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As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In legacy studies, a machine conducting a single continuous operation has been mainly observed for power estimation. However, the production machine of a modularized line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of this kind of production line, it is important to interpret mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline—from data collection (from different sources) to preprocessing, data conversion, synchronization, and deep learning classification—to estimate the total power use of the future process plan, is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building an power estimation model without manual data preprocessing. The proposed system is applied to a modular factory, connected with machine controllers, using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline, with the result of the power profile being synchronized with the robot program.

As Additive manufacturing (AM) technologies and connected machines are ever more widespread across most industries, there is a need for a better understanding about the standardization activities related to information technology (IT) within the scope of AM. The purpose of this paper is to provide an overview of existing standardization activities related to IT aspects of AM, including but not limited to data transfer and exchange. The paper covers the IT standardization activities in organizations, including ISO TC261, ASTM International, ISO TC 184, ISO/IEC JTC 1, Web3D Consortium, IEC committees, IEEE committees, 3MF Consortium and Khronos, which have the most active and ongoing projects. From this study, it can be observed that the manufacturing sector has generated the majority of standardization work, followed by industry R&D areas and the medical field. This paper also discusses two future IT standards for AM service platforms and for the medical industry.

Automatic robot gripper system which involves the automated object recognition of work-in-process in production line is the key technology of the upcoming manufacturing facility achieving Industry 4.0. Automatic robot gripper enables the manufacturing system to be autonomous, self-recognized, and adaptable by using artificial intelligence of robot programming dealing with arbitrary shapes of work-in-processes. This paper specifically explores the chain of key technologies, such as 3D object recognition with CAD and point cloud data, reinforcement learning of robot arm, and customized 3D printed gripper, in order to enhance the intelligence of the robot controller system. And it also proposes the integration with 3D point cloud based object recognition and game-engine based reinforcement learning. The result of the prototype of the intelligent robot gripping system developed by the proposed method with a 4 degree-of-freedom robot arm is explained in this paper.

Flexibility in mass-customized manufacturing can be supported significantly by the introduction of Cyber-Physical Production System and the connection of production modules to AI (artificial intelligence) Cloud services. Even though there exist standardized protocols from device to IT system, there are still challenges for the synchronization between cyber-model and physical object, and the application of decision making in the cyber-model. Although high performance machine learning services make the Cloud a preferred computation node, possible unstable connection with manufacturing resources enforce new service distribution approaches in the network. This paper proposes an Edge Computing architecture which is the mediator between machines, by providing local Cloud services with fast response time and preprocessing resources for a vast amount of data. As an illustrative example the selected Edge service pre-processes data form an augmented reality device in order to communicate with the cyber-model in real time. The Edge platform controls the computing resources and prioritizes all processes of Edge Services for a dynamic update of production lines and human-machine-interaction.

As the legislative pressure to reduce energy consumption is increasing, data analysis of power consumption is critical in the production planning of manufacturing facilities. In previous studies, a machine, conducting a single continuous operation, has been mainly observed for power estimation. However, a modularized production line, which conducts complex discrete operations, is more like the actual factory system than an identical simple machine. During the information collection of such production lines, it is important to interpret and distinguish mixed signals from multiple machines to ensure that there is no reduction in the information quality due to noise and signal fusion and discrete events. A data pipeline from data collection from different sources to pre-processing, data conversion, synchronization, and deep learning classification to estimate the total power use of the future process plan is proposed herein. The pipeline also establishes an auto-labeled data set of individual operations that contributes to building power estimation models without manual data pre-processing. The proposed system is applied to a modular factory connected with machine controllers using standardized protocols individually and linked to a centralized power monitoring system. Specifically, a robot arm cell was investigated to evaluate the pipeline with the result of the power profile synchronized with the robot program.

The Digital Age is becoming the de-facto of future direction in manufacturing domain. Its main driver is the pressure of shorter product life cycles and leads companies to faster engineering time of their production lines. To cope with the need, recent literature has successfully shown modular production and assembly line concepts by using standardized interfaces on electromechanical and communication level. This change entails new challenges. Given the fact that production life cycle decreases, the usage of tools for digital manufacturing and simulation grow in importance to support and evaluate the impact of increasing engineering and decision-making processes. The concurrent combination of the physical world with its digital counterpart will be crucial. This paper proposes how engineers set up the digital environment automatically by using a common data model inspired by the Digital Twin concept. Its instantiation for several assembly modules based on AutomationML consists of a set of engineering data and relevant models to support a proof-of-concept of Plug-and-Simulate within a modular, multi-vendor assembly line.

In today’s business environment, the efficiency of warehouses can be critical for the efficiency of the overall supply chains they belong to. As a result, new technologies are being tested and adopted in industry to improve the performance of warehouse operations. An example technology that has recently gained interest by both academia and industry is augmented reality. In this paper, we investigate the opportunities arising from the usage of augmented reality in warehouses as well as the barriers for its industrial adoption. This is done via a series of practitioners interviews and via an experiment designed using Google Glass. Our results indicate that even though the technology is not mature enough at the moment, the potential benefits it can offer make it promising for the near future.

Due to concerns about energy use in production systems, energy-efficient processes have received much interest from the automotive industry recently. Remote laser welding is an innovative assembly process, but has a critical issue with the energy consumption. Robot companies provide only the average energy use in the technical specification, but process parameters such as robot movement, laser use, and welding path also affect the energy use. Existing literature focuses on measuring energy in standardized conditions in which the welding process is most frequently operated or on modularizing unified blocks in which energy can be estimated using simple calculations. In this paper, the authors propose an integrated approach considering both process variation and machine specification and multiple methods&rsquo, comparison. A deep learning approach is used for building the neural network integrated with the effects of process parameters and machine specification. The training dataset used is experimental data measured from a remote laser welding robot producing a car back door assembly. The proposed estimation model is compared with a linear regression approach and shows higher accuracy than other methods.

This paper presents a production scheduling system that optimizes operations of a modular factory. The proposed system consists of a database, a scheduling optimizer and an interface connecting two other components. A scheduling model for optimal schedules plays a key role in the scheduling system. In this research, we aim to develop the scheduling model using constraint programming, and design a data mart storing the related operational data. In particular, we set the modular factory environment to the hybrid flow shop and apply the constraint programming for scheduling. We provide a case study to test the performance of the proposed scheduling model with the synthesized dataset based on a modular factory environment in SmartFactoryKL.

MES (Manufacturing Execution System) puts plans given by enterprise system such as ERP(Enterprise Resource Planning) into practice in the shop floor. As contemporary companies are faced with challenge represented by customer-centric market environment and intense competitions, it becomes increasingly important for companies to cope with dynamic customers’ demand and shop floor situation rapidly. Even though current MESs gets some improvement on collecting raw data, they fall short of expectation on analyzing data and takes action by cooperating various manufacturing management functions. Furthermore, even though many researches treat data collection, data analysis based on shop floor situation, little number of previous researches deals with collaboration among various functionalities provided by MES. In this paper, we introduce the design of advanced MES (namely Smart MES) for helping MES collect, analyze, take measure by collaborating various functions. This paper consists of two parts. In the f...

The Smart Factory is an important topic worldwide as a means for achieving Industry 4.0 in the manufacturing domain. Contemporary research on the Smart Factory has been concerned with application of the so-called Internet of Things (IoT) to the shop floor. However, IoT in this context is often restricted to solving local problems such as managing product information, collaborative information exchange, and increasing productivity. To take full advantage of the potential of the IoT in manufacturing systems, it is necessary that the information service perspective should receive keen attention. This paper proposes a reference architecture for the information service bus or middleware for the Smart Factory that can be used for information acquisition, analysis, and application for the various stakeholders at the levels of Machine, Factory, and Enterprise Resource Planning. To reflect the real voice of the industry, real industrial problems have been identified, transformed into requirements, and incorporated into the information architecture; i.e., Smart Factory Information Service Bus. The implementation process of the reference architecture is also presented and illustrated via case studies.

Remote laser welding which has benefits for both productivity and energy saving is receiving increased attention for automotive assembly lines. Introducing this innovative equipment requires a redesign process for assembly elements, for example to substitute resist spot welding with remote laser welding. The mating surfaces related to the welding need to be updated as well, but this is complicated when the shape design includes manufacturing and assembly elements based on current practice which hide the design intent. The challenge is to establish guidelines with the design parameters of automotive door frames and the process parameters of the joining process in order to maintain the design intent. In order to maintain the original design intent of the process planner, the author proposes a propagation model for the production stage which is defined as the interplay between parameters and quantitative comparison. The process parameters of remote laser welding are applied to the flange of the door frame. Also, the shape of each flange is synchronised with the changes according to the result of a quantitative comparison of the productivity and eco-efficiency. A design guideline tool to redesign the flanges was developed inside a commercial CAD system to show the proposed concept. Five flanges of a car door were subjected to redesign for introducing 4-kw remote laser welding with considerations of eco-efficiency, process time and weight change. The tools are shown to illustrate how to maintain design intent in the redesign process, and the concept was realised in a commercial CAD system.

Due to concerns about environmental protection, product life cycle management for end-of-life has received increasing attention in many industrial sectors. To support these functions, crucial issues have been studied to realize a product recovery management system. Until the present time most research has been concerned with decision making under the assumption that all the relevant and accurate information about a product is available by some means. However, these pieces of research ended in technological attempts because of the development complexity of implementation using ubiquitous computing devices such as identification chips and embedded systems to get data from products. An efficient development method is necessary in order to overcome this limitation. In this paper we overview a generic architecture based on ubiquitous computing technology. This is followed by how to develop such an innovative system by proposing a systematic approach called ubiquitous information engineering. To show the effectiveness of the architecture and approach a prototype for remanufacturing an industrial product has been developed. Through development of the proposed approach enough functions can be derived to collect information from a product. The study shows that major factors influencing development complexity are found and that information standards support network development in end-of-life activities.

The issues in the energy-efficiency process have much interest in the automotive industry. The energy criteria are also important in machine selection as well as productivity when new equipment is introduced on a shop floor. Remote laser welding having benefits for productivity and for energy saving is receiving attention in automotive assembly lines, but introducing this innovative equipment is a significant decision because of high initial cost in spite of the advantages. This paper specifically provides an estimation model of the total energy which is consumed by a robot arm, laser source, and cooling system. It considers the energy determined by robot operation parameters, arm path, and welding parts instead of the one for the laser melting phenomenon. Operational parameters and kinematic models are adjusted by comparison with experimental data of a car door assembly process. By developing an estimation model the major factors and devices determining consumed energy were found. This model will contribute to finding the effective process which will be improved by applying remote laser welding robot to legacy welding processes. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Professor Pedro Filipe do Carmo Cunha

A multi-channel complex machine tool (MCCM) is a versatile machining system equipped with more than two spindles and turrets for both turning and milling operations. Despite the potential of such a tool, the value of the hardware is largely dependent on how the machine tools are effectively programmed for machining. In this paper we consider a shop-floor programming system based on ISO 14649 (called e-CAM), the international standard for the interface between computer-aided manufacture (CAM) and computer numerical control (CNC). To be deployed in practical industrial usage a great deal of research has to be carried out. In this paper we present: 1) Design consideration for an e-CAM system, 2) The architecture design of e-CAM, 3) Major algorithms to fulfill the modules defined in the architecture, and 4) Implementation details.

Due to concerns about environmental protection and resource utilization, product lifecycle management for end-of-life (EOL) has received increasing attention in many industrial sectors including manufacturing, maintenance/repair, and recycling/refurbishing of the product. To support these functions, crucial issues are studied to realize a product recovery management system (PRMS), including: (1) an architecture design for EOL services, such as remanufacturing and recycling; (2) a product data model required for EOL activity based on international standards; and (3) an infrastructure for information acquisition and mapping to product lifecycle information. The presented works are illustrated via a realistic scenario.

There has been significant achievement in integrating product data during the whole lifecycle phases with shared common ontologies while taking advantage of intelligent retrieval mechanisms. In order to support integrated decision making on product redesign or maintenance operations, we should solve a challenging issue: ‘how the product lifecycle management (PLM) stores and retrieves the know-how and the knowledge of an organization concerning manufactured products’. This paper describes the extension of a previously developed PLM Semantic Ontology Model toward integration with design intent. The proposed approach uses OWL2 to represent product lifecycle data and design knowledge. The approach was applied to the redesign of a car door part for laser welding. Our work demonstrates how to retrieve design intent as a specific type of knowledge data in the context of design decisions. Such an approach can ultimately contribute to reducing design time, making knowledge transfer clear and thus improving the quality of designed products.

With consideration of the increasing relevance of energy consumption and rising energy costs, strategies to improve energy efficiency in manufacturing environments have to be developed. As the energy consumption results from the temporal accumulation of individual power demands of subordinate devices, energy metering enables to capture these demands and display the dynamic behaviour. A fundamental precondition for the identification and implementation of improvement measures is nevertheless transparency about energy demands as well as related couplings and interdependencies. In this paper, the context-aware analysis approach is proposed to provide additional information supporting the understanding of energy consumption in manufacturing environments.

Cloud computing sets to make a change in business between enterprises over the Internet based on services providing dynamically reconfigurable and virtualized networks. It is difficult for an aircraft manufacturer to consider the performance and status of each factory (i.e., capacity, inventory, order, technology, etc.) because of the huge amount of suppliers. In legacy environments, the supplier consumed a long time to identify the machine and process required and the energy to be consumed in order to satisfy a new customer demand. If the process occurred with a new order should be optimized, additional time and cost were needed. Cloud computing can enable the relationship between the manufacturer and suppliers of aircraft, which is representative of mass customized products, to be flexible and efficient. In order to enhance the response time during contracting with suppliers, this paper focuses on an architecture, providing a service for a decision with the concerns about the time and cost as well as energy with regard to process planning. For realizing this service the manufacturer provides the process plan described using neutral language for numerical controllers called 'STEP-NC' which is independent from a specific machine tool. On the supplier side, the machinability check, tool path generation and energy use estimation should be identified with machine tool specifications described by STEP-NC. In the Cloud computing environment, the analysis is performed to check the possibility that a supplier can complete a new order from the manufacturer on the due date with the analysis of machine tools and on-going production schedule. This function is developed in a system which can be installed on a virtual platform and provided by a cloud service. (C) 2014 Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

Additive manufacturing is becoming one of the key methods for reproducing repair sections in remanufacturing processes. The major advantage of using additive processes is to minimize production time and waste. However, the surface quality and shape accuracy are usually insufficient for the final product because the approximated representation format causes the accumulation of the error during the geometric operations of the process planning. This limitation is a barrier to utilize additive processes as finishing processes, such as general metal cutting. There is need to improve the final quality of parts obtained with additive manufacturing. In this paper, STEP-based numerical control (STEP-NC)-based process planning is applied to the additive manufacturing. ISO 14649 (STEP-NC) describes part programs with geometric data directly and also contains the information necessary for the intelligent process planning. This paper proposes the STEP-NC-based representation method of additive manufacturing and the series of geometric reasoning to automate the derivation of the repair section. The proposed representation has the benefits to provide a high accuracy for the final surface and to describe multiple materials. Topological data maintain low error during the series of process planning through the CAD-CAM-CNC chain. The proposed platform supports consideration of the process tolerance and comparison of the selected plan with alternative processes. In order to show the practical advantages, an analysis of the remanufacturing process is carried out. The case study of remanufacturing a pocket part is presented in order to validate the proposed process plan. The result of the case study shows the improvement in terms of automatic process planning and surface quality accuracy.