Flexible Manufacturing Environment Research Articles

Genetic Algorithm for Solving Mobile Robot Scheduling Problem in Flexible Manufacturing Environment

The scheduling genuinely a complex process, aimed at optimizing operational activities in pursuit of one or more objectives by leveraging production data which may include previous schedules. The scheduling problem in Flexible Manufacturing System (FMS) is commonly categorized as Nondeterministic Polynomial (NP)-hard combinatorial optimization problems and it remains as an endure problem to industrial practitioners and researchers. As part of real production scheduling, once one task is finished processing on a machine, transportation equipment such as mobile robot transports the completed task to the next machine. The problem of scheduling mobile robot in FMS pertains to the task allocation process for the robots, considering the transportation costs and the time spent to complete all operations. In recent years, Genetic Algorithm (GA) has been a remarkably effective search algorithm for solving a wide range of scheduling problems in a manner that achieves near-optimal solutions. This paper presents the metaheuristic techniques, specifically genetic algorithm, to address the NP-hard scheduling problem of two identical mobile robots in Job-Shop FMS environment. The algorithm is developed with the aim of finding feasible solutions to the integrated problem by minimizing the amount of time it takes to finish all tasks, commonly referred to as makespan. The performance of GA is evaluated with some numerical experiments which is executed via Matlab software. The scheduling results shows that the developed GA able to obtained the near-optimal solution of minimal makespan and converge within a short period of time.

Surface Quality Control by X-Ray Fluorescence

Elemental analysis by X-ray fluorescence, XRF, is a well-known technique, widely used in laboratories and industrial plants. XRF equipment manufacturers offer accessories and programs to integrate their equipment into automated plants and use it as quality control tools. The relationshipbetween the surface roughness of the samples and the quality of the measurement is studied and its physical principles are known. In this work it is proposed to use an XRF equipment to analyze the surface roughness of the elements produced in chain, and establish a pass/do not pass control of a batch.By integrating the XRF equipment into the chain, and comparing the measurement with a previous measurement considered reference (which can be done with other equipment such as a roughness meter or confocal microscope) it can be determined if the surface roughness of the samples conforms to the established tolerance limits. The advantages of using an XRF equipment are: using equipment that is already in the plant, automating the control process, integrating an XRF into a flexible manufacturing environment and non-contact roughness analysis (surface texture).In this work the authors start from different machined specimens of aluminium alloy perfectly known by spark emission spectrometry to the appropriate dimensions to be measured in an XRF spectrometer. The surface to be evaluated is machined until the desired order of roughness is achieved, its surface finish is determined by optical methods and subsequently measured in the XRF equipment. Different specimens, are produced to determine the changes in the XRF measurements. The intensity of selected elements in each specimen is related to the surface finish and conclusions are drawn.

A matheuristic for flexible job shop scheduling problem with lot-streaming and machine reconfigurations

Multi-variety and small-batch production mode enables manufacturing industries to expeditiously satisfy customers' personalised demands, where a large amount of identical jobs can be split into several sublots, and be processed by reconfigurable machines with multiple machining technics. However, such highly flexible manufacturing environments bring some intractable problems to the production scheduling. Mathematical programming and meta-heuristic methods become less efficient when a scheduling problem contains both discrete and continuous optimisation attributes. Therefore, matheuristic, which combines advantages of the two methodologies, is regarded as a promising solution. This paper investigates a flexible job shop scheduling problem with lot-streaming and machine reconfigurations (FJSP-LSMR) for the total weighted tardiness minimisation. First, a monolithic mixed integer linear programming (MILP) model is established for the FJSP-LSMR. Afterwards, a matheuristic method with a variable neighbourhood search component (MH-VNS) is developed to address the problem. The MH-VNS adopts the classical genetic algorithm (GA) as the framework, and introduces two MILP-based lot-streaming optimisation strategies, LSO1 and LSO2, to improve lot-sizing plans with varying degrees. Four groups of instances are extended from the well-known Fdata benchmark to evaluate the performance of proposed MILP model, LSO1 and LSO2 components, and MH-VNS. Numerical experimental results suggest that LSO1 and LSO2 are efficient in different scenarios, and the proposed MH-VNS can well balance the solution quality and computational costs for reasonably integrating the GA- and MILP-based local search strategies. In addition, a complicated FJSP-LSMR case is abstracted from a real-world shop floor for processing large-sized structural parts to further validate the MH-VNS.

Toward a Plug-and-Work Reconfigurable Cobot

The ongoing trend from mass-produced to mass-customized products with batch sizes as small as a single unit has highlighted the need for highly adaptable robotic systems with lower downtime for maintenance. To address these demands, this article proposes the development of a novel reconfigurable collaborative robot (cobot), which has the potential to open up many new scenarios within the rapidly emerging flexible manufacturing environments. As the technological contribution, we present a complete hard- and software architecture for a quickly reconfigurable EtherCAT-based robot. This novel approach allows to automatically reconstruct the topology of different robot structures, composed of a set of body modules, each of which represents an EtherCAT slave. As the theoretical contribution, we propose a method to obtain in an automatic way the kinematic and dynamic model of the robot and store it in universal robot description format (URDF) as soon as the physical robot is assembled or reconfigured. The method also automatically reshapes a generic optimization-based controller to be instantly used after reconfiguration. While this article focuses on reconfigurable manipulators, the proposed concept can support arbitrary serial kinematic tree-like configurations. We demonstrate the contributions with examples of the following: how the topology of the robot is reconstructed and the URDF model is generated, and a Cartesian task application for a cobot built with the basic modules, demonstrating the quick reconfigurabilty of the system from a 4-degrees-of-freedom (DOF) robot to a 5-DOF robot, in order to satisfy new workspace requirements.

Low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle by multi-objective particle swarm optimization

Reduction of carbon emissions is receiving growing attention in manufacturing industry. In the real-life flexible open-shop manufacturing environments, the workpieces generally need frequent transport between machines so that the limitation of the transport resource is non-negligible. Joint scheduling is a better way to achieve global optimum than sequential scheduling due to the interactive relationship between production and transport. However, owing to the extreme complexity, joint scheduling has received little attention over the past years. In this paper, the low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle (LCJS-FOSCA) is investigated. A mixed-integer programming (MIP) model is formulated with the objectives to minimize the total carbon emission and makespan. Facing the extreme complexity of LCJS-FOSCA, an enhanced multi-objective particle swarm optimization (EMOPSO) is developed. In EMOPSO, several improvement strategies including the initialization method for high-quality solutions, chaotic position updating and mutation for global exploration, and problem-knowledge-based neighborhood search for local exploitation, are proposed. Finally, the following results are obtained through a comprehensive case study: (1) the proposed strategies make significant promotion on the convergence and comprehensive quality, and EMOPSO is verified to be effective for solving LCJS-FOSCA; (2) compared with the two sequential scheduling methods, the makespan obtained by joint scheduling is reduced by approximately 27.7% and 25.5%, and the total carbon emission is reduced by 3.3% and 4.2%; (3) the optimal numbers of AGV in various scales of transport time are provided based on the analysis of optimization results. This work presents a significance on promoting cleaner production of the modern manufacturing industry.

3D printing to facilitate flexible sheet metal forming production

This study presents and discusses the possibility of using 3D printed polymer tools to achieve a flexible metal forming production line. An example is given of the design of a sheet metal part from tool printing to the actual forming and evaluation of the achieved precision of the formed part. The time spent by the engineer/operator during the full process cycle is documented and compared to the time it would take to make the corresponding tool by conventional metal machining. The time savings revealed in the paper are relevant for a low number of parts to be formed in a flexible manufacturing environment, while it is less relevant in mass production where metal forming is conventionally an attractive process. Besides the time invested by the engineer/operator, it is also important to keep the costs of the tools at a minimum in flexible manufacturing. Therefore, the paper also presents a comparison of the costs for printed tools as compared to conventional tools made from metal machining. This analysis considers the cost of raw materials, machine time and electricity consumption. The environmental aspects of metal forming are also considered, with the lubricating oils and greases typically used to improve frictional conditions being replaced by the polymers, which themselves have anti-friction properties. Finally, the recyclability of the tools is significant when they are only used to form a few parts. Different types of polymers and printing strategies are discussed in relation to recyclability and compared to the alternative metal tools.

An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

Traditionally, process planning and scheduling were performed sequentially, where scheduling depended on the result of process planning. Considering their complementarity, the two functions are more tightly integrated to improve the performance of job shop flexible manufacturing environment. This study proposes an adaptive multi-strategy artificial bee colony (AMSABC) algorithm to solve integrated process planning and scheduling (IPPS) problem. In AMSABC, two search strategies with different characteristics are introduced into employed bees and onlooker bees to take on the responsibility of both exploration and exploitation. The selection probability of each search strategy is dynamically adjusted according to previous experiences. To further improve the exploitation performance of the approach, a problem-specific multi-objective local search has been embedded in the proposed algorithm. Furthermore, AMSABC algorithm presents a unique solution representation where a food source is represented by three discrete vectors, and a well-designed decoding scheme is developed. Next, the corresponding neighborhood structure is adopted that it can directly generate feasible solutions in the search space. The proposed algorithm is tested on the well-known benchmark instances and compared with the state-of-the-art algorithms. Through detail analysis of experimental results, AMSABC algorithm is more beneficial in the quality and efficiency of solution.

Scheduling of flexible manufacturing plants with redesign options: A MILP-based decomposition algorithm and case studies

In the last years, the operational research on scheduling problems has been moving away from rigorous optimization approaches into solution strategies being capable of returning practical and fast solutions for large-scale industrial problems. Following this line, this paper proposes a novel MILP-based decomposition procedure for solving scheduling problems arising in flexible manufacturing environments, which generally involve multipurpose units and assembly operations. The solution strategy also considers redesign constraints with the goal of improving the efficiency of the production system, preventing bottlenecks and balancing the equipment utilization. The proposal is validated through the resolution of several instances derived from three real-world case-studies coming from different industrial sectors. The computational results show that the decomposition procedure is capable of generating high quality solutions, sometimes the optimal one, with minimum computational effort for all problem instances considered.

Scheduling of Machines and AGVs Simultaneously in FMS through Hybrid Teaching Learning Based Optimization Algorithm

The most complex problem in FMS is scheduling task, due to this complexity it has created interest among many researchers. Even though FMS scheduling problem was considered earlier, material handling systems like (AGVs) scheduling was not done effectively. As transportation times cannot be neglected in an FMS, a carefully managed and designed material handling system is important in achieving the required integration in flexible manufacturing environment. Hence there is a need for scheduling both the machines and material handling system simultaneously for the successful implementation of an FMS, which makes the scheduling of FMS more complex. Metaheuristic Algorithms are mostly received by the researchers, because of their capability to tackle more complex problems. Hybridization of the metaheuristics may further improve their performance. In the present work a new hybrid metaheuristic Teaching Learning based optimization(HTLBO) is proposed to solve simultaneous scheduling problems.

Autonomous mobile robots in manufacturing: Highway Code development, simulation, and testing

A dynamic and flexible manufacturing environment presents many challenges in the movement of autonomous mobile robots (AMRs), leading to delays due to the complexity of operations while negotiating even a simple route. Therefore, an understanding of rules related to AMR movement is important both from a utility perspective as well as a safety perspective. Our survey from literature and industry has revealed a gap in methodology to test rules related to AMR movement in a factory environment. Testing purely through simulations would not able to capture the nuances of shop floor interactions whereas physical testing alone would be incredibly time-consuming and potentially hazardous. This work presents a new methodology that can make use of observations of AMR behaviour on selected cases on the shop floor and build up the fidelity of those simulations based on observations. This paper presents the development of a Highway Code for AMRs, development of simulation models for an ideal AMR (based on the rules from the Highway Code), and physical testing of real AMR in an industrial environment. Finally, a behavioural comparison of an ideal AMR and a real AMR in five scenarios (taken from the shop floor of an industrial partner) is presented. This work could enable informed decisions regarding the implementation of AMRs through identification of any adverse behaviours which could then be mitigated either through improvements on the AMR or through establishing shop floor protocols that reduce the potential impact of these behaviours.

Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation

This paper proposes a genetic algorithm (GA) to find the pseudo-optimum of integrated process planning and scheduling (IPPS) problems. IPPS is a combinatorial optimization problem of the NP-complete class that aims to solve both process planning and scheduling simultaneously. The complexity of IPPS is very high because it reflects various flexibilities and constraints under flexible manufacturing environments. To cope with it, existing metaheuristics for IPPS have excluded some flexibilities and constraints from consideration or have built a complex structured algorithm. Particularly, GAs have been forced to construct multiple chromosomes to account for various flexibilities, which complicates algorithm procedures and degrades performance. The proposed new integrated chromosome representation makes it possible to incorporate various flexibilities into a single string. This enables the adaptation of a simple and typical GA procedure and previously developed genetic operators. Experiments on a set of benchmark problems showed that the proposed GA improved makespan by an average of 17% against the recently developed metaheuristics for IPPS in much shorter computation times.

Overall equipment effectiveness measurement of TPM manager model machines in flexible manufacturing environment: a case study of automobile sector

Total productive maintenance (TPM) has provided a quantitative metric tool which is known as overall equipment effectiveness (OEE). OEE provides an efficient and effective way of measuring and analysing the efficiency of a single machine cell or an integrated manufacturing system. The objective of this paper is to highlight the OEE measurement in flexible manufacturing systems of a selected automobile manufacturing plant by enhancing the equipment and plant reliability by eliminating all the losses incurred. In this study, a methodology is presented for analysis of OEE of TPM manager model machines in a selected semi-automated cell. In this paper, an approach is developed to identify and address the losses and failures which are responsible for lowering the OEE. The results show that there was a significant improvement in OEE measurement of TPM manager model machines in the semi-automated cell by addressing the six big losses. The findings of the research determine a significant contribution due to OEE measurement and assessment of losses and therefore were quite useful for the organisation to initiate TPM improvement activities to enhance the overall plant efficiency.

Overall equipment effectiveness measurement of TPM manager model machines in flexible manufacturing environment: a case study of automobile sector

Overall equipment effectiveness measurement of TPM manager model machines in flexible manufacturing environment: a case study of automobile sector

A Generic Multi-Layer Architecture Based on ROS-JADE Integration for Autonomous Transport Vehicles.

The design and operation of manufacturing systems is evolving to adapt to different challenges. One of the most important is the reconfiguration of the manufacturing process in response to context changes (e.g., faulty equipment or urgent orders, among others). In this sense, the Autonomous Transport Vehicle (ATV) plays a key role in building more flexible and decentralized manufacturing systems. Nowadays, robotic frameworks (RFs) are used for developing robotic systems such as ATVs, but they focus on the control of the robotic system itself. However, social abilities are required for performing intelligent interaction (peer-to-peer negotiation and decision-making) among the different and heterogeneous Cyber Physical Production Systems (such as machines, transport systems and other equipment present in the factory) to achieve manufacturing reconfiguration. This work contributes a generic multi-layer architecture that integrates a RF with a Multi-Agent System (MAS) to provide social abilities to ATVs. This architecture has been implemented on ROS and JADE, the most widespread RF and MAS framework, respectively. We believe this to be the first work that addresses the intelligent interaction of transportation systems for flexible manufacturing environments in a holistic form.

An integrated approach based on Fuzzy Inference System for scheduling and process planning through multiple objectives

Integrated process planning and scheduling (IPPS) problems are one of the most important flexible planning functions for a job shop manufacturing. In a manufacturing order to produce n jobs (parts) on m machines in a flexible manufacturing environment, an IPPS system intends to generate the process plans for all n parts and the overall job-shop schedule concurrently, with the objective of optimizing a manufacturing objective such as make-span. The optimization of the process planning and scheduling will be applied through an integrated approach based on Fuzzy Inference System (FIS), to provide for flexibilities of the given components and consider the qualitative parameters. The FIS, Constraint Programming (CP) and Simulated Annealing (SA) algorithms are applied in this design. The objectives of the proposed model consist of maximization of processes utility, minimization of make-span and total production costs including costs of flexible tools, machines, process and TADs. The proposed approach indicates that The CP and SA algorithms are able to resolve the IPPS problem with multiple objective functions. The experiments and related results indicate that the CP method outperforms the SA algorithm.

Joint production and transportation scheduling in flexible manufacturing systems

This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.

Flexible Automation Driven by Demonstration: Leveraging Strategies that Simplify Robotics

An important question for production engineers is, How do I automate this task? In traditional industrial automation, e.g., a car factory, robots perform a small set of tasks for long periods of time. Robots are selected because their kinematic structure and strength suit the task requirements, and their motions are preprogrammed by a skilled programmer. In flexible manufacturing environments, tasks can change daily or hourly. The classic approach to automation is less suitable here; buying a dedicated robot for each set of tasks is inefficient, and coding each task is very time-consuming. Furthermore, qualified programmers with the requisite knowledge may not be available. To solve these problems, we propose a framework for modular robots that determines their structure and program based on human demonstrations.

The Design and Evaluation of an Ergonomic Contactless Gesture Control System for Industrial Robots

In industrial human-robot collaboration, variability commonly exists in the operation environment and the components, which induces uncertainty and error that require frequent manual intervention for rectification. Conventional teach pendants can be physically demanding to use and require user training prior to operation. Thus, a more effective control interface is required. In this paper, the design and evaluation of a contactless gesture control system using Leap Motion is described. The design process involves the use of RULA human factor analysis tool. Separately, an exploratory usability test was conducted to compare three usability aspects between the developed gesture control system and an off-the-shelf conventional touchscreen teach pendant. This paper focuses on the user-centred design methodology of the gesture control system. The novelties of this research are the use of human factor analysis tools in the human-centred development process, as well as the gesture control design that enable users to control industrial robot’s motion by its joints and tool centre point position. The system has potential to use as an input device for industrial robot control in a human-robot collaboration scene. The developed gesture control system was targeting applications in system recovery and error correction in flexible manufacturing environment shared between humans and robots. The system allows operators to control an industrial robot without the requirement of significant training.

Developing of Integrated Process Planning System for Flexible Manufacturing Environment

Recently, manufacturing systems are striving for an integrated manufacturing environment. The main aim of this research is to propose methodology and develop a system for generating flexible process plan according to Non-linear process planning approach (NLPP) which is based on a shop floor condition named flexible process plans for all parts before entering the shop floor. Four types of flexibility have been considered which can be related with the part manufacturing. Firstly, sequencing flexibility indicate to the possibility of the sequence interchanging in which required manufacturing operations are performed. Secondly, processing flexibility is refers to the possibility of producing the same manufacturing feature with alternative processes. Thirdly, machine tools flexibility is determined by the possibility of performing an operation on more than one machine and the last is cutting tools flexibility which refers to the possibility of producing the manufacturing feature with alternative cutting tool, the researcher creates mathematical model for each activity of process planning to provide mathematical solution for each one. The developed system basically consists of two modules; firstly, Product design module adopted to define design; secondly process planning module divided into five sub-modules: machining sequence, process selection, machine tools selection, cutting tools selection and machining time sub-modules.

Performance Evaluation of Flexible Manufacturing System Under Different Material Handling Strategies

The current market scenario is such that customers demand a variety of good quality products at a very short notice. The need of manufacturing flexibility in flexible manufacturing system producing these parts is a major challenge in effectively integrating material, information and decision flow in the system. The evolving flexible manufacturing environment requires a judicious combination of manufacturing flexibility, material handling strategies, design, planning and operational strategies, etc. In this paper, a series of simulation experiments are conducted to study the impact of routing flexibility on the make-span and average waiting time of parts in queue as performance measures of flexible manufacturing system under different material handling strategies. The results are further analyzed with the help of ANOVA to measure the effect of input variables on flexible manufacturing system performance. It is seen that for assumed conditions there is an optimal routing flexibility level for a given material handling strategy. This work will help system designers in taking judicious decision in setting manufacturing strategies. The results also show the impact of sequencing and dispatching rules, buffer size and number of parts on the performance of flexible manufacturing system.