Blockchain ready manufacturing supply chain using distributed ledger

The blockchain technology as a foundation for distributed ledgers offers an innovative platform for a new decentralized and transparent transaction mechanism in industries and businesses. The inherited characteristics of this technology enhance trust through transparency and traceability within any transaction of data, goods, and financial resources. Despite initial doubts about this technology, recently governments and large corporations have investigated to adopt and improve this technology in various domains of applications, from finance, social and legal industries to design, manufacturing and supply chain networks. In this article, the authors review the current status of this technology and some of its applications. The potential benefit of such technology in manufacturing supply chain is then discussed in this article and a vision for the future blockchain ready manufacturing supply chain is proposed. Manufacturing of cardboard boxes are used as an example to demonstrate how such technology can be used in a global supply chain network. Finally, the requirements and challenges to adopt this technology in the future manufacturing systems are discussed

Real time energy consumption analysis for manufacturing systems using integrative virtual and discrete event simulation

Manufacturing companies need greater capabilities to respond quicker to market dynamics and varying demands. Paradigms such as mass customization, global manufacturing operations and competition provide a platform to meet these needs. Therefore a continuous restructuring and re-engineering of the processes is seen in the manufacturing industries. This is extremely important in cases when automated machines are used in production. Automotive industry is an example of having intensive use of automated processes. During the reengineering of the processes it must be focused to control the factors which add cost during the processes. Energy is one of the important parameter which acts continuously over the process and increases the product price. Therefore, this paper proposes to validate the processes for energy optimization during the design stages well before, to physically build a machine. This could be done by using virtual environment and discrete event simulation integration. The pilot study of an ongoing research has been carried out to identify the level of energy consumption in a case study along with the identification of information to be used in the virtual tool prior to build the models. The adopted approach would propose to identify the processes to keep them off if consuming energy even in idle states. It could be identify through simulation that which one is the energy intensive process when they are idle, and then try it for the option, to keep it off when not working. Utilizing less energy in production helps society to have low cost products as well as to maintain the sustainable resources over a long period of time

Development of an intelligent automated polishing system

In high-value manufacturing sectors, many manufacturing processes are still performed manually, such as polishing operations for small metallic parts. Increasing volume, the need for consistency in quality, and health and safety issues are some of the reasons for industry to search urgently for alternative solutions for manual polishing processes. This article reports the development of an intelligent automated polishing system to achieve consistent surface quality and removal of superficial defects from high-value components, such as those used in aerospace industry. The article reports an innovative method to capture manual polishing processes by skilled operators. The captured polishing parameters are then used to develop and control a robotic polishing system that can adopt various polishing patterns. A brief summary of existing fully and semi-automated polishing systems and their inadequacy for industrial applications are discussed. The need for building automation system based on manual operations are explained and a systematic data capturing process for a specific aerospace-based component is defined. The development of the process capturing device is explained, the data analysis and interpretations are discussed and the migration from manual operation to an automated polishing system is reported. Further detailed information is given in relation with combining data from various sensors and building of an automated system based on learning from manual operations. The research results are also briefly discussed and conclusions are drawn regarding applicability of automated systems for highly skilled manual operations

An automated solution for fixtureless sheet metal forming

Manual forming of sheet metal parts through traditional panel beating is a highly skilled profession used in many industries, particularly for sample manufacturing or repair and maintenance. However, this skill is becoming gradually isolated mainly due to the high cost and lack of expertise. Nonetheless, a cost-effective and flexible approach to forming sheet metal parts could significantly assist various industries by providing a method for fast prototyping sheet metal parts. The development of a new fixtureless sheet metal forming approach is discussed in this article. The proposed approach, named Mechatroforming®, consists of integrated mechanisms to manipulate sheet metal parts by a robotic arm under a controlled hammering tool. The method includes mechatronics-based monitoring and control systems for (near) real-time prediction and control of incremental deformations of parts. This article includes description of the proposed approach, the theoretical and modelling backgrounds used to predict the forming, skills learned from manual operations, and proposed automation system being built

Towards an automated polishing system: capturing manual polishing operations

Advancements in robotic and automation industries have influenced many manual manufacturing operations. With a great level of success, robots have taken over from man in many processes such as part manufacturing, transfer and assembly. However, in other traditionally manual operations such as polishing, automation has only partially been successful, typically limited to parts with simple geometry and low accuracy. Automated polishing systems using robots have been attempted already by a number of industrial and research groups; however, there are few examples of deploying such a system as a part of a routine production process in high-technology industries, such as aerospace. This is due to limitations in flexibility, speed of operation, and inspection processes, when compared with manual polishing processes. The need for automated polishing processes is discussed in this article and the problem with the existing system was explained to be a lack of understanding and the disconnect from manual operations. In collaboration with industrial partners, a mechatronic based data capturing device was developed to accurately capture and analyze operational variables such as force, torque, vibration, polishing pattern, and feed rates. Also reported in this article is a set of experiments carried out to identify the polishing parameters that a manual operator controls through tactile and visual sensing. The captured data is interpreted to the operators’ preferences and polishing methods and should then be included in the design of an automated polishing system. The research results reported in this article are fed back to an ongoing research project on developing an integrated robotic polishing system

Innovative mechanism to identify robot alignment in an automation system

Robotic applications are commonly used in industrial automation systems. Such systems are often comprised of a series of equipment, including robotic arms, conveyors, a workspace, and fixtures. While each piece of equipment may be calibrated with the highest precision, their alignment in relation to each other is an important issue in defining the accuracy of the system. Currently, a variety of complex automated and manual methods are used to align a robotic arm to a workspace. These methods often use either expensive equipment or are slow and skill-dependent. This paper presents a novel low-cost method for aligning an industrial robot to its workcell at 6 degrees of freedom (DoF). The solution is new, simple and easy to use and intended for the SMEs dealing with low volume, high complexity automated systems. The proposed method uses three dial indicators mounted to a robot end effector and a fixed measurement cube, positioned on a workcell. The robot is pre-programmed for a procedure around the cube. The changes on the dial indicators are used to calculate the misalignment between the robot and the workcell. Despite simplicity of the design, the solution is supported with complex real-time mathematical calculations and proven to identify and eliminate misalignment up to 3mm and 5 degrees to an accuracy of 0.003mm and 0.002 degrees: much higher than the precision required for a conventional industrial robot. In this article, the authors describe a proposed solution, validate the computation both theoretically and through a laboratory test rig and simulation

Robotic assembly of threaded fasteners in a non-structured environment

Over the past two decades, a major part of the manufacturing and assembly market has been driven by the increasing demand for customised products. This has created the need for smaller batch sizes, shorter production times, lower costs, and the flexibility to produce families of products—or to assemble different parts—with the same sets of equipment. Consequently, manufacturing companies have deployed various automation systems and production strategies to improve their resource efficiency and move towards right-first-time production. Threaded fastening operations are widely used in assembly and are typically time-consuming and costly. In high-volume production, fastening operations are commonly automated using jigs, fixtures, and semi-automated tools. However, in low-volume, high-value manufacturing, fastening operations are carried out manually by skilled workers. The existing approaches are found to be less flexible and robust for performing assembly in a less structured industrial environment. This motivated the development of a flexible solution, which does not require fixtures and is adaptable to variation in part locations and lighting conditions. As a part of this research, a novel 3D threaded hole detection and a fast bolt detection algorithms are proposed and reported in this article, which offer substantial enhancement to the accuracy, repeatability, and the speed of the processes in comparison with the existing methods. Hence, the proposed method is more suitable for industrial applications. The development of an automated bolt fastening demonstrator is also described in this article to test and validate the proposed identification algorithms on complex components located in 3D space

Combining business process and failure modelling to increase yield in electronics manufacturing

The prediction and capturing of defects in low-volume assembly of electronics is a technical challenge that is a prerequisite for design for manufacturing (DfM) and business process improvement (BPI) to increase first-time yields and reduce production costs. Failures at the component-level (component defects) and system-level (such as defects in design and manufacturing) have not been incorporated in combined prediction models. BPI efforts should have predictive capability while supporting flexible production and changes in business models. This research was aimed at the integration of enterprise modelling (EM) and failure models (FM) to support business decision making by predicting system-level defects. An enhanced business modelling approach which provides a set of accessible failure models at a given business process level is presented in this article. This model-driven approach allows the evaluation of product and process performance and hence feedback to design and manufacturing activities hence improving first-time yield and product quality. A case in low-volume, high-complexity electronics assembly industry shows how the approach leverages standard modelling techniques and facilitates the understanding of the causes of poor manufacturing performance using a set of surface mount technology (SMT) process failure models. A prototype application tool was developed and tested in a collaborator site to evaluate the integration of business process models with the execution entities, such as software tools, business database, and simulation engines. The proposed concept was tested for the defect data collection and prediction in the described case study

Integration issues in the development of a modelling and simulation tool for low volume high-complexity electronics manufacture

In order to design and implement the information systems and modules that could comprise an “industrial strong” knowledge-based tool, links to shop floor systems containing real-time production data and PCA customer information (e.g. bill of materials (BOM), CAD drawings) are required. Details of the issues of implementing the tool in an industrial organisation and the integration of various data sources (e.g. “in-house” developed systems, enterprise resource planning systems, ad-hoc developed databases, machine data and CAD data) are presented in this paper. The application of the CLOVES system in an industrial setup highlights the difficulties in integrating information from design as CAD data and shows how these setbacks could be overcome if the electronics industry were to adopt a common CAD assembly information exchange platform. Hence, this paper concludes that existing automation tool manufacturers should focus exclusively on developing generic connections by adopting industry standards that can facilitate the deployment of “plug and play” tools. This standardisation could in turn help software developers, to provide the electronics industry with more integrated systems that communicate better among loosely coupled information systems and avoid depending on extensive time consuming manual data input

An ensemble based on neural networks with random weights for online data stream regression

Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on Neural Network with Random Weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm's response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift and much faster updating times compared to the existing available approach. Additionally, the use of Design of Experiments as an effective tool for hyperparameter tuning is demonstrated