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A maturity model for the autonomy of manufacturing systems
Modern manufacturing has to cope with dynamic and changing circumstances. Market fluctuations, the effects caused by unpredictable material shortages, highly variable product demand, and worker availability all require system robustness, flexibility, and resilience. To adapt to these new requirements, manufacturers should consider investigating, investing in, and implementing system autonomy. Autonomy is being adopted in multiple industrial contexts, but divergences arise when formalizing the concept of autonomous systems. To develop an implementation of autonomous manufacturing systems, it is essential to specify what autonomy means, how autonomous manufacturing systems are different from other autonomous systems, and how autonomous manufacturing systems are identified and achieved through the main features and enabling technologies. With a comprehensive literature review, this paper provides a definition of autonomy in the manufacturing context, infers the features of autonomy from different engineering domains, and presents a five-level model of autonomy — associated with maturity levels for the features — to ensure the complete identification and evaluation of autonomous manufacturing systems. The paper also presents the evaluation of a real autonomous system that serves as a use-case and a validation of the model.
Journal Article
Machine learning and simulation-based surrogate modeling for improved process chain operation
In this contribution, a concept is presented that combines different simulation paradigms during the engineering phase. These methods are transferred into the operation phase by the use of data-based surrogates. As an virtual production scenario, the process combination of thermoforming continuous fiber-reinforced thermoplastic sheets and injection overmolding of thermoplastic polymers is investigated. Since this process is very sensitive regarding the temperature, the volatile transfer time is considered in a dynamic process chain control. Based on numerical analyses of the injection molding process, a surrogate model is developed. It enables a fast prediction of the product quality based on the temperature history. The physical model is transferred to an agent-based process chain simulation identifying lead time, bottle necks and quality rates taking into account the whole process chain. In the second step of surrogate modeling, a feasible soft sensor model is derived for quality control over the process chain during the operation stage. For this specific uses case, the production rejection can be reduced by 12% compared to conventional static approaches.
Journal Article
Manufacturing facilities design and material handling
\"Designed for junior- and senior-level courses in plant and facilities planning and manufacturing systems and procedures, this textbook also is suitable for graduate-level and two-year college courses. The book takes a practical, hands-on, project-oriented approach to exploring the techniques and procedures for developing an efficient facility layout. It also introduces state-of-the-art tools including computer simulation. Access to Layout-iQ workspace planning software is included for purchasers of the book. Theoretical concepts are clearly explained and then rapidly applied to a practical setting through a detailed case study at the end of the volume. The book systematically leads students through the collection, analysis, and development of information to produce a quality functional plant layout for a lean manufacturing environment. All aspects of facility design, from receiving to shipping, are covered. In the sixth edition of this successful book, numerous updates and corrections have been made, and a chapter on engineering cost estimating and analysis has been added. Also, rather than including brief case-in-point examples at the end of each chapter, a single, detailed case study is provided that better exposes students to the multiple considerations that need to be taken into account when improving efficiency in a real manufacturing facility. The textbook has enjoyed substantial international adoptions and has been translated into Spanish and Chinese\"-- Provided by publisher.
Book
TDD-net: a tiny defect detection network for printed circuit boards
Tiny defect detection (TDD) which aims to perform the quality control of printed circuit boards (PCBs) is a basic and essential task in the production of most electronic products. Though significant progress has been made in PCB defect detection, traditional methods are still difficult to cope with the complex and diverse PCBs. To deal with these problems, this article proposes a tiny defect detection network (TDD-Net) to improve performance for PCB defect detection. In this method, the inherent multi-scale and pyramidal hierarchies of deep convolutional networks are exploited to construct feature pyramids. Compared with existing approaches, the TDD-Net has three novel changes. First, reasonable anchors are designed by using k-means clustering. Second, TDD-Net strengthens the relationship of feature maps from different levels and benefits from low-level structural information, which is suitable for tiny defect detection. Finally, considering the small and imbalance dataset, online hard example mining is adopted in the whole training phase in order to improve the quality of region-of-interest (ROI) proposals and make more effective use of data information. Quantitative results on the PCB defect dataset show that the proposed method has better portability and can achieve 98.90% mAP, which outperforms the state-of-arts. The code will be publicly available.
Journal Article
Reinventing manufacturing and business processes through artificial intelligence
\"This book describes how newly emerging Artificial Intelligence (AI) technologies will provide unprecedented opportunities to penetrate technology and automation into everything we do, and at the same time, provide a huge playing field for businesses to develop newer models to capture market share. It establishes a milestone in understanding global transformational changes occurring in the manufacturing and corporate world due to AI and tries to find powerful and sophisticated solutions that will improve and streamline operations. Reinventing Manufacturing and Business Processes through Artificial Intelligence will be of interest to students, researchers, and professionals of the AI community as well as interdisciplinary researchers\"-- Provided by publisher.
Book
Box-Behnken modeling to optimize the engineering response and the energy expenditure in material extrusion additive manufacturing of short carbon fiber reinforced polyamide 6
The field of production engineering is constantly attempting to be distinguished for promoting sustainability, energy efficiency, cost-effectiveness, and prudent material consumption. In this study, three control parameters (3D printing settings), namely nozzle temperature, travel speed, and layer height (L
H
) are being investigated on polyamide 6/carbon fiber (15 wt%) tensile specimens. The aim is the optimum combination of energy efficiency and mechanical performance of the specimens. For the analysis of the results, the Box-Behnken design-of-experiment was applied along with the analysis of variance. The statistical analysis conducted based on the experimental results, indicated the importance of the L
H
control setting, as to affecting the mechanical strength. In particular, the best tensile strength value (σ
B
= 83.52 MPa) came from the 0.1 mm L
H
. The same L
H
, whereas caused the highest energy consumption in 3D printing (E
PC
= 0.252 MJ) and printing time (P
T
= 2272 s). The lowest energy consumption (E
PC
= 0.036 MJ) and printing time (PT = 330 s) were found at 0.3 mm L
H
. Scanning electron microscopy was employed as a part of the manufactured specimens’ 3D printing quality evaluation, while Thermogravimetric analysis was also conducted. The modeling approach led to the formation of equations for the prediction of critical metrics related to energy consumption and the mechanical performance of composite parts built with the MEX 3D printing method. These equations proved their reliability through a confirmation run, which showed that they can safely be applied, within specific boundaries, in real-life applications.
Graphical abstract
Journal Article
Cost analysis of electronic systems
This text provides an introduction to the cost modeling for electronic systems that is suitable for advanced undergraduate and graduate students in electrical, mechanical and industrial engineering, and professionals involved with electronics technology and development and management.
Book
Geometrical process design during continuous generating grinding of cutting tools
Modern cutting tools like end mills, drilling tools, and reamers underlie high requirements regarding geometrical accuracy, cutting edge quality, and production costs. However, the potential for process optimization is limited due to the process kinematics during grinding. Consequently, a novel tool grinding process for the manufacture of cutting tools has been developed recently at the Institute for Production Engineering and Machine Tools (IFW). This continuous generating grinding process allows the simultaneous production of all flutes and circumferential flank faces of rotational symmetrical cutting tools. The present paper focuses on the geometrical process design and develops a method to determine the necessary basic rack and process parameters in order to create a desired cutting edge geometry by continuous generating grinding. The developed method can define all parameters with an accuracy of up to 5 µm and 0.2° within a simulation in five iteration steps and allows not only the quantitative design of the cutting tool geometry but a qualitative modification of the flute geometry as well. Subsequently performed grinding tests showed that the presented method allows the design of grinding worms for continuous generating grinding of cutting tools and enables the successful implementation of these processes.
Journal Article