Showing papers on "Production engineering published in 2019"

Engineering Materials, Research, Applications and Advances

Abstract: Introduces Emerging Engineering MaterialsMechanical, materials, and production engineering students can greatly benefit from Engineering Materials: Research, Applications and Advances. This text focuses heavily on research, and fills a need for current information on the science, processes, and applications in the field. Beginning with a bri

Classification of forecasting methods in production engineering

Abstract: Abstract Business management is a continuous decision-making process. It is difficult to imagine a company that does not use forecasting techniques. Even small enterprises without relevant forecasting departments more or less consciously anticipate future events, forecasting the volume of production and setting directions for development. Today’s production companies must quickly adapt to changing customer requirements, implementing structural and technological changes and delivering projects related to the production of new products. Under the dynamically changing conditions, the functioning and effective management of modern enterprises depend on future-oriented information. This increases the validity of forecasting. This article aimed to identify forecasting methods and areas of their use in production engineering. The publications on this subject were reviewed in the Scopus database, using the time frame from January 1970 to June 2018. An original classification of research subareas was created using VOS viewer software, and then, a bibliometric map was developed to visualise the results of the word coexistence analysis. The analysis of the co-occurrence and co-classification of words made it possible to indicate research subareas of forecasting in production engineering and related emerging research areas and issues.

VR and AR in Lean Manufacturing Classes

Abstract: The paper presents preliminary results of research on integrating Virtual and Augmented Reality in Production Management and Lean Manufacturing classes. The aim of the classes is to familiarize students Production Management and Lean Manufacturing tools which are commonly used in many manufacturing enterprises. Two applications (VR training application and AR assembly instructions) were tested by a number of students of Production Engineering. The results suggest that both applications can improve the process of assembly operations as well as LM classes.

Survey on learning concepts applying creativity methods in education and industry

Abstract: Mechanical Engineering students acquire knowledge and skills in engineering design through several courses reaching from learning how to setup technical design drawings, CAD-courses, several courses dealing with machine elements and numerical analysis (FEM). In Mechanical Engineering the percentage of courses on engineering design covers, depending on the specific studies, from 10% in production engineering to 25% in engineering design of the overall credits. At the DHBW Engineering Design students are additionally encountered with a course on systematic approaches and processes (e.g. time to market process, agile project management, design thinking), diverse methods to facilitate and strengthen efficiency in the design and development process. In the last few years new concepts of education were implemented: Additive manufacturing allows the integration of prototyping, e-learning platforms allow the placement of pre- and post-educational content (blended learning) and therefore support open cooperation between students and lecturer. This paper reflects experiences in teaching creativity methods, e.g. brainwriting, brainstorming or TRIZ. It compares ‘classic’ lectures where methods are described by the lecturer (‘teacher-centred’ learning) to a concept of specific exercises, where students are asked to apply the methods in teams. This rather ‘student centred’ learning concept allocates time to discuss the methods and experiences made by applying them in class. At a later point in the learning process students get more information, reflective views and discussion points through an e-learning platform. In order to evaluate the students’ perception of the learning concepts a questionnaire-based survey was conducted among altogether 150 students in four courses, three Bachelor courses of different Engineering programmes and a Master course. The survey evaluates 1. which learning concept according to the students helps to broaden systematic and methodical creativity skills best, 2. which creativity methods students consider to be applicable in industry and 3. if these methods are already applied in the students’ companies. The latter point can be analysed as every student at DHBW Baden-Wuerttemberg Cooperative State University compulsorily has a contract with a company and spends half of the studies in on projects in industry. Thus, the paper gains valuable insights into approximately hundred companies of different branches and sizes. Besides the conceptual description of the learning approach on methodical skills and the survey itself, the paper also furnishes an overview of best-practice examples that aim at helping students to engage with the Engineering Design process, focussing in particular on supporting methods and their application in industry.

Simulation of Production Flow : A simulation-based approach to evaluate and optimize future production scenarios

Abstract: This master thesis is the last part of the master program Production Engineering and Management at the Royal Institute of Technology, KTH, in Stockholm. The thesis is conducted at Exeger Operations ...

Manufacturing Engineering Requirements in the Early Stages of New Product Development — A Case Study in Two Assembly Plants

Abstract: The interface between the product development function and the manufacturing function is one key dimension in new product development (NPD) projects. Hard and soft requirements for manufacturability are defined and communicated to product development teams early in the NPD project to ensure the new products are fit for the manufacturing system. In this chapter, we determined what requirements are important for a manufacturing system and how these requirements are handled during an NPD project by analyzing two industrial cases. The results showed that requirements communicated from the manufacturing function to the design function had different sources and effects on different aspects of the manufacturing system. They were communicated and integrated to various degrees and through various mechanisms. There was a tendency to rely on the personal and verbal communication of requirements, as opposed to using more formal structured methods. This way of working was sufficient when product change was incremental and not radical. The case studies showed that the manufacturing function needed to employ more efficient methods to define and communicate their requirements in large and complex NPD projects.

Increasing the Value of Data in Production Systems

Abstract: A digital transformation is taking place, where information is available about almost anything, changing how work is performed and anticipated. More digitized information enabled by the digital technologies is supporting businesses to measure more about their processes and thereby also to know more. The same transformation is taking place in the manufacturing domain, which is referred to as the fourth industrial revolution. There are numerous national initiatives to approach the fourth industrial revolution and the aim is to make the manufacturing industry more digitalized and increase competitiveness. Digitalization is making more information about processes available, but it is first when data is informing decisions in an organization it will add value. Along with all the benefits and potential values of the digital transformation, much of the attention has been on the technologies and systems that can enable the digitalization. Less focus has been spent on how the technologies and systems should be put into practice in the organizations to fulfill the needs of the manufacturing domain. New knowledge about digital technologies in combination with already existing expertise about manufacturing processes is needed. The aim of the thesis is to identify the value of data for decision-making. The approach outlined in this thesis will identify the values gained from the raw data itself and from the further processed data to provide decision support. The distinction between these two forms of data, raw data and further processed data, is important because it is believed that these can provide different values and that they involve different challenges for the organization. 5G telecommunication and 3D laser scanning serve as digital technologies in this thesis to enable more data in digital form on a production system. 5G was used for connecting a machine enabling the collection of data about critical machine components. 3D laser scanning was used to collect the spatial data in a factory environment. The results show that more data available about the connected machine provide values to the organization to know the status of the machine, be able to compare the designed system against the behavior in the real-world setting, a better understanding of the process and to learn from data. Spatial data provide values by being able to represent the production system as-is in a very accurate and photorealistic way. The values identified from having more data available for the decision support were in the daily operations to know the condition of the machine, for the manufacturing organization to plan proactive actions, and for the production engineer to understand the behavior of the designed system in the real-world context. The spatial data could both support when making changes to the physical setup and when planning the design of the factory environment in an offline mode. The initial studies presented in this thesis supported to build the understanding of the current practice of data as decision support in the production organization. The understanding that data should support decision-making was high, but the data availability in the current state was scarce or of poor quality. This strengthens the aim of the thesis, to provide results that can show the value of data for decision-making. “To measure more is to know more” (McAffee and Brynjolfsson, 2012) is a statement serving as a cornerstone throughout this thesis and has also been justified by the results presented to answer research question 1 and 2. Data enabled by digital technologies can support multiple roles in the manufacturing organization throughout the different phases of the production system, for example in daily operations and maintenance.

Built-in lean management tools in simulation modeling

Abstract: The paper presents an approach for integrating Lean management tools, value-added analysis and Yamazumi charts, with simulation modeling in order to analyze the behavior of certain types of production systems. This is accomplished through the development of a set of high-level, operational, task-oriented instructions that are based on production engineering means and language. This is in contrast to the more traditional simulation modeling approach that uses lower-level, logic-oriented instructions that are based on the means and language of computer programming. The paper defines the approach and its implementation and its application is illustrated through a simple manufacturing cell example.

Production Controlling in the Digital Age

Abstract: This textbook is written for academic courses and practicing professionals. For the purpose of academic courses, the book is written as a core text for graduate and postgraduate students who want to acquire in-depth knowledge of the production controlling concept. For specialists like controllers, analysts, accountants, auditors, and other similar professions, the book provides an opportunity to apply their knowledge of the methodology that is to be used in strategic and operational process controlling. The five chapters of the textbook describe the essential approach to production controlling adapted to the needs of both students and professionals. Chapter 1 introduces the technological basis – Big Data. Cyber- physical systems are introduced for the purposes of business model creation and controlling. The introduction covers the impact of digitalization, and Chapter 2 describes the concrete concept of smart production, new forms and benefits of industrialization, business model in relation to smart production and economic dimension of Big Data, and the role of controllers. Chapter 3 discusses key performance indicators and a ratio system that can be used for controlling purposes. Chapter 4 deals with production process controlling starting with their components and determinants as success factors. Strategic and operational process controlling is described in detail by the methods and key performance indicators of value stream controlling. Production business model controlling is covered in the fifth chapter in which methodology is presented as a combination of process-oriented production controlling, supply chain controlling, and corporate controlling. Business model controlling methodology is based on innovation in production engineering and computer science, which calls for powerful IT business applications.

Robust optimization in production engineering - methods and application

Abstract: Methods that use robust optimization are aimed at finding robustness to decision uncertainty. Uncertainty may affect the input parameters (problem) and the final solution. Robust optimization is applicable in many areas, such as: operational research, IT, energy, production engineering and others. The aim of the work was to indicate the main methods and examples of applications of robust optimization in the area of production engineering. Documents (articles and proceedings paper) indexed in the Web of Science - Core Collection database (WoSCC) from 2014-2018 were used for analysis. The search has been limited to the WoS-CC category: Engineering Industrial and Engineering Manufacturing. The main areas of application were: the scheduling of projects and tasks, production planning, and risk management. The most common methods were: linear programming, evolutionary algorithms, mixed integer programming, dynamic programming and many others.

Construction method of space-time constraint model for mining production plan compilation

Abstract: The invention discloses a construction method of a space-time constraint model for mining production plan compilation and particularly relates to the field of mining production plan making. Accordingto the method, based on WBS of underground mine excavation production, the logic relation between an excavation production engineering project and procedures is analyzed, and a space-time constraint logic model of the excavation production engineering project is researched and constructed and comprises an excavation engineering project space constraint model PSCM and a time sequence constraint model PTCM; and according to the construction method and process flow requirements of the engineering project, a time sequence constraint logic model PWTCM of the working procedure of the mining engineering project is researched and constructed. According to the model, the technical requirements of a mine excavation production design scheme and a technological process are deeply analyzed. NPT and spatial topological sorting technologies and the like are adopted, a logic model for describing the space-time constraint relation of mining production in a formalized mode is provided, preconditions areprovided for mining production plan making, and theoretical and technical foundations are laid for automatic decomposition and automatic sorting of mining production tasks.

Computer simulation in identification and elimination of waste in the production process

Abstract: This paper lays the theoretical foundations for the characterisation the course of processes in production engineering by means of computer tools, with the focus on the essence and benefits resulting from the use the programmes in question. The main objective of this study is to employ a selected computer tool for modelling, analysing and simulating a selected product manufacturing process to identify areas in the process that require improvement. The work involved the Tecnomatix Plant Simulation programme, in which the existing production process was modelled. The created model was used to simulate the runtime of the production lot, identify bottlenecks and analyse production losses, such as: waiting, stocks, and unnecessary transport. The conducted analysis has produced an outcome in the form of methods for eliminating identified production losses and modifying the model. The simulations were subsequently carried out on a modified model, which allowed determining the level of improvement of the assumed indicators, e.g. order completion time, set-up time, waiting time, stocks. The selected tool served not only as a means to visualising the course of the manufacturing process but also enabled us to optimise and improve it. The article presents the possibilities of using simulation programmes to identify and eliminate waste in production processes. In addition, the conclusions show not only the results of the simulations but also the most important benefits resulting from the use of this type of tools in production engineering, in particular in lean production management.

Automated & Enriched Reservoir Inflow Performance Relationship Expedites Massive Wells Diagnosis to Assist Production Engineering Decision Making, Maximize Efficiency and Overall Asset Performance

Abstract: Enriched Reservoir Inflow Performance Relationship (ER-IPR) is a novel, expert and intuitive surveillance tool to manage by exception underperforming wells, using smart analytics for well problem classification, prescribe possible remedial actions or opportunity generation as well as predicting wells that potentially will cease to flow, exploiting smart field architecture capabilities by adding value to big data through meaningful visualization plots. Although existing intelligent systems (IS) updates massive amount of well models automatically, engineers require high expertise and time to understand well performance, identify root cause problem and capitalize opportunities. The ER-IPR is a holistic approach that display multiple well operating-points in one normalized reservoir performance curve that systematically drives users to identify underperforming wells, along with their associated problems and give insights of the reservoir management strategy by implementing simple four steps: 1) Obtain all well operating-points from IS. 2) Generate the ER-IPR. 3) Execute expert diagnostic module. 4) Perform short-term production forecast for the highlighted problematic wells using Integrated Asset Model. The tool was tested in a Digital Oil Field (DOF) in two different reservoirs. One reservoir is an anticline with a strong compositional gradient starting from gas condensate to black oil with saturation pressure and GOR varying in a wide range. The other one is an undersaturated reservoir under peripheral water injection with very stable bubble pressure and GOR across all areas. ER-IPR facilitates reservoir/sector/well performance review, providing proper well diagnostic evaluation as well as recommending possible remedial action based on analytics that consider the physics phenomenal of the production system and user performance acceptance limits according to field real time data observation. ER-IPR tool automatically compares, classifies and visualizes all wells simultaneously, translating big data in effective recommendations to sustain/increase production. ER-IPR minimize the number of inactive strings by proactively identifying wells that potentially will cease to flow, prioritizing well remedial actions, considering production forecast analysis and resources. ER-IPR enables proactive surveillance and monitors well performance thought nodal analysis over time, to have fully well behavior assessment and instantaneous asset awareness.

Continuous Improvement in Education: Adaptation of Kaizen Philosophy on the Example of the Student Project AGH Leanline

Abstract: The current study purpose is o highlight the creation and execution of a project that will teach effectively and practically the knowledge of production engineering and will create an opportunity to acquire key competencies and attitudes on the market while still at university. This research uses the descriptive and qualitative approach. Students Research Group Management’ runs a project which assumes optimization of production and logistic processes on the basis of identification of waste and implementation of Lean Manufacturing tools. The production line made of lego blocks - AGH LeanLine is an original undertaking of students of the AGH University of Science and Technology, adapted to practice and experience Lean methods, tools, and principles in the university environment. Each production process simulation is a Kaizen Workshop, during which the losses occurring in the basic model are defined and then eliminated from the value stream with the help of known methods of process organization. Such training is an active passage through the Plan Do-Check-Act (PDCA) cycle, taking up and testing all the activities included in each stage. Thanks to such projects, students experience the practical application of theory and are ready to take such actions on a living organism a production company.