Production engineering

About: Production engineering is a research topic. Over the lifetime, 2657 publications have been published within this topic receiving 37409 citations.

A project scheduling approach to production and material requirement planning in Manufacturing-to-Order environments

Abstract: Long- and medium-term production planning are tools to match production orders with resource capacity and that can also be used as a baseline for material procurement. The lack of a detailed schedule for the manufacturing operations, however, may cause difficulties in providing a proper material requirements planning and may affect the feasibility of the production plan itself. This paper proposes an approach, based on production process knowledge, to extract scheduling information from an aggregate production plan in order to support material procurement. The proposed approach is applied to an industrial case involving machining center production.

Introduction to FACTOR/AIM

Abstract: FACTOWAIM (AIM) is a simulation system designed specifically for use in manufacturing decision support. AIM has been successfully applied to engineering design, scheduling, and planning problems within numerous manufacturing organizations. AIM operates on the Windows platform and all model data is stored in a Microsoft Access@ database. This open database structure provides many opportunities. Models can be built faster by importing data from existing sources into AIM. Custom reports are easily created with Access@ report wizards. In addition, simulation based decision support applications complete with menus and dialog boxes can be developed with Access@ application wizards. Unlike language-based simulation products which require modelers to learn specific syntax and then abstract your system to fit this syntax, AIM uses the language of manufacturing. Example AIM components include machines, operators, materials, parts, jobsteps, process plans (routings), and conveyors. In addition, a comprehensive set of pre-defined manufacturing rules is available to you. Using AIM you can quickly and accurately build a model of any manufacturing process on your PC. Spending less time on modeling means more time to use the model to help you make decisions to improve your manufacturing operations. This paper provides an introduction to AIM including AIM modeling constructs, the use of AIM for capacity engineering, planning and scheduling, and costing with AIM. 1 USING AIM TO MAKE MANUFACTURING DECISIONS AIM is designed to help you make decisions regarding your manufacturing organization’s productive capacity. When you build an AIM model, you develop a more thorough understanding of how your system operates and its capacity. You can use the model to investigate a variety of issues, for example to determine the impact of a proposed change, without affecting production. This enhances your ability to manage the system, control its capacity, and make better decisions regarding its operation. Which in turn improves profitably and your ability to predictably deliver quality product to your customers. These issues of predictability, profitability, and quality face every manufacturing organization today. Figure 1 shows the functional breakdown of the capacity management decision areas. Figure 1 : Capacity Management Decision Areas Engineering problems focus on long-term questions regarding system design and continuous improvement. Planning problems address capacity issues including evaluating the impact of changing product mix or demand. Scheduling problems seek solutions to daily issues including on-time order completion, priority changes, and unplanned changes in resource availability. Most products focus on one capacity management decision area. With AIM you build a single model and use it to support engineering, planning and scheduling

Design of dynamic multi sensor systems

Abstract: Recent increase in machine automation, machine capability, and the performance of machining processes lead to high demands on process monitoring systems. In the Center of Production Engineering in Hannover Gentelligent® manufacturing systems are researched, which will be able to meet those requirements. One enabling factor for these systems is the “feeling” capability, achieved by multi sensor systems. This paper copes with the design of these systems. This includes on the one hand the development of sensor configurations and on the other hand a concept for sensor fusion. For the search of optimum sensor configurations a previously developed algorithm is extended to consider the dynamic behavior of the sensor systems.

Agricultural Process Engineering

Abstract: Agricultural process engineering , Agricultural process engineering , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

Cyber-Physical Systems Engineering for Manufacturing

Abstract: There is a convergence of interests in cyber-physical systems, systems engineering, and manufacturing innovation in the United States. The U.S. National Institute of Standards and Technology (NIST) has undertaken research programs in smart manufacturing systems to address many of the standards and measurement science issues that arise from this convergence. This chapter describes the convergence, the progress made thus far in the NIST programs in smart manufacturing systems, and the challenges that drive further research.