Integrated computational product and production engineering for multi-material lightweight structures

Within product development processes, computational models are used with increasing frequency. However, the use of those methods is often restricted to the area of focus, where product design, manufacturing process, and process chain simulations are regarded independently. In the use case of multi-material lightweight structures, the desired products have to meet several requirements regarding structural performance, weight, costs, and environment. Hence, manufacturing-related effects on the product as well as on costs and environment have to be considered in very early phases of the product development process in order to provide a computational concept that supports concurrent engineering. In this contribution, we present an integrated computational concept that includes product engineering and production engineering. In a multi-scale framework, it combines detailed finite element analyses of products and their related production process with process chain and factory simulations. Including surrogate models based on machine learning, a fast evaluation of production impacts and requirements can be realized. The proposed integrated computational product and production engineering concept is demonstrated in a use case study on the manufacturing of a multi-material structure. Within this study, a sheet metal forming process in combination with an injection molding process of short fiber reinforced plastics is investigated. Different sets of process parameters are evaluated virtually in terms of resulting structural properties, cycle times, and environmental impacts. © 2020, The Author(s)

Evaluación del sistema de mantenimiento de la máquina de inyección de plástico mediante el método TOPSIS

The following document talks about the parameters of the maintenance system implemented in plastic injection machines of a maquiladora company located in Ciudad Juarez Chihuahua. The method used in this investigation is essentially ulti-criteria decision making, the technique for the order of preference for similarity to the ideal TOPSIS solution. Therefore, an evaluation of the maintenance system is carried out, using measurable collected in said company. With this evaluation the plastic injection machine is determined in better conditions, as well as the machine in worse conditions, which helps management to make better decisions regarding each machine. The objective of this research is to address the study of decision-making in the industrial field specifically in the maintenance system of plastic injection molding machines.El siguiente documento habla sobre los parámetros del sistema de mantenimiento implementado en las máquinas de inyección de plástico de una empresa maquiladora ubicada en Ciudad Juárez Chihuahua. El método utilizado en esta investigación es esencialmente la toma de decisiones multicriterio,  la  técnica  para  el  orden  de  preferencia  por  la  similitud  con  la  solución  ideal  de  TOPSIS.  Por  lo  tanto,  se  realiza  una  evaluación  del  sistema  de  mantenimiento,  utilizando datos medibles recogidos en dicha empresa. Con esta evaluación se determina la máquina de inyección de plástico en mejores condiciones, así como la máquina en peores condiciones, lo que ayuda a la dirección a tomar mejores decisiones respecto a cada máquina. El objetivo de esta investigación es abordar el estudio de la toma de decisiones en el campo industrial específicamente en  el  sistema  de  mantenimiento  de  las  máquinas  de  moldeo  por  inyección  de  plástic

AN APPROACH TO INVERSE MODELING THROUGH THE INTEGRATION OF ARTIFICIAL NEURAL NETWORKS AND GENETIC ALGORITHMS

A hybrid model integrating predictive capabilities of Artificial Neural Network (ANN) and optimization feature of Genetic Algorithm (GA) is developed for the purpose of inverse modeling. The proposed approach is applied to Superplastic forming of materials to predict the material properties which characterize the performance of a material. The study is carried out on two problems. For the first problem, ANN is trained to predict the strain rate sensitivity index m given the temperature and the strain rate. The performance of different gradient search methods used in training the ANN model is demonstrated. Similar approach is used for the second problem. The objective of which is to predict the input parameters, i.e. strain rate and temperature corresponding to a given flow stress value. An attempt to address one of the major drawbacks of ANN, which is the black box behavior of the model, is made by collecting information about the weights and biases used in training and formulating a mathematical expression. The results from the two problems are compared to the experimental data and validated. The results indicated proximity to the experimental data

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154105/1/kampfner1997.pd

Automotive Industrial Supply Chain Performance Evaluation under Uncertain Constraints on Cloud Computing System

Performance evaluation is a critical and complex task as well as uncertain demands for automotive supply chain. Several methods are applied and adopted to deal with current situations and maintain competitiveness such as fuzzy logic, neuro fuzzy, agent (multi) based evaluation, etc. However, such systems are not rapid enough to respond customer requirements by real-time on mobile cloud computing system. There are many companies that operate under the first tier company as subcontractors on the same goal. Cloud computing system is capable to monitor real-time production processes for every subcontractor to assist the 1st tier to make decision and respond customer effectively. Daily monitoring data of all subcontractors in the supply chain are stored in the central database and finally the performance evaluation can be done. The implication is cost reduction of the whole supply chain and increase competitiveness as well as continuous process improvement for all

Optimization and cost evaluation of RTM production systems

In recent years, applications of composite materials have had significant growth in many industrial sectors. Light weight and high mechanical properties of composites supported by efficient manufacturing technologies such as resin transfer molding (RTM) make them better alternatives to metal products in several applications. Cost analysis of composite manufacturing processes is important to increase their manufacturing competencies. Cost reduction of composite manufacturing processes offsets their high material cost drawback. Thus a competent manufacturing process, along with outstanding mechanical properties, makes composites desirable materials of choice. A comprehensive production cost analysis for a hypothetical but realistic RTM manufacturing line is performed in this research. An optimized plant configuration is determined based on production volumes, resource utilization and material handling policies. Three different cases are studied to show how cost per item and profit values of the production behave on different production levels. In the first case production of a single product is studied while in the second and third cases two different products are assumed to be produced utilizing common facilities. An algorithm is proposed to search for optimal combination of production volumes of different products utilizing the common preconfigured production system. Cost fluctuations on different production volumes are analyzed to identify different factors which might influence the cos

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Industry/University Collaboration at the University of Michigan-Dearborn: A Focus on Relevant Technology

https://deepblue.lib.umich.edu/bitstream/2027.42/154106/1/kampfner1998.pd