A developing trend within the manufacturing shop-floor domain is the move of manufacturing activities into cloud environments, as scalable, on-demand and pay-per-usage cloud services. This will radically change traditional manufacturing, as borderless, distributed and collaborative manufacturing missions between volatile, best suited groups of partners will impose a multitude of advantages. The evolving Cloud Manufacturing (CM) paradigm will enable this new manufacturing concept, and on-going research has described many of its anticipated core virtues and enabling technologies. However, a major key enabling technology within CM which has not yet been fully addressed is the dynamic and distributed planning, control and execution of scattered and cooperating shop-floor equipment, completing joint manufacturing tasks.
In this paper, the technological perspective for a cloud service-based control approach is described, and how it could be implemented. Existing manufacturing resources, such as soft, hard and capability resources, can be packaged as cloud services, and combined to create different levels of equipment or manufacturing control, ranging from low-level control of single machines or devices (e.g. Robot Control-as-a-Service), up to the execution of high level multi-process manufacturing tasks (e.g. Manufacturing-as-a-Service). A multi-layer control approach, featuring adaptive decision-making for both global and local environmental conditions, is proposed. This is realized through the use of a network of intelligent and distributable decision modules such as event-driven Function Blocks, enabling run-time manufacturing activities to be performed according to actual manufacturing conditions. The control system’s integration to the CM cloud service management functionality is also described.
In the intermittent turning and milling processes, during the entry phase the cutting edges are subjected to high impact loads that can give rise to dynamical and strength issues which in general cause tool life reduction. In this study the effect of geometrical features of the cutting tool on the force generation during the entry phase is investigated. Cutting forces are measured by a stiff dynamometer at a high sampling frequency. In addition, the chip load area is analyzed and related to the measured cutting force. The results show that micro-geometrical features, in particular the protection chamfer, significantly affect the force generation during the entry phase.
Minimizing the non-machining time of CNC machines requires optimal positioning of cutting tools on indexes (stations) of CNC machine turret magazine. This work presents a genetic algorithm with a novel solution representation and genetic operators to find the best possible index positions while tool duplicates and tools life are taken in to account during the process. The tool allocation in a machining process of a crankshaft with 10 cutting operations, on a 45-index magazine, is optimized for the entire life of the tools on the magazine. The tool-indexing time is considerably reduced compared to the current index positions being used in an automotive factory.
Old machine reconditioning projects extend the life length of machines with reduced investments, however they frequently involve complex challenges. Due to the lack of technical documentation and the fact that the machines are running in production, they can require a reverse engineering phase and extremely short commissioning times. Recently, emulation software has become a key tool to create Digital Twins and carry out virtual commissioning of new manufacturing systems, reducing the commissioning time and increasing its final quality. This paper presents an industrial application study in which an emulation model is used to support a reconditioning project and where the benefits gained in the working process are highlighted.
Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration.
In the face of global market challenges, manufacturers place a high priority on the improvement of their production system efficiency to sustain their competitive stance. Flexible Transfer Lines (FTLs) stand out for their adaptability, enabled by cutting-edge Computer Numerical Control (CNC) technology, automated transport, and sophisticated control software, allowing for swift adjustments to changes in product specifications. These systems are identified as essential for industries dependent on mass production, such as the automotive and aerospace sectors, where a significant impact on productivity and cost efficiency is seen due to operational efficiency. This study introduces a heuristic approach for balancing FTLs. The heuristic is characterized by uniquely incorporating a broad spectrum of real-world considerations, including equipment-related, time-related, and operational-related characteristics. Through a detailed numerical example, the practical application and effectiveness of the heuristic are demonstrated, showcasing its capacity to produce a feasible solution.
Production platforms can be considered as the foundation for the design, development, and reconfiguration of a production system. Even though, the product platform domain has been extensively researched and applied widely in practice, this is not the case in the production domain regarding production platforms and reconfigurable production systems. Therefore, this paper reviews the current product and production platform literature to distinguish the production platform co-development research. A systematic literature review has been carried out to explore the concept of production platforms and pinpoint what research gaps that needs to be bridged.
The aim of this paper is to give an overview of the current knowledge and future challenges of augmented reality smart glasses (ARSG) for use by industrial operators. This is accomplished through a survey of the operator perspective of ARSG for industrial application, aiming for faster implementation of ARSG for operators in manufacturing. The survey considers the categories assembly instructions, human factors, design, support, and training from the operator perspective to provide insights for efficient use of ARSG in production. The main findings include a lack of standards in the design of assembly instructions, the field of view of ARSG are limited, and the guidelines for designing instructions focus on presenting context-relevant information and limiting the disturbance of reality. Furthermore, operator task routine is becoming more difficult to achieve and testing has mainly been with non-operator testers and overly simplified tasks. Future challenges identified from the review include: longitudinal user-tests of ARSG, a deeper evaluation of how to distribute the weight of ARSG, further improvement of the sensors and visual recognition to facilitate better interaction, and task complexity is likely to increase.
Robots are becoming more adaptive and aware of their surroundings. This has opened up the research area of tight human-robot collaboration,where humans and robots work directly interconnected rather than in separate cells. The manufacturing industry is in constant need ofdeveloping new products. This means that operators are in constant need of learning new ways of manufacturing. If instructions to operatorsand interaction between operators and robots can be virtualized this has the potential of being more modifiable and available to the operators.Augmented Reality has previously shown to be effective in giving operators instructions in assembly, but there are still knowledge gapsregarding evaluation and general design guidelines. This paper has two aims. Firstly it aims to assess if demonstrators can be used to simulatehuman-robot collaboration. Secondly it aims to assess if Augmented Reality-based interfaces can be used to guide test-persons through apreviously unknown assembly procedure. The long-term goal of the demonstrator is to function as a test-module for how to efficiently instructoperators collaborating with a robot. Pilot-tests have shown that Augmented Reality instructions can give enough information for untrainedworkers to perform simple assembly-tasks where parts of the steps are done with direct collaboration with a robot. Misunderstandings of theinstructions from the test-persons led to multiple errors during assembly so future research is needed in how to efficiently design instructions.
Augmented Reality (AR) has shown its potential in supporting operators in manufacturing. AR-glasses as a platform both in industrial use are emerging markets, thereby making portable and hands-free AR more and more feasible. An important aspect of integrating AR as a support tool for operators is their acceptance of the technology. This paper presents the results of interviewing operators regarding their view on AR technology in their field and observing them working in automotive engine assembly and how they interact with current instructions. The observations and follow-up questions identified three main aspects of the information that the operators looked at: validating screw torque, their current assembly time, and if something went wrong. The interviews showed that a large amount of the operators were positive towards using AR in assembly. This has given an insight in both the current information interaction the operators do and their view on the potential in using AR. Based on these insights we suggest a mock-up design of an AR-interface for engine assembly to serve as a base for future prototype designs.
The finite element (FE) method based modeling of chip formation in machining provides the ability to predict output parameters like cutting forces and chip geometry. One of the important characteristics of chip morphology is chip segmentation. Majority of the literature within chip segmentation show cutting speed (vc) and feed rate (f) as the most influencing input parameters. The role of tool rake angle (α) on chip segmentation is limited and hence, the present study is aimed at understanding it. In addition, stress triaxiality’s importance in damage model employed in FE method in capturing the influence of α on chip morphology transformation is also studied. Furthermore, microstructure characterization of chips was carried out using a scanning electron microscope (SEM) to understand the chip formation process for certain cutting conditions. The results show that the tool α influences chip segmentation phenomena and that the incorporation of a stress triaxiality factor in damage models is required to be able to predict the influence of the α. The variation of chip segmentation frequency with f is predicted qualitatively but the accuracy of prediction needs improvement. © 2017 The Authors.
Using modified Nyquist contours, the dominant poles of the closed loop delay-differential equation for machining systems such as milling are identified. Contours with constant damping ratio of the dominant poles are constructed using this method. These contours are similar in shape to the stability lobes, but move upwards and to the right as the instability parameter increases. Additionally, it is possible to study the movement of the dominant poles to the right-hand side of the complex plane as the system becomes unstable by increasing the depth of cut at a constant spindle speed. The movement of the dominant pole is shown to be towards the right (unstable) and upward (higher vibration frequency) of the complex plane. In some cases, there would be a jump of vibration frequency due to the change of the lobe number. It is also shown that the damping ratio of the structure strongly affects both the vibration frequency and the damping ratio of the dominant poles in the closed loop system. Finally, in two milling experiments with two different spindle speeds and continuously increasing depth of cuts, vibration frequencies are measured and compared to the theoretical predictions. The measurements agree with the theoretical predictions, particularly in the unstable cutting conditions.
Chatter vibrations encountered in machining can degrade surface finish and damage the machining hardware. Since chatter originates from unstable interaction of the machining process and the machining structure, information about vibration parameters of the machining structure should be used to predict combinations of cutting parameters that allow stable machining. While modal test methods, for example those with impact hammers, are widely used to identify structural parameters; the need for sophisticated test equipment is prohibitive in their use. Furthermore, dynamic properties of critical components of a machine tool may change as they get affected by cutting loads, material removal and spindle rotation. Recently few algorithms have been proposed that identify the in-process dynamic parameters by frequency measurements, thus avoiding these problems. In this paper, some of these algorithms are reviewed and their capabilities and limitations in processing am experimental data set are compared and discussed. © 2018 The Authors. Published by Elsevier Ltd.
This study is motivated by a real case study and addresses the material supply problem at assembly lines. The aim of the study is to optimally schedule the delivery of raw material at assembly lines while using the minimum number of vehicles. To cope with the problem an original mixed integer linear programming model has been proposed based on the assumptions and constraints observed in the case study. The validity of the model has been examined by solving several real cases and analysing different scenarios. The results of the study show the efficiency and effectiveness of the model.
Despite the increased use of Simulation based Optimization, the design of facility layout is challenged by high levels of uncertainty associatedwith new production processes. Addressing this issue, this paper aims to understand the conceptual modeling activities of Simulation-basedOptimization for facility layout design in conditions of high uncertainty. Based on three in-depth case studies, the results of this paper showhow characterization criteria of production systems can be used in conceptual modelling to reduce uncertainty. These results may be essentialto support managers and stakeholders during the introduction of new production processes in the design of facility layouts.
The current work presents a decision support system architecture for evaluating the features representing the health status to predict maintenance actions and remaning useful life of component. The evaluation is possible through pattern analysis of past and current measurements of the focused research components. Data mining visualization tools help in creating the most suitable patterns and learning insights from them. Estimations like features split values or measurement frequency of the component is achieved through classification methods in data mining. This paper presents how the quantitative results generated from data mining can be used to support decision making of domain experts.
Industrial robot manufacturers have in recent years developed collaborative robots and these gains more and more interest within the manufacturing industry. Collaborative robots ensure that humans and robots can work together without the robot being dangerous for the human. However, collaborative robots themselves are not enough to achieve collaboration between a human and a robot; collaboration is only possible if a proper communication between the human and the robot can be achieved. The aim of this paper is to identify and categorize technologies that can be used to enable such communication between a human and an industrial robot.
In recent years human-robot collaboration has been an important topic in manufacturing industries. By introducing robots into the same working cell as humans, the advantages of both humans and robots can be utilized. A robot can handle heavy lifting, repetitive and high accuracy tasks while a human can handle tasks that require the flexibility of humans. If a worker is to collaborate with a robot it is important to have an intuitive way of communicating with the robot. Currently, the way of interacting with a robot is through a teaching pendant, where the robot is controlled using buttons or a joystick. However, speech and touch are two communication methods natural to humans, where speech recognition and haptic control technologies can be used to interpret these communication methods. These technologies have been heavily researched in several research areas, including human-robot interaction. However, research of combining these two technologies to achieve a more natural communication in industrial human-robot collaboration is limited. A demonstrator has thus been developed which includes both speech recognition and haptic control technologies to control a collaborative robot from Universal Robots. This demonstrator will function as an experimental platform to further research on how the speech recognition and haptic control can be used in human-robot collaboration. The demonstrator has proven that the two technologies can be integrated with a collaborative industrial robot, where the human and the robot collaborate to assemble a simple car model. The demonstrator has been used in public appearances and a pilot study, which have contributed in further improvements of the demonstrator. Further research will focus on making the communication more intuitive for the human and the demonstrator will be used as the platform for continued research.
When looking in rear view mirrors the Swedish as well as the international production industries can overview several years of progress covering all aspects of production. Production methodologies and machines etc. have changed and evolved, and so has the environment of the shop-floor operator. The demands on the shop-floor operators have grown from simple monotonic tasks with low complexity to pro-active team work requiring flexibility, continuous improvements and a holistic approach. With a base in a study where production and HR-managers at six Swedish manufacturing industries have been interviewed this paper identifies the role of today’s and the future Swedish shop-floor operator. The response to the described role of the future operator is compiled from the ones who will become the future Swedish shop-floor operators – today’s teenagers attending technical high-school. Their views of the environment of the future shop-floor operator are described by accuracy, development, a good working environment and team work. The paper also reveals what the offer should include to make these teenagers say: I want to be a future Swedish shop-floor operator.
Today's operators on factory shop-floors are often not stationed, dealing with a single or few tasks but have increasing responsibilities demanding enhanced skills and knowledge in a production environment where any disturbance must be settled with adequate actions without delay to keep optimum output. To be able to respond to these demands, the operators need dynamic, distributed and adaptive decision support in real-Time, helping them to distinguish decision options and maximizing productivity despite incoming stochastic events. The minimum of time and option for operators to consider appropriate action both during normal production and when facing unexpected or unscheduled events point out the need of adaptive decision support for operators. When initiating this research project the question from the industry partner was the following: In what ways is it possible to support operators in making decisions for optimal productivity? By targeting this problem this paper introduces a novel framework for an adaptive decision-support system enabled by event-driven function blocks and based on decision logics. The proposed decision support systems' ability to adapt to the actual conditions on the shop-floor is validated through a case study, and its capability is compared to the voice message system installed on-site.
The health of a machine tool directly affects its ability to produce components with high precision. Therefore, monitoring and diagnosing early faults can enhance its reliability resulting in an improvement in manufacturing throughput and overall product quality. This paper concerns condition monitoring of the ballscrew drive, a machine tool component that transforms rotary motion of the drive shaft into linear motion of the work table along the guideways. The degradation of the ballscrew drive is often characterized by backlash, which results in imprecise linear motion and, therefore, affects the position of guideways during machining operations. Many physical characteristics of the ballscrew drive, such as required torque, viscous friction, and Coulomb friction, change with the degradation of the ballscrew during its lifetime. The paper proposes a condition monitoring methodology consisting of four main steps: data collection, data preprocessing and feature engineering, model building, and anomaly detection. The machine tool drive system is operated under no-load condition at regular intervals to capture health data using Siemens Analyze MyCondition instrumentation. Subsequently, the data is preprocessed and features are extracted from raw signals using the wavelet transform approach. The unsupervised machine learning technique, principal component analysis, is used to reduce the dimensionality of the dataset and find feature combinations that capture most of the variation in the data. Next, Hotelling’s T2 statistic is computed for each sample on a rolling basis, and anomalous behavior is detected based consistent deviations beyond the moving median of Hotelling’s T2 statistic. The proposed methodology is applied on condition monitoring data from a Swedish automotive manufacturer and the health assessments are validated against backlash measurements obtained from a different conditional monitoring test. This shows that the health status of a ballscrew can be derived directly from its physical characteristics.
Human-Robot Collaboration (HRC) could improve manual labor manufacturing processes by relieving workers of inappropriate operations, such as heavy, repetitive, or manual quality inspections. The literature shows that the greatest challenge for large scale implementation of Human-Robot Collaboration is safety, intuitive interfaces and design methods. We present a comprehensive framework that incorporates the use of virtual simulation for the implementation of Human-Robot Collaboration. The proposed framework defines a development process with five major steps, ending up with a virtual simulation model that can provide a foundation for the physical implementation. The framework has been developed using an automotive industrial case.
Factory layouts are frequently planned and designed in virtual environments, based on the experience of the layout planner. This planning and design process depends on information from several cross-disciplinary activities performed by several functions and experts, e.g., product development, manufacturing process planning, resource descriptions, ergonomics, and safety. Additionally, the layout planner also needs to consider applicable rules and regulations. This experience-based and manual approach to plan and design factory layouts, considering a multitude of inputs and parameters, is a cumbersome iterative process with a high risk of human error and faulty inputs and updates. The general trend in industry is to automate and assist users with their tasks and activities, deriving from concepts such as Industry 4.0 and Industry 5.0. This paper presents and demonstrates how digital support for rules and regulations can assist layout planners in factory layout work. The objective is to support the layout planner in accounting for area/volume reservations required to comply with rules and regulations for workers and equipment in the factory layout. This is a step in a wider initiative to provide enhanced digital support to layout planners, making the layout planning and design process more objective and efficient, and bridge gaps between cross-disciplinary planning and design activities.
Identifying sustainable strategies to develop maintenance performance within the short-termism framework is indeed challenging. It requires reinforcing long-term capabilities while managing short-term requirements. This study explores differently applied time horizons when optimizing the tradeoff between conflicting objectives, in maintenance performance, which are: maximize availability, minimize maintenance costs, and minimize maintenance consequence costs. The study has applied multi-objective optimization on a maintenance performance system dynamics model that contains feedback structures that explains reactive and proactive maintenance behavior on a general level. The quantified results provide insights on how different time frames are conditional to enable more or less proactive maintenance behavior in servicing production.
In order to meet the challenges of future complex systems, manufacturing companies need to better understand how social sustainability affects the operator. One way of studying this is to investigate the possible relationships between operator performance and emotion in an assembly experiment. 60 participants took part in an experiment to investigate the relationships between operator performance and objective and subjective arousal. Results showed a weak relationship between operator performance and objective arousal but no significant relationship was found between performance and subjective arousal. The relationships indicate that further studies on operator emotion could be important to better assembly performance. A tool for doing this might be the Qsensor used in this experiment (measure of objective arousal). More studies are needed to further investigate found relationship and if objective emotion measures can be used to predict performance at assembly workstations.
Robots are widely used in industry due to their efficiency and high performance. Many of them are operating in the manufacturing stage of the production line where the highest percentage of energy is consumed. Therefore, their energy consumption became a major focus for many robots manufacturers and academic research groups. Nevertheless, the optimisation of that consumption is still a challenging task which requires a deep understanding of the robot’s kinematic and dynamic behaviours. This paper proposes an approach to develop an optimisation module using Matlab® to minimise the energy consumptions of the robot’s movement. With the help of Denavit-Hartenberg notation, the approach starts first by solving the inverse kinematics of the robot to find a set of feasible joint configurations required to perform the task, solving the inverse kinematics is usually a challenging step which requires in-depth analyses of the robot. The module then solves the inverse dynamics of the robot to analyse the forces and torques applied on each joint and link in the robot. Furthermore, a calculation for the energy consumption is performed for each configuration. The final step of the process represents the optimisation of the calculated configurations by choosing the one with the lowest power consumption and sends the results to the robot controller. Three case studies are used to evaluate the performance of the module. The experimental results demonstrate the developed module as a successful tool for energy efficient robot path planning. Further analyses for the results have been done by comparing them with the ones from commercial simulation software. The case studies show that the optimisation of the location for the target path could reduce the energy consumption effectively.
Since the beginning of the development of machine vision, researchers have realized its importance in the robotics field, as it provides a useful tool for both the environment detection and decision making during the automation process. At the same time, path planning for robots influences many in the robotics and automation field and it has remained active for both methodology research and system implementation. This research combines machine vision with robot path planning with an aim of programming-free robotic applications. Particularly as a proof of concept, a programming-free robotic sketching prototype is developed as a case study. Within the context, this paper consists of three parts. The first part covers the processing of a facial image taken by a webcam to identify the contours that represent the image; the second part converts these contours to paths for an industrial robot to follow; and the third part controls the robot adaptively for sketching including auto-generation of control codes and self-calibration. The developed prototype is a closed-loop system with networked camera and robot. Intelligent computation is applied to identify the contours of the image with minimum representation of points and with the correct sequence of points for each curve (path); the sequence of the output robot paths represents the near-optimal sequence to preserve the minimum travelling time for the robot. The robot control module can also retrieve the TCP of the robot for off-site monitoring. The ultimate goal of this research is future applications of robot path following, e.g. ad-hoc robotic cutting or welding where the paths can be specified by hand-drawings of an operator on the target workpiece with zero programming for the operator.
Saving and managing virtual models’ provenance information (models’ history) can increase the level of reusability of those models. This paper describes a provenance management system (PMS) that has been developed based on an industrial case study.
The product lifecycle management (PLM) system, as a main data management system, is responsible for receiving virtual models and their related data from Computer-Aided technologies (CAx) and providing this information for the PMS. In this paper, the management of discrete event simulation data with the PLM system will be demonstrated as the first link of provenance data management chain (CAx-PLM-PMS).
This paper presents an innovative approach for the design and analysis of production systems using multi-objective optimization and data mining. The innovation lies on how these two methods using different computational intelligence algorithms can be synergistically integrated and used interactively by production systems designers to support their design decisions. Unlike ordinary optimization approaches for production systems design which several design objectives are linearly combined into a single mathematical function, multi-objective optimization that can generate multiple design alternatives and sort their performances into an efficient frontier can enable the designer to have a more complete picture about how the design decision variables, like number of machines and buffers, can affect the overall performances of the system. Such kind of knowledge that can be gained by plotting the efficient frontier cannot be sought by single-objective based optimizations. Additionally, because of the multiple optimal design alternatives generated, they constitute a dataset that can be fed into some data mining algorithms for extracting the knowledge about the relationships among the design variables and the objectives. This paper addresses the specific challenges posed by the design of discrete production systems for this integrated optimization and data mining approach and then outline a new interactive data mining algorithm developed to meet these challenges, illustrated with a real-world production line design example.
This paper aims to investigate the effect of station demands variations on supermarket location problem (SLP). This problem arises in the real-world assembly line part feeding (PF) context where supermarkets are used as the intermediate storage areas for stations. To this purpose a stochastic SLP model is developed to optimize the total cost of PF in terms of shipment, inventory and installation costs. The computational results over a real case as well as different test instances verify that the station demands variation has an effect on the SLP solutions.
This paper aims to deal with assembly line design from both line balancing and parts feeding (PF) aspects as two-interrelated decision problems while supermarkets are used. These problems arise in the real-world assembly lines (ALs) where decision makers are planning to simultaneously determine the optimal number of stations and the optimal number of supermarkets so that the total installation costs of ALs including line balancing and PF costs are minimized. To this purpose an integrated mathematical model is proposed and its performance is tested through solving a number of benchmark problems and a real case taken from industry.
It is widely known that the assembly line balancing problem (ALBP) is an NP-hard optimization problem. Although different meta-heuristics have been proposed for solving this problem so far, there is no convincing support that what type of algorithms can perform more efficiently than the others. Thus, using some statistical measures, the landscape of the simple ALBP is studied for the first time in the literature. The results indicate a flat landscape for the problem where the local optima are uniformly scattered over the search space. Accordingly, the efficiency of population-based algorithms in addressing the considered problem is statistically validated.
Originated by a real-world case study from the automotive industry, this paper attempts to address the assembly lines balancing problem with human-robot collaboration and heterogeneous operators while optimizing the cycle time. A genetic algorithm (GA) with customized parameters and features is proposed while considering the characteristics of the problem. The computational results show that the developed GA can provide the decision-makers with efficient solutions with heterogeneous humans and robots. Furthermore, the results reveal that the cycle time is highly influenced by order of the operators’ skills, particularly when a fewer number of humans and robots exist at the stations.
The simulation possibilities of Human Industrial Robot Collaboration (HIRC) are limited in commercial software and published research. In order to meet this a demonstrator software has been developed. This paper presents the combination of the quantitative output from the software (measuring operation time and biomechanical load) together with existing optimisation techniques used to design the optimal HIRC workstation. An industrial case is used as an example where the optimal geometric handover position between robot and human is found. From the simulation software metamodels were created in order to represent the investigated workstation. The model was used in a multi-objective optimisation problem and resulted in a trade-off chart between operation time and biomechanical load. The result shows one example of the possibilities to combine the quantitative results from the simulation with optimisation in order to get the best solution to a HIRC design problem. (C) 2016 The Authors. Published by Elsevier B.V.
Holes are made in many industrial parts that need screws, pins or channels for passing fluids. The general method to produce holes in metal cutting is by drilling operations. Indexable insert drills are often used to make short holes at a low cost. However, indexable drills are prone to vibrate under certain circumstances, causing vibrations that affect tool life. Therefore, a good prediction of cutting-forces in drilling is important to get a good description of the cutting process for optimization of tool body and insert design. Reliable simulations of dynamic forces also aid in prediction of chatter vibrations that have significant effects on the quality of the manufactured parts as well as the tool life. In this paper, a mechanistic approach is used to model the cutting-forces. Cutting-force coefficients are identified from measured instantaneous forces in drilling operations. These coefficients are used for simulating torque around drill-axis, axial force and cutting-forces in the plane perpendicular to drill-axis. The forces are modeled separately for peripheral and central insert, which results in a detailed description of the cutting-forces acting on each insert. The forces acting on each insert are estimated by dividing the cutting edges into small segments and the cutting-forces acting on each segment are calculated. The total forces are predicted by summation of the forces acting on each segment. Simulated torque and forces are compared to measured cutting-forces for two different feeds. A good agreement between predicted and experimental results, especially in torque and axial-force, is observed.
This paper proposes a guideline for interpreting frequency content and time history of sound measurements in metal drilling processes. Different dynamic phenomena are reflected in generated sound in cutting processes. The footprint of such phenomena including torsional, lateral regenerative chatter and whirling in sound measurement results are discussed. Different indexable insert drills, at several cutting conditions, are covered. The proposed analysis could be used for studying, online monitoring and controlling of drilling processes. © 2017 The Authors.
The transfer lines are among highly complex automated production systems that manufacture a large volume of identical or similar products with high demand. Recently, the design of environmentally friendly production systems has become the focus of more and more enterprises as one of the sustainable manufacturing strategies. In light of the recent energy-efficient manufacturing trends, this paper investigates the transfer line balancing problem (TLBP), considering both efficiency and energy aspects. The problem arises in the automated transfer lines equipped with dedicated machining centers and automated material handling systems, producing a large volume of specific products. The operations are performed at machines by realizing particular processing requirements, including machining features, inclusions, and exclusions considerations. The objectives to be minimized are the cycle time and the total energy consumption. The latter objective consists of machines’ operating and non-operating energy costs. The problem is first formulated as a mixed-integer linear programming model. Due to the problem’s complexity, an efficient multi-objective optimization algorithm based on the non-dominated sorting genetic algorithm (NSGA-II) is also proposed. The performance of the proposed algorithm is compared with the e-constraint method in terms of the Pareto-front metrics while solving various test problems and a case study. The computational results show the effectiveness of the proposed algorithm in dealing with the TLBP.
In recent years Mixed Reality technology has been widely used to enhance operators in manual assembly operations. This paper introduces a Mixed Reality environment for assembly operations and describes how the process can be supported by virtual sensors. The structure of the environment allows seamless adaption from a fully virtual training scenario, only using virtual assets, to a full production scenario supporting operators in assembling physical products in actual production. The training system which has been developed together with the company Skandia Elevator in Sweden enables the operators to train with much less disturbance to the real production line compared to training using the actual production equipment. In fact, the training can be done only using virtual assets.
Wire harnesses are vital for any modern automotive vehicle. They control the basic functions in a vehicle, for example, windshield wipers and critical functions such as sensors, cameras, and autopilot functions. Thus, the quality of wire harness assembly is highly important. Today, wire harnesses are usually assembled manually, which creates unergonomic and tedious working conditions for operators. Traditional and collaborative industrial robots have been identified as possible solutions to overcome challenges faced by operators in this type of assembly. The international research community has proposed many solutions for automating the assembly of wire harnesses in automotive vehicles but despite these solutions, the industry has not been able to adopt a method to automate this assembly process fully or partially. This paper presents a review of findings on robot-assisted wire harness assembly processes based on a systematic literature review. Specifically, the assembly of wire harnesses in Electric Vehicles (EVs). The state-of-the-art review focuses on solutions to improve unergonomic work situations and ensure the quality of assembly operations. Best practices and reasons for the lack of extensive implementation in automotive final assembly systems are described. Further, the paper presents suggestions based on success stories where the automation of the wire harness assembly in automotive vehicles has been realised by leveraging human-centred automation solutions. Based on the findings, this paper identifies the research for future study. The findings also indicate that there is already technology that can support the automation of wire harness assembly processes in EVs but it is crucial to identify the human aspects and the role of humans in the assembly of wire harness assembly process.
The presented work is toward population-based predictive maintenance of manufacturing equipment with consideration of the automaticselection of signals and processing methods. This paper describes an analysis performed on double ball-bar measurement from a population ofsimilar machine tools. The analysis is performed after aggregation of information from Computerised Maintenance Management System,Supervisory Control and Data Acquisition, NC-code and Condition Monitoring from a time span of 4 years. Economic evaluation is performedwith use of Monte Carlo simulation based on data from real manufacturing setup.
In today's manufacturing environment, safe human-robot collaboration is of paramount importance, to improve efficiency and flexibility. Targeting the safety issue, this paper presents an approach for human-robot collaboration in a shared workplace in close proximity, where real data driven 3D model of a robot and multiple depth images of the workplace are used for monitoring and decision-making to perform a task. The strategy for robot control depends on the current task and the information about the operator's presence and position. A case study of assembly is carried out in a robotic assembly cell with human collaboration. The results show that this approach can be applied in real-world applications such as human-robot collaborative assembly with human operators safeguarded at all time.
Proper maintenance of manufacturing equipment is crucial to ensure productivity and product quality. To improve maintenance decision support, and enable prediction-as-a-service there is a need to provide the context required to differentiate between process and machine degradation. Correlating machine conditions with process and inspection data involves data integration of different types such as condition monitoring, inspection and process data. Moreover, data from a variety of sources can appear in different formats and with different sampling rates. This paper highlights those challenges and presents a semantic framework for data collection, synthesis and knowledge sharing in a Cloud environment for predictive maintenance.
Enhancing data visualization on the shop floor provides support for dealing with the increasing complexity of production and the need for progressing towards emerging goals like energy efficiency. It enables personnel to make informed decisions based on real-time data displayed on user-friendly interfaces. Augmented reality (AR) technology provides a promising solution to this problem by allowing for the visualization of data in a more immersive and interactive way. The aim of this study is to present a framework to visualize live and historic data about energy consumption in AR, using Power BI and Unity, and discuss the applications' capabilities. The study demonstrated that both Power BI and Unity can effectively visualize near-real-time machine data with the aid of appropriate data pipelines. While both applications have their respective strengths and limitations, they can support informed decision-making and proactive measures to improve energy utilization. Additional research is needed to examine the correlation between energy consumption and production dynamics, as well as to assess the user-friendliness of the data presentation for effective decision-making support.
This paper presents a Mixed reality (MR) environment to support industrial cobots programming for welding purposes. Several intuitive menus allow a user to program the path, which can be configured and simulated in the virtual environment with reachability checking. A guide can be activated to show recommended steps and allows validation of the created program on the virtual robot to aid the learning process. The MR application is integrated with a robot programming platform and a robot controller, that allows the user to test paths on a real robot. Moreover, results from spatial accuracy and user experience evaluations are presented.
This paper presents a Mixed Reality (MR) approach to extend the Safe Move tool from ABB Robot Studio (RS) to view and intuitively edit the safety configuration, allowing the import of existing configurations and the export of modified ones to RS for certification by the expert. The added virtual sensor can detect the user's position allowing collision detection and avoidance. The robot's motion is not only adjusted to the safety zone in which the robot is but also to its relative position to the operator by monitoring in real-time positions of the operator’s hands as well as head and triggering appropriate action in the robot.
This paper discusses the expected benefits of using linked data for the tasks of gathering, managing and understanding the data of smart factories. It has the further specific focus of using this data to maintaining a Digital Twin for the purposes of analysis and optimisation of such factories. The proposals are motivated by the use of an industrial example looking at the types of information required, the variation in data which is available and the requirements of an analysis platform to provide parameters for seamless, automated simulation and optimisation.
As factories become increasingly computerised, and with the increasing interest in Cyber-Physical-Systems and the Internet-of-Things, the issues of software management, deployment, configuration and integration are expected to become increasingly important. This paper reports on the ongoing experiences of using the Docker container technology in a major EU research project targeting smart factories. Docker is used to distribute, deploy and manage the configuration of multiple software modules between multiple teams and demonstrator sites in multiple locations, where each module can use its own mixture of protocols, programming languages and platforms.
Augmented reality is currently a hot research topic within manufacturing and a great potential of the technique is seen. In this study, we aim to increase the knowledge of the adaptation and usability of augmented reality for the training of operators. We propose an approach of using dynamic information content that is automatically adjusted to the individual operator and his/her learning progress for increased efficiency and shorter learning times. The approach make use of the concept of expert systems from the field of artificial intelligence for determine the information content on-line. We develop a framework called "Augmented Reality Expert System" (ARES) that combines AR and expert systems. A proof-of-concept evaluation of the framework is presented in the paper and possible future extensions are discussed.
Almost all manufacturing processes are subject to uncontrollable variations, caused, for example, by human operators or worn-out machines. When optimizing real-world product sequencing problems, it is of importance to find solutions that are robust, that is, whose performance remains relatively unchanged when exposed to uncertain conditions. In this paper, an extension of the traditional method of handling variations through replications is suggested that aims at finding solutions with an increased degree of robustness. The basic idea is to use standard deviation as an additional optimization objective and transform the single-objective problem into a multi-objective problem. Using standard deviation as an additional objective aims to focus the optimization on solutions that exhibit both high performance and high robustness (that is, having low standard deviation). In order to optimize the two objectives simultaneously, a multi-objective evolutionary algorithm is utilized. The proposed method for improved robustness is evaluated using a real-world test case found at the company GKN Aerospace in Sweden. GKN Aerospace manufactures a variety of different components for aircraft engines and aero derivative gas turbines. The company has recently installed a new workshop, and the focus of the study is on the x-ray stations in this workshop. For performing optimizations the company has created a simulation model that realistically mimics the workshop. As an optimization technique, a multi-objective evolutionary algorithm called NSGA-2 is being used. The algorithm considers the mean value and standard deviation from replications of the stochastic simulation as objectives, optimizing both of them simultaneously. Results from the study show that the optimization is able to successfully find robust solutions using the proposed method. However, the general increase in algorithm performance expected with the proposed method is absent, and possible reasons for this are discussed in the paper.
With augmented reality, virtual information can be overlaid on the real world in order to enhance a human’s perception of reality. In this study, we aim to deepen the knowledge of augmented reality in the shop-floor context and analyze its role within smart factories of the future. The study evaluates a number of approaches for realizing augmented reality and discusses advantages and disadvantages of different solutions from a shop-floor operator’s perspective. The evaluation is done in collaboration with industrial companies, including Volvo Cars and Volvo GTO amongst others. The study also identifies important future research directions for utilizing the full potential of the technology and successfully implement it on industrial shop-floors.