An Aggregated Optimization Model for Multi-Head SMD Placements

In this article we propose an aggregate optimization approach by formulating the multi-head SMD placement optimization problem into a mixed integer program (MIP) with the variables based on batches of components. This MIP is tractable and effective in balancing workload among placement heads, minimizing the number of nozzle exchanges, and improving handling class. The handling class which specifies the traveling speed of the robot arm, to the best of our knowledge, has been for the first time incorporated in an optimization model. While the MIP produces an optimal planning for batches of components, a new sequencing heuristics is developed in order to determine the final sequence of component placements based on the outputs of the MIP. This two-stage approach guarantees a good feasible solution to the multi-head SMD placement optimization problem. The computational performance is examined using real industrial data.Multi-head surface mounting device;Component placement;Variable placement speed

Importance and applications of robotic and autonomous systems (RAS) in railway maintenance sector: a review

Maintenance, which is critical for safe, reliable, quality, and cost-effective service, plays a dominant role in the railway industry. Therefore, this paper examines the importance and applications of Robotic and Autonomous Systems (RAS) in railway maintenance. More than 70 research publications, which are either in practice or under investigation describing RAS developments in the railway maintenance, are analysed. It has been found that the majority of RAS developed are for rolling-stock maintenance, followed by railway track maintenance. Further, it has been found that there is growing interest and demand for robotics and autonomous systems in the railway maintenance sector, which is largely due to the increased competition, rapid expansion and ever-increasing expense

Setup Optimization in High-Mix Surface Mount PCB Assembly

Siirretty Doriast

Feasibility analysis of using special purpose machines for drilling-related operations

This work focuses on special purpose machine tools (SPMs), providing a modular platform for performing drilling-related operations. One of the main challenges in using SPMs is selecting the most appropriate machine tool among many alternatives. This thesis introduces a feasibility analysis procedure developed to support decision-making through the assessment of the strengths and limitations of SPMs. To achieve this, technical and economic feasibility analyses, a sensitivity analysis, and an optimisation model were developed and a case study was provided for each analysis. The results indicated that although technical feasibility analysis leads decision-makers to select a feasible machine tool, complementary analyses are required for making an informed decision and improving profitability. Accordingly, a mathematical cost model was developed to perform economic and sensitivity analyses and investigate the profitability of any selected SPM configuration. In addition, an optimisation procedure was applied to the cost model in order to investigate the effect of process parameters and the SPM configuration on the decision-making. Finally, the developed analyses were then integrated into a model in a proper sequence that can evaluate whether the SPM is appropriate for producing the given part and achieving higher productivity. To validate this integrated model three different case studies were presented and results were discussed. The results showed that the developed model is a very useful tool in assisting manufacturers to evaluate the performance of SPMs in comparison with other alternatives considered from different perspectives

OPTIMIZATION OF CUTTING CONDITIONS FOR SUSTAINABLE MACHINING OF SINTERED POWDER METAL STEELS USING PCBN AND CARBIDE TOOLS

Powder metals are becoming a popular choice in the automotive and other manufacturing industries because of their ability to meet wide ranging product functional requirements without compromising the performance of the product. They offer various advantages, including weight reduction, near net-shape processing capability, and their ability to be sintered to achieve desired properties in the end-product. However, in order to satisfy the product design requirements during manufacturing, they need to be machined to the required tolerances. Machining of powder metals is quite different to machining of traditional metals because of their specific properties, including porosity. This thesis work deals with the finish machining of powder metal steels in automotive applications, for increased tool-life/reduced tool-wear. Tool-life is affected by a variety of factors such as tool grade selection, tool coating, cutting conditions and tool geometry including cutting edge geometry. This work involves optimization of cutting conditions for plunge cutting and boring operations of automotive powder metal components using PCBN and carbide tools. The cycle time of the process introduces an additional constraint for the optimization model along with the tool-wear criterion. Optimized cutting conditions are achieved for maximum tool-life

Optimisation process for robotic assembly of electronic components

Adoption of robots in the manufacturing environment is a way to improve productivity, and the assembly of electronic components has benefited from the adoption of highly dedicated automation equipment. Traditionally, articulated 6-axis robots have not been used in electronic surface mount assembly. However, the need for more flexible production systems that can be used for low to medium production builds means that these robots can be used due to their high degrees of flexibility, excellent repeatability and increasingly lower investment costs. This research investigated the application of an articulated robot with six degrees of freedom to assemble a multi-component printed circuit board (PCB) for an electronic product. A heuristic methodology using a genetic algorithm was used to plan the optimal sequence and identify the best location of the parts to the assembly positions on the PCB. Using the optimised paths, a condition monitoring method for cycle time evaluation was conducted using a KUKA KR16 assembly cell together with four different robot path motions. The genetic algorithm approach together with different assembly position iterations identified an optimisation method for improved production throughput using a non-traditional but highly flexible assembly method. The application of optimised articulated robots for PCB assembly can bridge the gap between manual assembly and the high-throughput automation equipment

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

Optimization of robotic assembly of printed circuit board by using evolutionary algorithm

This research work describes the development and evaluation of a custom application exploring the use of Artificial Immune System algorithms (AIS) to solve a component placement sequencing problem for printed circuit board (PCB) assembly. In the assembly of PCB’s, the component placement process is often the bottleneck and the equipment to complete component placement is often the largest capital investment

Optimal scope of supply chain network & operations design

The increasingly complex supply chain networks and operations call for the development of decision support systems and optimization techniques that take a holistic view of supply chain issues and provide support for integrated decision-making. The economic impacts of optimized supply chain are significant and that has attracted considerable research attention since the late 1990s. This doctoral thesis focuses on developing manageable and realistic optimization models for solving four contemporary and interrelated supply chain network and operations design problems. Each requires an integrated decision-making approach for advancing supply chain effectiveness and efficiency. The first model formulates the strategic robust downsizing of a global supply chain network, which requires an integrated decision-making on resource allocation and network reconfiguration, given certain financial constraints. The second model also looks at the strategic supply chain downsizing problem but extends the first model to include product portfolio selection as a downsizing decision. The third model concerns the redesign of a warranty distribution network, which requires an integrated decision-making on strategic network redesign and tactical recovery process redesign. The fourth model simultaneously determines the operational-level decisions on job assignment and process sequence in order to improve the total throughput of a production facility unit

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book