To ensure the use of energy produced from renewable energy sources, this paper presents a method for consumer planning in the consumer–producer–distributor structure. The proposed planning method is based on the genetic algorithm approach, which solves a cost minimization problem by considering several input parameters. These input parameters are: the consumption for each unit, the time interval in which the unit operates, the maximum value of the electricity produced from renewable sources, and the distribution of energy production per unit of time. A consumer can use the equipment without any planning, in which case he will consume energy supplied by a distributor or energy produced from renewable sources, if it is available at the time he operates the equipment. A consumer who plans his operating interval can use more energy from renewable sources, because the planning is done in the time interval in which the energy produced from renewable sources is available. The effect is that the total cost of energy to the consumer without any planning will be higher than the cost of energy to the consumer with planning, because the energy produced from renewable sources is cheaper than that provided from conventional sources. To be validated, the proposed approach was run on a simulator, and then tested in two real-world case studies targeting domestic and industrial consumers. In both situations, the solution proposed led to a reduction in the total cost of electricity of up to 25%.

Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with "fail-sating" or "mistake-proofing". The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

The paper presents a method based on the neural networks to study of working conditions, for the workstations from the manufacture industry. The neural networks were chosen because they excel in gathering difficult non-linear relationships between the inputs and outputs of a system. The neural network was simulated with Matlab. In this paper, we considered as relevant for the study of working conditions, 6 input parameters: temperature, humidity, noise, luminosity, load and frequency. The neural network designed for the study presented in this paper has 6 input neurons and 3 neurons in the output layer. Some experimental results obtained through simulations, are presented in the final part of the paper.

Presently, in the automotive industry, the tendency is to adapt permanently to the changes and introduce the market tendency in the new products that leads of the customer satisfaction. Many quality techniques were adopted in this field to continuous improvement of product and process quality and advantages were also gained. The present paper has focused on possibilities that offers the use of Quality Assurance Matrix (QAM) and Quality Control Story (QC Story) to provide largest protection against nonconformities in the production process, throughout a case study in the automotive industry. There is a direct relationship from the QAM to a QC Story analysis. The failures identified using QAM are treated with QC Story methodology. Using this methods, will help to decrease the PPM values and will increase the quality performance and the customer satisfaction.

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the crisp RPN method has been criticized to have several deficiencies. In this paper, linguistic variables, expressed in Gaussian, trapezoidal or triangular fuzzy numbers, are used to assess the ratings and weights for the risk factors S, O and D. A new risk assessment system based on the fuzzy set theory and fuzzy rule base theory is to be applied to assess and rank risks associated to failure modes that could appear in the functioning of Turn 55 Lathe CNC. Two case studies have been shown to demonstrate the methodology thus developed. It is illustrated a parallel between the results obtained by the traditional method and fuzzy logic for determining the RPNs. The results show that the proposed approach can reduce duplicated RPN numbers and get a more accurate, reasonable risk assessment. As a result, the stability of product and process can be assured.

Risk analysis increased in importance within environmental, health and safety regulation last few years. Process Failure Mode and Effects Analysis (PFMEA) is one of the most used techniques to evaluate a process for strengths, weaknesses, potential problem areas or failure modes, and to prevent problems before they occur. The traditional PFMEA determines the risk priorities of failure modes using the risk priority numbers (RPNs) by multiplying the scores of the risk factors like the occurrence (O), severity (S) and detection (D) of each failure mode. The method has been criticized to have several shortcomings. Fuzzy logic approach is preferable in order to remove the deficiencies in assigning the risk priority numbers. In this study, a fuzzy-based FMEA is to be applied to improve the manufacturing process of rear bumper, injection part used in automotive industry. The fuzzy model PFMEA can provide the stability of process assurance.