Design for Additive Manufacturing in the Cloud Platform

Additive manufacturing (AM) has experienced a phenomenal expansion in recent years and new technologies and materials rapidly emerge in the market. Design for Additive Manufacturing (DfAM) becomes more and more important to take full advantage of the capabilities provided by AM. However, most people still have limited knowledge to make informed decisions in the design stage. Therefore, an interactive DfAM system in the cloud platform is proposed to enable people sharing the knowledge in this field and guide the designers to utilize AM efficiently. There are two major modules in the system, decision support module and knowledge management module. A case study is presented to illustrate how this system can help the designers understand the capabilities of AM processes and make rational decisions.

Interoperability in Cloud Manufacturing and Practice on Private Cloud Structure for SMEs

In recent years, Cloud manufacturing has become a new research trend in manufacturing systems leading to the next generation of production paradigm. However, the interoperability issue still requires more research due to the heterogeneous environment caused by multiple Cloud services and applications developed in different platforms and languages. Therefore, this research aims to combat the interoperability issue in Cloud Manufacturing System. During implementation, the industrial users, especially Small- and Medium-sized Enterprises (SMEs), are normally short of budget for hardware and software investment due to financial stresses, but they are facing multiple challenges required by customers at the same time including security requirements, safety regulations. Therefore in this research work, the proposed Cloud manufacturing system is specifically tailored for SMEs.

Smart Manufacturing Through a Framework for a Knowledge-Based Diagnosis System

Various techniques are used to diagnose problems throughout all levels of the organization within the manufacturing industry. Often times, this root cause analysis is ad-hoc with no standard representation for artifacts or terminology (i.e., no standard representation for terms used in techniques such as fishbone diagrams, 5 why’s, etc.). Once a problem is diagnosed and alleviated, the results are discarded or stored locally as paper/digital text documents. When the same or similar problem reoccurs with different employees or in a different factory, the whole process has to be repeated without taking advantage of knowledge gained from previous problem(s) and corresponding solution(s). When discussing the diagnosis, personnel may miscommunicate over terms used in the root cause analysis leading to wasted time and errors. This paper presents a framework for a knowledge-based manufacturing diagnosis system that aims to alleviate these miscommunications. By learning from diagnosis methods used in manufacturing and in the medical community, this paper proposes a framework which integrates and formalizes root cause analysis by categorizing faults and failures that span multiple organizational levels. The proposed framework aims to enable manufacturing operations by leveraging machine learning and semantic technologies for the manufacturing system diagnosis. A use case for the manufacture of a bottle opener demonstrates the framework.

Software Defined Manufacturing Extends Cloud-Based Control

Nowadays, the key goal in manufacturing is being very efficient within changing markets and under turbulent conditions. Therefore, production plants with their machines logistics and all the other involved components have to be adaptable to changing conditions. For this reason, reconfigurable manufacturing systems are needed, which allow a fast adaption to new requirements of the product to be manufactured. Today, reconfiguration in manufacturing is mostly limited due to missing reconfigurability of the control software in combination with the underlying hardware. The coupling is that strong that in manufacturing control software is always bound to special hardware. Until now, flexibility is only possible by changing application or part programs that are interpreted by a fixed control kernel. The adaption of any core functionality is impossible, and any other changes require high manual effort for redesigning software systems and parametrizing their functionalities. For better adaptability in manufacturing this coupling has to be dissolved. Other disciplines and industries have similar requirements like the information and communication technology (ICT). In the area of ICT, there are more and more concepts of Software Defined Anything (SDX) like Software Defined Networking (SDN) or Software Defined Radio (SDR). Flexible, adaptive and really reconfigurable manufacturing should be improved by a new concept of Software Defined Manufacturing (SDM). SDM allows freely defined functionalities within the physical limitations of the mechanical and electrical components of a machine. But current manufacturing equipment with its control architecture does not offer the technical basis for such a concept. Existing concepts of cloud-based control architectures show indeed a virtualization of the control algorithms. Due to the fact that the software is running remotely, the software is decoupled from its hardware. However, the local control algorithms with hard real-time requirements still have a very strong coupling with the hardware. The local control software could not be defined freely according to the requirements of the product to be manufactured. In this paper, a new control architecture for manufacturing that combines cloud-based control as a service (CaaS) and Software Defined Manufacturing is presented. As a result, an architecture of an operating system for manufacturing equipment is shown, which is freely programmable. This paper deals with Software Defined Manufacturing for local control software, communication and cloud-based control systems. SDM allows defining the behavior of the entire manufacturing process based on design description of a product to be manufactured. In addition, methods are described, which allow the automatic configuration and optimization of such an architecture by using simulation technics and collected process data.

Dynamic Manufacturing Capability Assessment of Industrial Robots Based on Feedback Information in Cloud Manufacturing

With the development of information and computer network technology, cloud manufacturing has been developing rapidly, industrial robots (IRs) as a vital symbol and an advanced technology of manufacturing industry, in scheduling service, the constantly changing information data will result in the corresponding vary of the manufacturing capability. Under a fixed constraint of some capability service request, this will decrease the number of the optimal solutions and provide the inaccurate service to users. So it is important to make the manufacturing capability stable and obtain more optimal solutions to satisfy the constraint, thus the dynamic assessment of manufacturing capability based on information feedback is investigated in this paper. A set of indicators is established considering the IRs’ manufacturing capability and a new dynamic assessment model is proposed to achieve the actual data and the expected data information feedback, using the “normal distribution” model, which can correct the assessment weight. By the way, a case study is simulated in the MATLAB, which shows the reliability and reasonability of this method in evaluate the manufacturing capability in IR.

Transport and Interfacial Phenomena in Nanoscale Confined Laser Crystallization

Laser processing (sintering, melting, crystallization and ablation) of nanoscale materials has been extensively employed for electronics manufacturing including both integrated circuit and emerging printable electronics. Many applications in semiconductor devices require annealing step to fabricate high quality crystalline domains on substrates that may not intrinsically promote the growth of high crystalline films. The recent emergence of FinFETs (Fin-shaped Field Effect Transistor) and 3D Integrated Circuits (3D-IC) has inspired the study of crystallization of amorphous materials in nano/micro confined domains. Using Molecular Dynamics (MD) simulation, we study the characteristics of unseeded crystallization within nano/microscale confining domains. Firstly, it is demonstrated that unseeded crystallization can yield single crystal domains facilitated by the confinement effects. A phenomenological model has been developed and tailored by MD simulations, which was applied to quantitatively evaluate the effects of domain size and processing laser pulse width on single crystal formation. Secondly, to predict crystallization behaviors on confining walls, a thermodynamics integration scheme will be used to calculate interfacial energies of Si-SiO2 interfaces.

Replacing Mechanized Oxyfuel Cutting Sensors With Ion Current Sensing

This paper describes a method using electrical characteristics of the torch, flame, and work piece to replace active sensing elements most commonly used for mechanized oxyfuel cutting applications; height, fuel/oxygen ratio, work temperature, and preheat flow rate. Calibrations are given for the torch under test for standoff accurate to ±1/32 in (0.8 mm) and F/O ratio accurate to ±.008. Methods are proposed for balancing flow across multi-torch systems, and detecting the work kindling temperature. Additional work is needed if calibrated flow and work temperatures are to be measured electrically.

Hierarchical Decomposition of a Manufacturing Work Cell to Promote Monitoring, Diagnostics, and Prognostics

Manufacturing work cell operations are typically complex, especially when considering machine tools or industrial robot systems. The execution of these manufacturing operations require the integration of layers of hardware and software. The integration of monitoring, diagnostic, and prognostic technologies (collectively known as prognostics and health management (PHM)) can aid manufacturers in maintaining the performance of machine tools and robot systems by providing intelligence to enhance maintenance and control strategies. PHM can improve asset availability, product quality, and overall productivity. It is unlikely that a manufacturer has the capability to implement PHM in every element of their system. This limitation makes it imperative that the manufacturer understand the complexity of their system. For example, a typical robot systems include a robot, end-effector(s), and any equipment, devices, or sensors required for the robot to perform its task. Each of these elements is bound, both physically and functionally, to one another and thereby holds a measure of influence. This paper focuses on research to decompose a work cell into a hierarchical structure to understand the physical and functional relationships among the system’s critical elements. These relationships will be leveraged to identify areas of risk, which would drive a manufacturer to implement PHM within specific areas.

Remaining Useful Life Estimation Based on a Segmental Hidden Markov Model With Continuous Observations

In this paper, a segmental hidden Markov model (SHMM) with continuous observations, is developed to tackle the problem of remaining useful life (RUL) estimation. The proposed approach has the advantage of predicting the RUL and detecting the degradation states simultaneously. As the observation space is discretized into N segments corresponding to N hidden states, the explicit relationship between actual degradation paths and the hidden states can be depicted. The continuous observations are fitted by Gaussian, Gamma and Lognormal distribution, respectively. To select a more suitable distribution, model validation metrics are employed for evaluating the goodness-of-fit of the available models to the observed data. The unknown parameters of the SHMM can be estimated by the maximum likelihood method with the complete data. Then a recursive method is used for RUL estimation. Finally, an illustrate case is analyzed to demonstrate the accuracy and efficiency of the proposed method. The result also suggests that SHMM with observation probability distribution which is closer to the real data behavior may be more suitable for the prediction of RUL.

The Behavior Simulation of Manufacturing Services in a Service-Oriented Networked Manufacturing Environment

In a service-oriented networked manufacturing (SONM) environment, geographically distributed manufacturing resources are encapsulated as various manufacturing services. These manufacturing services release via the Internet and can provide services on the demand of manufacturing tasks. Usually one manufacturing task needs several different services belonged to different organizers to work together. Hence, effective cooperation among services is the foundation of the efficient operation of SONM. In this paper, a bipartite network model is presented to describe the relationship of two different kinds of nodes in SONM, and also is projected as a weighed network for further exploring the behaviors of service nodes. Furthermore, an agent-based model is built for modeling the interactive behaviors of service nodes in a cooperative network and an agent-based simulating system is developed with Repast. The simulation results show that the emergence of cooperative behaviors among service nodes is related to both the cost of cooperation and initial trust of services in the SONM environment.