Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

Development of the Integrated Model of the Automotive Product Quality Assessment

Issues on building an integrated model of the automotive product quality assessment are studied herein basing on widely applicable methods and models of the quality assessment. A conceptual model of the automotive product quality system meeting customer requirements has been developed. Typical characteristics of modern industrial production are an increase in the production dynamism that determines the product properties; a continuous increase in the volume of information required for decision-making, an increased role of knowledge and high technologies implementing absolutely new scientific and technical ideas. To solve the problem of increasing the automotive product quality, a conceptual structural and hierarchical model is offered to ensure its quality as a closed system with feedback between the regulatory, manufacturing, and information modules, responsible for formation of the product quality at all stages of its life cycle. The three module model of the system of the industrial product quality assurance is considered to be universal and to give the opportunity to explore processes of any complexity while solving theoretical and practical problems of the quality assessment and prediction for products for various purposes, including automotive

Winning customer loyalty in an automotive company through Six Sigma: a case study

Six Sigma is a disciplined approach to improving product, process and service quality. Since its inception at Motorola in the mid 1980s Six Sigma has evolved significantly and continues to expand to improve process performance, enhance business profitability and increase customer satisfaction. This paper presents an extensive literature review based on the experiences of both academics and practitioners on Six Sigma, followed by the application of the Define, Measure, Analyse, Improve, Control (DMAIC) problem-solving methodology to identify the parameters causing casting defects and to control these parameters. The results of the study are based on the application of tools and techniques in the DMAIC methodology, i.e. Pareto Analysis, Measurement System Analysis, Regression Analysis and Design of Experiment. The results of the study show that the application of the Six Sigma methodology reduced casting defects and increased the process capability of the process from 0.49 to 1.28. The application of DMAIC has resulted in a significant financial impact (over U.S. $110 000 per annum) on the bottom-line of the company

Fuzzy DEMATEL Approach to Assess Factors Leading to Navigational Equipment Defect

The increasing attention to the improvement and continuous enhancement of navigational safety has led to a high standard of navigation systems and the introduction of new technologies. Although several conventions, recommendations, guidelines, and performance standards for navigational equipment are set out by the International Maritime Organization (IMO), unexpected defects on this equipment may still occur on board. Any defect on this equipment may cause both accidents and commercial loss. This paper presented A fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to assess factors that may cause navigational equipment defects, considering the academic and industrial gaps. Five homogeneous experts were asked to evaluate the relationship among the factors with respect to the linguistic scale. After the factors were ascertained and evaluated, preventive measures for most important factors were recommended in the view of experts’ opinions in order to minimize and avoid their effect. The findings of the study will contribute to understanding the causes of navigational equipment defects and provide a basis for the continuous safety process of the ship’s bridge operations in a comprehensive aspect

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Measures related to social and human factors that influence productivity in software development teams

Software companies need to measure their productivity. Measures are useful indicators to evaluate processes, projects, products, and people who are part of software development teams. The results of these measurements are used to make decisions, manage projects, and improve software development and project management processes. This research is based on selecting a set of measures related to social and human factors (SHF) that influence productivity in software development teams and therefore in project management. This research was performed in three steps. In the first step, there was performed a tertiary literature review aimed to identify measures related to productivity. Then, the identified measures were submitted for its evaluation to project management experts and finally, the measures selected by the experts were mapped to the SHF. A set of 13 measures was identified and defined as a key input for designing improvement strategies. The measures have been compared to SHF to evaluate the development team\u27s performance from a more human context and to establish indicators in productivity improvement strategies of software projects. Although the number of productivity measures related to SHF is limited, it was possible to identify the measures used in both traditional and agile contexts

Integrating Multiobjective Optimization With The Six Sigma Methodology For Online Process Control

Over the past two decades, the Define-Measure-Analyze-Improve-Control (DMAIC) framework of the Six Sigma methodology and a host of statistical tools have been brought to bear on process improvement efforts in today’s businesses. However, a major challenge of implementing the Six Sigma methodology is maintaining the process improvements and providing real-time performance feedback and control after solutions are implemented, especially in the presence of multiple process performance objectives. The consideration of a multiplicity of objectives in business and process improvement is commonplace and, quite frankly, necessary. However, balancing the collection of objectives is challenging as the objectives are inextricably linked, and, oftentimes, in conflict. Previous studies have reported varied success in enhancing the Six Sigma methodology by integrating optimization methods in order to reduce variability. These studies focus these enhancements primarily within the Improve phase of the Six Sigma methodology, optimizing a single objective. The current research and practice of using the Six Sigma methodology and optimization methods do little to address the real-time feedback and control for online process control in the case of multiple objectives. This research proposes an innovative integrated Six Sigma multiobjective optimization (SSMO) approach for online process control. It integrates the Six Sigma DMAIC framework with a nature-inspired optimization procedure that iteratively perturbs a set of decision variables providing feedback to the online process, eventually converging to a set of tradeoff process configurations that improves and maintains process stability. For proof of concept, the approach is applied to a general business process model – a well-known inventory management model – that is formally defined and specifies various process costs as objective functions. The proposed iv SSMO approach and the business process model are programmed and incorporated into a software platform. Computational experiments are performed using both three sigma (3σ)-based and six sigma (6σ)-based process control, and the results reveal that the proposed SSMO approach performs far better than the traditional approaches in improving the stability of the process. This research investigation shows that the benefits of enhancing the Six Sigma method for multiobjective optimization and for online process control are immense

Adaptive control of an electromagnetically actuated presser-foot for industrial sewing machines

This study describes some possibilities of setting up an adaptive control method for an electromagnetically actuated presser-foot in an industrial high-speed sewing machine. The control of fabrics feeding in sewing machines is difficult not only because of the complexity of relations between the intervening variables (material properties, sewing speed), but also because in many operations a varying number of material plies are crossed. This implies that the reference for the controller has to be adapted dynamically. Several methods, using PID and/or fuzzy logic control, have been tried and are described in this paper. A preliminary sewing test is able to provide data to tune the controller variables. With these adaptation techniques, the machine would be able to automatically adapt its feeding system according to the material being sewn.Fundação para a Ciência e a Tecnologia (FCT