Showing papers on "Production engineering published in 2021"

Optimal Scheduling of Human–Robot Collaborative Assembly Operations With Time Petri Nets

Abstract: The novel paradigm of collaborative automation, with machines and industrial robots that synergically share the same workspace with human workers, requires to rethink how activities are prioritized in order to account for possible variabilities in their durations. This article proposes a scheduling method for collaborative assembly tasks that allows to optimally plan assembly activities based on the knowledge acquired during runtime and so adapts to variations along the life cycle of a manufacturing process. The scheduler is based on time Petri nets and the output plan is optimized by minimizing the idle time of each agent. The experimental validation carried out on a realistic industrial use-case consisting of a small assembly line with two robots and a human operator confirms the effectiveness of the approach. Note to Practitioners —The optimization of manufacturing execution is a long standing problem in production engineering. Modern engineering tools are available to monitor and help decision-makers to reduce waste and schedule resources to optimize the efficiency of a manufacturing process. This article proposes a scheduling algorithm that continuously collects data from the manufacturing process and iteratively plans an optimal resource allocation strategy, trying to reduce the idle time of each agent. The approach is demonstrated on a realistic case study, where two robots and a human worker cooperate to assemble a USB/microSD adapter.

Classification of research and applications of the computer aided process planning in manufacturing systems

Abstract: The Computer Aided Process Planning (CAPP) systems are recently developed in manufacturing engineering to provide links between Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) systems. The CAPP systems are developed by considering the different issues of computer applications in production engineering. Optimization techniques can be applied to the CAPP to increase efficiency in part production processes. The energy consumption of part production process can be analyzed and optimized using the CAPP systems in order to increase added value in the part manufacturing process. Also, artificial neural networks as well as cloud manufacturing systems can be applied to the CAPP systems to share advantages of the different CAPP systems in different industry applications. Flexible process planning systems are developed using dynamic CAPP in order to cope with product varieties in process of part production. To develop potential energy saving strategies during product design and process planning stages, the advanced CAPP systems can be used. In this paper, a review of Computer Process Planning systems (CAPP) is presented and future research works are also suggested. It has been observed that the research filed can be moved forward by reviewing and analyzing recent achievements in the published papers.

The Use of Principal Component Analysis and Logistic Regression for Cutter State Identification

Abstract: It is well known that due to Industry 4.0 requirements and challenges, future research directions in production engineering will focus on the creation of intelligent sensors and their integration by means of intelligent platforms. Therefore, the key skill will be appropriate analysis and processing of signals recorded by these sensors, which may relate to manufacturing process parameters. The application of principal component analysis and logistic regression enables effective data processing. This has been shown using a real-world numerical example - the data related to cutter state identification based on signals generated during machining. This way, it has been proven, that the above methods may find practical application in condition monitoring systems. In particular, they may be highly helpful in real-time cutter state identification.

Machine learning and simulation-based surrogate modeling for improved process chain operation

Abstract: 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.

Automating production engineering for custom and small-batch production on the basis of simulation modeling

Abstract: The paper describes a simulation model of production engineering for custom and small-batch production. The described simulation model includes three levels: control over the operation of separate elements of the production system and their interaction, planning and modeling of various variants of a processing route, selection of rational cutting parameters, and constraints analysis of the production system. In order to determine the rational cutting modes, total accuracy of processing is calculated and Taguchi method is applied. A rational variant of the production process is selected by multiple criteria analysis.

Prediction of energy consumption in the Industry 4.0 platform- solutions overview

Abstract: For a long time, scientific and technical work has been focused on production management, which affects both the correctness of the process and the costs generated. One of the integral elements of the production process management is energy, which has an impact on the organization of work, operation of machines or production. Predicting the energy consumption of smart facilities is crucial for implementing energy-efficient management systems, the area of this problem is a key aspect of smart grids whereby loads must be planned in real time. One of the main tasks of intelligent systems is to optimize the energy demand and costs to maximize energy efficiency of the facility. According to forecasting requirements, the following article presents several approaches to prediction of energy consumption models for production engineering systems. The proposed models were adopted and analyzed in terms of their usability and were trained and validated with the use of real data collected from the electrical installation of some company using the APA IPOE system.

How to build a linkage between high quality assurance production system and production support automated system

Abstract: To maintain the planned production volume for potential disaster, the Japanese manufacturing industry needs to develop and rebuilt the global product that is to strengthen QCD (Quality, Cost, and Delivery). The keys to fulfilling this need are automated facility, human skills to operate the facility (production operator), and a production system incorporated with production data systems that activate the facility and human system subject to each overseas plant conditions. Therefore, the author has created a new system that ensures the necessity of building a new production system for global production, eliminates ambiguity among the processes of production planning, production preparation, production and process control, and formalizes and builds the linkage among the processes. This paper is to reveal the effectiveness of the above-specified objectives of the newly created systems. Especially the highly accurate robot production system has been tested and confirmed at Toyota Motor Corporation.

Teaching-Learning Strategies to Production Planning and Control Concepts: Application of Scenarios to Sequencing Production with Virtual Reality Support

Abstract: This paper aims to present scenarios to be applied in higher education to the theme of production planning and control, addressing factors of the production system and indicators arising from this process and the application of virtual reality to support the process. The applied method combines the development of six scenarios for virtual reality application and the discussion about the impacts in indicators from the production planning and control, for example, inventory in the process, manufacturing lead-time, use of equipment, and punctual delivery attendance. Findings revealed that the teaching-learning process of production planning and control, when applied through scenarios, generates opportunities for students to learn the impact in the indicators. The virtual reality in this environment supports creating differentiated teaching-learning environments to generate the most significant knowledge for students which positively impacts the future in the world of work. In addition, it allows people involved in the teaching-learning processes of production engineering to apply the concepts presented in the sequencing process, lean about the impacts of decisions on production sequencing indicators and appreciate the support of virtual reality to generate an environment more cognitive for students. https://doi.org/10.26803/ijlter.20.8.7

Achieving parametric transparency in model-based factory planning

Abstract: The planning of new factories, as well as the re-planning of existing factories, has become more frequent due to increasingly changing business requirements, as for example shorter product life cycles and Industry 4.0. A higher number of involved planners and the resulting high amount of planning information strongly require coordination. In this context, the importance of Building Information Modeling (BIM) in factory planning rises as it provides a method of integrated building planning and planning validation by means of 3D software and object-oriented modelling. However, despite the use of BIM, there are still major interface problems in factory planning that cannot be solved by the still manual plausibility checks of non-geometrical planning information. To enable automatic checking of planning results, thereby improving the BIM-based factory planning process, machine-readable explication of the parametric dependencies are required between different planning fields such as production planning and building planning. The goal of this paper is to show parametric and thus non-geometric dependencies that exist between the sub-models of BIM-based factory planning in such a way that software agents can automatically evaluate this design information. Within the planning interface between production planning and building planning, the paper focusses on the particular exchange between the planning of the manufacturing system and the planning of a cutting fluid pump. With the involvement of domain experts from factory planning, systems engineering and production engineering, we as the authors have managed to develop a coherent system of block diagrams, constraint diagrams and parametric diagrams that explicate the focused interface in a machine-readable manner. We believe our accomplishments are an essential element for completely automated planning validation in BIM-based factory planning and general object-oriented modelling in the future.

Identifying the Thematic Trends of Model Based Systems Engineering in Manufacturing and Production Engineering Domains

Abstract: Manufacturing and production systems have become increasingly complex in the past decade to meet the competitive demand in a growing industry. As these systems grow in complexity and flexibility, there is a need for efficient management and analysis of these systems. Model-based systems engineering (MBSE) addresses the complexity inherent with systems development with a model-centric approach that supported tailored modeling languages, methods and tools. This paper identifies the thematic evolution and trends and relationships found in the use and application of MBSE specifically in the manufacturing and production engineering domain. A collection of 471 published article from Institute of Electrical and Electronics Engineers (IEEE) and Science Direct over the past decade were used for the analysis using text mining techniques. Due to the limitation on the access to full text information of all the articles identified, only abstracts were considered for analysis. This effort helps the researchers across the domain to explore the reason behind and understand the change of the thematic perspectives of MBSE application over the last decade. In addition, the finding of the growing interest in addressing the aspects of complexity and systems requirements, and on the aspects of the use of MBSE for identifying and addressing the challenges related to Cyber Physical Systems help in paving a path for future research.

Design Guidelines for the Evolutionary Integration of Physical Behaviour Models into Plant Simulations for Production Engineering

Abstract: Currently, validation during the planning phases of smart production plants is approached very domain-specific. Interdisciplinary use of validation models is rare. Instead, models are often optimized for their single purpose. On the basis of a semi-formal analysis, a model integration concept, which spans different validation phases is proposed. A model for a production station is build based on "Component Behaviour Models" for mechatronic components, which are integrated into a plant simulation. In this publication, a mechanical interface for such integration is proposed and different interface concepts are discussed in order to establish guidelines for the model combination and integration depth, enabling an evolutionary introduction of this concept in practical applications.

Technology Transfer from a Tacit Knowledge Conservation Model into Explicit Knowledge in the Field of Data Envelopment Analysis

Abstract: This work presents a preservation model of production engineers’ tacit knowledge of Data Envelopment Analysis (DEA). Their expertise was explicitly coded into a computer system, and the model was developed by applying techniques and procedures from the fields of engineering and knowledge management. Technology transfer enables to solve the problem of selecting criteria and interpreting results with DEA techniques, when the efficiency of similar organizations is compared using an efficient frontier derived from non-parametric approximations of such techniques. Misunderstanding the techniques leads to misinterpretations of DEA results. This model was created by applying Knowledge Engineering, which enables to preserve and extend specific experiences and expertise in time by means of computer solutions. The model had an efficient and positive impact on strategic self-learning processes for the community interested in production engineering, knowledge transfer and management.

Analysis of the stability of stationary technological transfer processes in the oil-gas and mechanical engineering areas of production

Abstract: The work examines the imitation analogue of the degenerating differential equation that arising from the study of continuous Markov processes in stationary systems closely connected to mathematical models of oil- gas processes in mechanical engineering production. The study of the classes of such equations is associated with the diffusion processes of many areas of mechanical engineering and industrial production, in the case when under approach at the boundary of the domain changes of spatial variable weight coefficient of the equation tends to zero. This means degeneration of the equation, and in applications it is a change in the nature of the diffusion process near the boundary: there is a need to change the technological process and, as a result, changes in production engineering. The solution to the equation is considered in a function class with values in the Hilbert space. A priori estimates of the solution in the spaces of abstract functions at semi-infinite interval have been obtained. A priori estimates of the solution of the Dirichlet problem has been proved. Under certain conditions on the nature of degeneration is established the uniqueness solution to this problem. An effective algorithm has been developed, on the basis of which a computer program has been formed for numerical analysis of the problem. The results are used to automation mechanical engineering production at computation and simulation modeling of diffusion processes, as well as in the formation of a complex of technological operations of production.