employed two established optimization techniques and introduced a metaheuristic algorithm. Two established

According to Deng et al. (Deng et al., 2016), surgical robotics milling systems presents a great potential in the medical industry. Their work investigated key issues during the milling process of vertebral lamina. The vertebral lamina’s milling is part of the laminectomy surgical operation at which parts of the vertebral lamina are usually removed to create a 285

2019 IFAC IMS 286 August 12-14, 2019. Oshawa, Canada

M. Imad et al. / IFAC PapersOnLine 52-10 (2019) 282–287

optimization techniques were PSO, and Firefly Algorithm (FA). The introduced technique was identified as Bat Algorithm (BA). The goal of the paper was to reduce the surface roughness during the turning of a titanium alloy. The minimization of surface roughness was achieved by the manipulation of the feed rate, cutting speed, depth of cut, and tool flank wear/vibration. Results of the three approaches were compared. The authors concluded that the BA approach concluded better results in comparison to the other two methods. Hegab et al. (Hegab et al., 2018) employed NonDominated Sorting Genetic Algorithm II (NSGA-II) technique with RSM. Their goal was to minimize tool wear, maximize surface quality, and minimize the power consumption during the machining of Ti-6Al-4V alloy. The optimized machining parameters included: cutting speed, feed rate, and the percentage of adduced Nano-additives. The study resulted in finding a feasible solution for the objectives. The optimized values were able to achieve a fair balance between dodging the ploughing effect as much as possible while resulting in a fair heat transfer performance.

3. CONCLUSION AND FUTURE WORK Intelligent machining systems is a major part of intelligent manufacturing, due to its significant impact of the world’s economy. The research and development of optimization techniques contribute to the innovation of traditional machining systems into intelligent machining systems. The usefulness of such techniques falls within the intelligent developed process algorithms. This review paper further discussed the application of optimization techniques in three categories of intelligent machining systems. The categories of intelligent machining systems are planning of intelligent machining systems, the intelligent management and control of these systems, and intelligent machining parameter optimization techniques. Future work will include further information in each of the previously mentioned categories, and a comparison between the referenced works in each category. REFERENCES ALTINTAŞ, Y. & BUDAK, E. 1995. Analytical prediction of stability lobes in milling. CIRP annals, 44, 357362. ASTAKHOV, V. & OUTEIRO, J. 2005. Modeling of the contact stress distribution at the tool-chip interface. Machine Science and Technology, 9, 85-99. ASTAKHOV, V. P. & XIAO, X. 2008. A methodology for practical cutting force evaluation based on the energy spent in the cutting system. Machining Science and Technology, 12, 325-347. CHEN, M., WANG, C., AN, Q. & MING, W. 2018. Tool path strategy and cutting process monitoring in intelligent machining. Frontiers of Mechanical Engineering, 13, 232-242. D’MELLO, G., PAI, P. S. & PUNEET, N. 2017. Optimization studies in high speed turning of Ti6Al-4V. Applied Soft Computing, 51, 105-115. DENG, Z., JIN, H., HU, Y., HE, Y., ZHANG, P., TIAN, W. & ZHANG, J. 2016. Fuzzy force control and state detection in vertebral lamina milling. Mechatronics, 35, 1-10. DESHAYES, L., WELSCH, L., DONMEZ, A., IVESTER, R., GILSINN, D., RHORER, R., WHITENTON, E. & POTRA, F. 2006. Smart machining systems: issues and research trends. Innovation in Life Cycle Engineering and Sustainable Development. Springer. GUPTA, M. K., SOOD, P. & SHARMA, V. S. 2016. Optimization of machining parameters and cutting fluids during nano-fluid based minimum quantity lubrication turning of titanium alloy by using evolutionary techniques. Journal of Cleaner Production, 135, 1276-1288. HEGAB, H., ABDELFATTAH, W., RAHNAMAYAN, S., MOHANY, A. & KISHAWY, H. 2018. Multiobjective optimization during machining Ti-6Al-4V using nano-fluids. HUANG, B., ZHANG, S., HUANG, R., LI, X., ZHANG, Y. & LIANG, J. 2019. An Effective Numerical Control Machining Process Optimization Approach of Part

Radovanović (Radovanović, 2019) conducted a multiobjective optimization study during the turning of AISI 1064 steel by a carbide cutting tool. The work was meant to optimize two machining scenarios during the turning operation. The first scenario was a multi-pass roughing. The second scenario was a single-pass finishing. The optimization objective of the multi-pass roughing was to increase MRR and decrease the machining cost. The cutting speed, feed rate, and depth of cut were optimized to achieve the first scenario’s objective. The optimization of the single-pass finishing operation had the same objective as the multi-pass roughing operation. However, the analysed machining parameters also included the tool nose radius. In order to find an optimized solution for the turning operation, three techniques were compared. The first technique was the iterative search method, where an optimum solution was selected based on the machining parameters combination. The second technique was the Multi-Objective Genetic Algorithm (MOGA) and an optimum solution was selected from the pareto optimal set of solutions. Finally, the author utilized GA to obtain an optimum solution. As a result, the paper concluded that based on comparing the three technique it was found that the iterative search method obtained better results compared to the other two techniques. Xu et al. (Xu et al., 2019) investigated the optimization of feed rates during the end-milling of aluminium alloys in a CNC system. The feed rate values were obtained from the CNC system. The objective of the optimizing the feed rate was to decrease the inconsistency in the spindle power, and increase the efficiency of the end-milling operation. A spindle power prediction model was proposed and experimentally validated.

286

2019 IFAC IMS August 12-14, 2019. Oshawa, Canada

M. Imad et al. / IFAC PapersOnLine 52-10 (2019) 282–287

287

ŠEDA, M. 2007. Mathematical models of flow shop and job shop scheduling problems. World Academy of Science, Engineering and Technology, 1, 122-127. WANG, S., LU, X., LI, X. & LI, W. 2015. A systematic approach of process planning and scheduling optimization for sustainable machining. Journal of Cleaner Production, 87, 914-929. XU, G., CHEN, J., ZHOU, H., YANG, J., HU, P. & DAI, W. 2019. Multi-objective feedrate optimization method of end milling using the internal data of the CNC system. The International Journal of Advanced Manufacturing Technology, 101, 715-731. YAN, J., LI, L., ZHAO, F., ZHANG, F. & ZHAO, Q. 2016. A multi-level optimization approach for energyefficient flexible flow shop scheduling. Journal of Cleaner Production, 137, 1543-1552. YILDIZ, A. R. 2013. A new hybrid differential evolution algorithm for the selection of optimal machining parameters in milling operations. Applied Soft Computing, 13, 1561-1566. YUSSEFIAN, N., HOSSEINI, A., HOSSEINKHANI, K. & KISHAWY, H. 2018. Design for Manufacturing of Variable Microgeometry Cutting Tools. Journal of Manufacturing Science and Engineering, 140, 011014. YUSUP, N., ZAIN, A. M. & HASHIM, S. Z. M. 2012. Overview of PSO for optimizing process parameters of machining. Procedia Engineering, 29, 914-923. ZHANG, L., FENG, J., WANG, Y. & CHEN, M. 2009. Feedrate scheduling strategy for free-form surface machining through an integrated geometric and mechanistic model. The International Journal of Advanced Manufacturing Technology, 40, 11911201. ZHENG, P., SANG, Z., ZHONG, R. Y., LIU, Y., LIU, C., MUBAROK, K., YU, S. & XU, X. 2018. Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13, 137-150. ZUPERL, U., CUS, F. & MILFELNER, M. 2005. Fuzzy control strategy for an adaptive force control in endmilling. Journal of Materials Processing Technology, 164, 1472-1478.

With Complex Pockets for Numerical Control Process Reuse. IEEE Access, 7, 45146-45165. HUANG, P. B. 2016. An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations. Journal of Intelligent Manufacturing, 27, 689-700. IMANI, B. M., EL-MOUNAYRI, H. & HOSSEINI, S. Analytical chip load prediction for rough end mills. 2nd Tehran International Congress on Manufacturing Engineering-TICME2007, 2007. KAYA, N. 2006. Machining fixture locating and clamping position optimization using genetic algorithms. Computers in Industry, 57, 112-120. KISHAWY, H., HEGAB, H., UMER, U. & MOHANY, A. 2018. Application of acoustic emissions in machining processes: analysis and critical review. The International Journal of Advanced Manufacturing Technology, 98, 1391-1407. KISHAWY, H., KANNAN, S. & BALAZINSKI, M. 2005. Analytical modeling of tool wear progression during turning particulate reinforced metal matrix composites. CIRP annals, 54, 55-58. LIANG, S., RAJORA, M., LIU, X., YUE, C., ZOU, P. & WANG, L. 2018. Intelligent Manufacturing Systems: A Review. International Journal of Mechanical Engineering and Robotics Research, 7, 324-330. LIU, C., LI, Y. & SHEN, W. 2015. Dynamic feature modelling for closed-loop machining process control of complex parts. International Journal of Computer Integrated Manufacturing, 28, 753-765. MARKOPOULOS, A. P., MANOLAKOS, D. E. & VAXEVANIDIS, N. M. 2008. Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing, 19, 283-292. MOREIRA, L., LI, W., LU, X. & FITZPATRICK, M. 2019. Supervision controller for real-time surface quality assurance in CNC machining using artificial intelligence. Computers & Industrial Engineering, 127, 158-168. MUKHERJEE, I. & RAY, P. K. 2006. A review of optimization techniques in metal cutting processes. Computers & Industrial Engineering, 50, 15-34. NOUIRI, M., BEKRAR, A., JEMAI, A., TRENTESAUX, D., AMMARI, A. C. & NIAR, S. 2017. Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns. Computers & Industrial Engineering, 112, 595-606. RADOVANOVIĆ, M. 2019. Multi-objective optimization of multi-pass turning AISI 1064 steel. The International Journal of Advanced Manufacturing Technology, 100, 87-100. SAIKUMAR, S. & SHUNMUGAM, M. 2012. Development of a feed rate adaption control system for high-speed rough and finish end-milling of hardened EN24 steel. The International Journal of Advanced Manufacturing Technology, 59, 869-884.

287