A Multi-Objective Optimization Approach for Multi-Head Beam-Type Placement Machines

This paper addresses a highly challenging scheduling problem in the field of printed circuit board (PCB) assembly systems using Surface Mounting Devices (SMD). After describing some challenging optimization sub-problems relating to the heads of multi-head surface mounting placement machines, we formulate an integrated multi-objective mathematical model considering of two main sub-problems simultaneously. The proposed model is a mixed integer nonlinear programming one which is very complex to be solved optimally. Therefore, it is first converted into a linearized model and then solved using an efficient multi-objective approach, i.e., the augmented epsilon constraint method. An illustrative example is also provided to show the usefulness and applicability of the proposed model and solution method.PCB assembly. Multi-head beam-type placement machine. Multi-objective mathematical programming. Augmented epsilon-constraint method

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

Computational evaluation of a novel approach to process planning for circuit card assembly on dual head placement machines

Dual head placement machines are commonly used in industry for placing components on circuit cards with great speed and accuracy. This thesis evaluates a novel approach for prescribing process plans for circuit card assembly on dual head placement machines. Process planning involves assigning component types to heads and to feeder slots associated with each head and prescribing appropriate sequences of picking, placing and nozzle-changing steps. The approach decomposes these decisions into four inter-related problems: P1, P2, P3 and P4. This thesis reviews this approach; presents a new heuristic to address P1; a method to facilitate P2 and P3 solutions; a method to control nozzle changes in P4; tests approaches to P1, P2, P3 and P4; and presents a thorough analysis of computational results to evaluate the efficacy of the approach which aims to balance workloads on machine heads to maximize assembly line throughput

Production and distribution research center

Issued as Annual report, Project no. E-24-62

Algorithmic Solutions for Combinatorial Problems in Resource Management of Manufacturing Environments

This thesis studies the use of heuristic algorithms in a number of combinatorial problems that occur in various resource constrained environments. Such problems occur, for example, in manufacturing, where a restricted number of resources (tools, machines, feeder slots) are needed to perform some operations. Many of these problems turn out to be computationally intractable, and heuristic algorithms are used to provide efficient, yet sub-optimal solutions. The main goal of the present study is to build upon existing methods to create new heuristics that provide improved solutions for some of these problems. All of these problems occur in practice, and one of the motivations of our study was the request for improvements from industrial sources. We approach three different resource constrained problems. The first is the tool switching and loading problem, and occurs especially in the assembly of printed circuit boards. This problem has to be solved when an efficient, yet small primary storage is used to access resources (tools) from a less efficient (but unlimited) secondary storage area. We study various forms of the problem and provide improved heuristics for its solution. Second, the nozzle assignment problem is concerned with selecting a suitable set of vacuum nozzles for the arms of a robotic assembly machine. It turns out that this is a specialized formulation of the MINMAX resource allocation formulation of the apportionment problem and it can be solved efficiently and optimally. We construct an exact algorithm specialized for the nozzle selection and provide a proof of its optimality. Third, the problem of feeder assignment and component tape construction occurs when electronic components are inserted and certain component types cause tape movement delays that can significantly impact the efficiency of printed circuit board assembly. Here, careful selection of component slots in the feeder improves the tape movement speed. We provide a formal proof that this problem is of the same complexity as the turnpike problem (a well studied geometric optimization problem), and provide a heuristic algorithm for this problem.Siirretty Doriast

Evaluation of Single and Dual image Object Detection through Image Segmentation Using ResNet18 in Robotic Vision Applications

This study presents a method for enhancing the accuracy of object detection in industrial automation applications using ResNet18-based image segmentation. The objective is to extract object images from the background image accurately and efficiently. The study includes three experiments, RGB to grayscale conversion, single image processing, and dual image processing. The results of the experiments show that dual image processing is superior to both RGB to grayscale conversion and single image processing techniques in accurately identifying object edges, determining CG values, and cutting background images and gripper heads. The program achieved a 100% success rate for objects located in the workpiece tray, while also identifying the color and shape of the object using ResNet-18. However, single image processing may have advantages in certain scenarios with sufficient image information and favorable lighting conditions. Both methods have limitations, and future research could focus on further improvements and optimization of these methods, including separating objects into boxes of each type and converting image coordinate data into robot working area coordinates. Overall, this study provides valuable insights into the strengths and limitations of different object recognition techniques for industrial automation applications

Parallelization of the mosaic image alighment algorithm

Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.Includes bibliographical references (p. 33).Laughton M. Stanley.S.B.and M.Eng