Design of a smart carrier for asset-awareness production

The adaptation of new technology to production system is an open challenge. The most advanced devices allow producing faster, cheaper and better, helping to fulfil the market needs. The companies are under pressure to satisfy the changing requirements from customers. Production lines have limited resources, making a perfect use of them is nowadays mandatory, due to the fact that the lifecycle of the products are shorter and in consequence the manufacturing lines lifecycle is being reduced. To increase the profitably, the bottlenecks and no operational time of the robots have to be avoided, or minimize them in order to arrive to a balanced line where the idle time of each robot is reduced to the minimum. To achieve this scenario, during last years, many fields such as monitoring, automatic quality control, asset-aware and self-recovery have got momentum. The purpose of this Master Thesis is the design, production, and integration of a transport system with asset-aware capabilities for an existing manufacturing line. The different devices to allow gathering the necessary data must be found, analysed, tested and incorporated to the transport element. The design of the new system counts for many aspects apart from the traditional functional design, such as imperceptible as wireless communication or ease robots reprograming. At the same time all this work has been done looking forward to design a transport system that could be easily implemented in the line, making the changes as fast as possible

Machine-Learning Based Microwave Sensing: A Case Study for the Food Industry

Despite the meticulous attention of food industries to prevent hazards in packaged goods, some contaminants may still elude the controls. Indeed, standard methods, like X-rays, metal detectors and near-infrared imaging, cannot detect lowdensity materials. Microwave sensing is an alternative method that, combined with machine learning classifiers, can tackle these deficiencies. In this paper we present a design methodology applied to a case study in the food sector. Specifically, we offer a complete flow from microwave dataset acquisition to deployment of the classifiers on real-time hardware and we show the effectiveness of this method in terms of detection accuracy. In the case study, we apply the machine-learning based microwave sensing approach to the case of food jars flowing at high speed on a conveyor belt. First, we collected a dataset from hazelnutcocoa spread jars which were uncontaminated or contaminated with various intrusions, including low-density plastics. Then, we performed a design space exploration to choose the best MLPs as binary classifiers, which resulted to be exceptionally accurate. Finally, we selected the two most light-weight models for implementation on both an ARM-based CPU and an FPGA SoC, to cover a wide range of possible latency requirements, from loose to strict, to detect contaminants in real-time. The proposed design flow facilitates the design of the FPGA accelerator that might be required to meet the timing requirements by using a high-level approach, which might be suited for the microwave domain experts without specific digital hardware skills

IR spectroscopy of the higher fullerene C84-D2:22 for its qualitative and quantitative determination

The stable isomer of the higher fullerene C84 of D2 symmetry was isolated from carbon soot by the new advanced extraction and chromatographic methods and processes. Characteriyation of the isolated C84-D2:22 was performed by the FT-IR(KBr) method, over the relevant region from 400 to 2000 cm-1, in the absorption mode. A series of various characteristic, dominant and new absorption maxima of this fullerene was registered and confirmed. All the observed infrared absorption bands are in excellent agreement with the theoretical calculations for this molecule. The molar absorptivity, ε, as well as the integrated molar absorptivity, ψ, of its IR absorption bands were determined in adequate and different spectral regions. These results are significant for the qualitative and quantitative determination of the C84-D2:22 fullerene either in natural resources in space and on the Earth or in artificially synthesized materials, electronic, optical and biomedical devices, polymers, composites, catalysts, nanowires, batteries, sensors, optical limiters, solar cells, nanophotonic lenses with improved optical absorption characteristics, refraction features and wettability, diagnostic and therapeutic agents, pharmaceutical substances, such as for diabetes, targeted drug delivery, in biomedical engineering, applied optics industry and so forth

Yield sensing technologies for perennial and annual horticultural crops: a review

Yield maps provide a detailed account of crop production and potential revenue of a farm. This level of details enables a range of possibilities from improving input management, conducting on-farm experimentation, or generating profitability map, thus creating value for farmers. While this technology is widely available for field crops such as maize, soybean and grain, few yield sensing systems exist for horticultural crops such as berries, field vegetable or orchards. Nevertheless, a wide range of techniques and technologies have been investigated as potential means of sensing crop yield for horticultural crops. This paper reviews yield monitoring approaches that can be divided into proximal, either direct or indirect, and remote measurement principles. It reviews remote sensing as a way to estimate and forecast yield prior to harvest. For each approach, basic principles are explained as well as examples of application in horticultural crops and success rate. The different approaches provide whether a deterministic (direct measurement of weight for instance) or an empirical (capacitance measurements correlated to weight for instance) result, which may impact transferability. The discussion also covers the level of precision required for different tasks and the trend and future perspectives. This review demonstrated the need for more commercial solutions to map yield of horticultural crops. It also showed that several approaches have demonstrated high success rate and that combining technologies may be the best way to provide enough accuracy and robustness for future commercial systems

Development of an automated robot vision component handling system

Thesis (M. Tech. (Engineering: Electrical)) -- Central University of technology, Free State, 2013In the industry, automation is used to optimize production, improve product quality and increase profitability. By properly implementing automation systems, the risk of injury to workers can be minimized. Robots are used in many low-level tasks to perform repetitive, undesirable or dangerous work. Robots can perform a task with higher precision and accuracy to lower errors and waste of material. Machine Vision makes use of cameras, lighting and software to do visual inspections that a human would normally do. Machine Vision is useful in application where repeatability, high speed and accuracy are important. This study concentrates on the development of a dedicated robot vision system to automatically place components exiting from a conveyor system onto Automatic Guided Vehicles (AGV). A personal computer (PC) controls the automated system. Software modules were developed to do image processing for the Machine Vision system as well as software to control a Cartesian robot. These modules were integrated to work in a real-time system. The vision system is used to determine the parts‟ position and orientation. The orientation data are used to rotate a gripper and the position data are used by the Cartesian robot to position the gripper over the part. Hardware for the control of the gripper, pneumatics and safety systems were developed. The automated system‟s hardware was integrated by the use of the different communication protocols, namely DeviceNet (Cartesian robot), RS-232 (gripper) and Firewire (camera)

Micro-manufacturing : research, technology outcomes and development issues

Besides continuing effort in developing MEMS-based manufacturing techniques, latest effort in Micro-manufacturing is also in Non-MEMS-based manufacturing. Research and technological development (RTD) in this field is encouraged by the increased demand on micro-components as well as promised development in the scaling down of the traditional macro-manufacturing processes for micro-length-scale manufacturing. This paper highlights some EU funded research activities in micro/nano-manufacturing, and gives examples of the latest development in micro-manufacturing methods/techniques, process chains, hybrid-processes, manufacturing equipment and supporting technologies/device, etc., which is followed by a summary of the achievements of the EU MASMICRO project. Finally, concluding remarks are given, which raise several issues concerning further development in micro-manufacturing

Automated Quality Control in Manufacturing Production Lines: A Robust Technique to Perform Product Quality Inspection

Quality control (QC) in manufacturing processes is critical to ensuring consumers receive products with proper functionality and reliability. Faulty products can lead to additional costs for the manufacturer and damage trust in a brand. A growing trend in QC is the use of machine vision (MV) systems because of their noncontact inspection, high repeatability, and efficiency. This thesis presents a robust MV system developed to perform comparative dimensional inspection on diversely shaped samples. Perimeter, area, rectangularity, and circularity are determined in the dimensional inspection algorithm for a base item and test items. A score determined with the four obtained parameter values provides the likeness between the base item and a test item. Additionally, a surface defect inspection is offered capable of identifying scratches, dents, and markings. The dimensional and surface inspections are used in a QC industrial case study. The case study examines the existing QC system for an electric motor manufacturer and proposes the developed QC system to increase product inspection count and efficiency while maintaining accuracy and reliability. Finally, the QC system is integrated in a simulated product inspection line consisting of a robotic arm and conveyor belts. The simulated product inspection line could identify the correct defect in all tested items and demonstrated the system’s automation capabilities

Nondestructive measurement of fruit and vegetable quality

We review nondestructive techniques for measuring internal and external quality attributes of fruit and vegetables, such as color, size and shape, flavor, texture, and absence of defects. The different techniques are organized according to their physical measurement principle. We first describe each technique and then list some examples. As many of these techniques rely on mathematical models and particular data processing methods, we discuss these where needed. We pay particular attention to techniques that can be implemented online in grading lines

Active and Passive Thermography for the Detection of Defects in Green-State Powdermetallic Compacts

Despite its maturity, the powder metallurgy (PM) fabrication process continues to rely heavily on indirect methods to determine and predict the quality of its compacts early in the manufacturing line. Currently, the most comprehensive testing is performed on sintered parts, resulting in higher cost and increased waste. This dissertation addresses the need of early inspection by developing a novel approach whereby PM compacts are tested in the green-state without intrusion and with minimal cost per compact tested. The method is based on an infrared detection scheme with two fundamental embodiments. For high resolution applications, or offline testing, an active thermography approach is adopted; electric energy is deposited into the compact in a contact-less fashion to evaluate all parts for cracks, inclusions, or delaminations. As an alternative, for lower resolution high-yield applications, a system based on a passive thermography approach is developed. This system relies on residual heating emanating from the process. Thermal data is then collected and analyzed in an effort to yield part integrity and process stability information. In this dissertation we will discuss our design approach, theoretical modeling aspects, and a proof-of-concept instrument with associated data processing software. We will first describe the underlying physical principles, followed by predictions from the modeling formulation, including a solution of the heat equation. As part of our experimental data processing, we will present results that are collected both in a laboratory setting and in an industrial manufacturing line. The integrity of the compacts is carried out with the aid of a specialized software package