Tolerance Assignment for Minimizing Manufacturing Cost

This paper describes an extension of a model which determines an optimum set of dimensions and tolerances for machining processes at minimum manufacturing cost. This optimisation minimizes the cost of scrap, which is a function of manufacturing tolerances, as the objective function. Requirements of design sizes, geometrical tolerances (including both form and position) and machining allowances are expressed mathematically as constraints for the optimisation. A computerised trace method has been extended to determine the relationships between geometrical tolerances and associated relevant manufacturing dimensions and tolerances. In addition to the manufacturing cost, the model takes into account manufacturing sequence, distribution of manufacturing dimensions, process capabilities, tolerances, design sizes, geometrical tolerances, machining allowances and optimum scrap level. The resulting computerized interactive system can be used not only in process planning, but also in quality control.

Operation sequencing has been a key area of research and development for computer-aided process planning (CAPP). An optimal process sequence could largely increase the efficiency and decrease the cost of production. Genetic algorithms (GAs) are a technique for seeking to ‘breed’ good solutions to complex problems by survival of the fittest. Some attempts using GAs have been made on operation sequencing optimization, but few systems have intended to provide a globally optimized fitness function definition. In addition, most of the systems have a lack of adaptability or have an inability to learn. This paper presents an optimization strategy for process sequencing based on multi-objective fitness: minimum manufacturing cost, shortest manufacturing time and best satisfaction of manufacturing sequence rules. A hybrid approach is proposed to incorporate a genetic algorithm, neural network and analytical hierarchical process (AHP) for process sequencing. After a brief study of the current research, relevant issues of process planning are described. A globally optimized fitness function is then defined including the evaluation of manufacturing rules using AHP, calculation of cost and time and determination of relative weights using neural network techniques. The proposed GA-based process sequencing, the implementation and test results are discussed. Finally, conclusions and future work are summarized.

The extract of lemon verbena ( Aloysia triphylla ) was obtained by supercritical fluid extraction (SFE) using laboratory and pilot scale equipments. The scale-up criterion selected (maintaining solvent to feed ratio constant) was successfully used for a 14-fold scale-up. The extract obtained in pilot scale was separated in three fractions, which were characterized as for their phytochemical profile, total flavonoids content, and antioxidant activity. The extracts obtained by SFE were compared to classical Soxhlet extraction method. Maximum yield obtained for SFE was 1.8%, and for Soxhlet, 7.1%. The chemical composition revealed different phytochemical profiles for SFE and Soxhlet extracts; the last ones presented more flavonoids, while SFE extracts were more concentrated in volatile compounds. The major compounds identified in the volatile fraction of the extracts were spathulenol, phytol and octadecatrienal. Some extracts presented pro-oxidant activity and others presented antioxidant activity. The SFE process was shown to be economically feasible for obtaining lemon verbena extracts; the minimum manufacturing cost (COM) obtained was US$ 1070.00/kg, with a payback time of 2 years.

The main conditions and criteria are considered for forecasting a range of refractory materials and objects of the future. Materials science, raw material geochemistry, processing efficiency, ecology, and production economics, are suggested as development criteria for ceramics. An important factor governing substance composition and range of future ceramics is an optimum combination of maximum values of thermal energy content (with respect to wear resistance) and minimum manufacturing cost.

Machining is the most vital operation, widely used in the manufacturing industry to remove materials and to achieve the desired geometry. As with most engineering problems we want to get the uppermost return, with the least investment. In this case we want to minimize costs, while increasing cutting speeds. But the major challenge faced is about understanding the mechanics of machining and significant influence of tool geometry, cutting velocity, depth and feed for cut that can be iterated to bring the best in the process. In this study, emphasizes will be made on range of speed causing stress on turning tool during metal cutting. A model has been developed using ANSYS software that can help in understanding the machining process. A different approach is proposed which will lead to the minimization of the damage to work material as well as tool material and to increase the profit or minimizing manufacturing cost per unit. In cutting operation, the region near the tool tip is the most important region and conditions at the tool tip is examined with precision so that improvements in tool performance can be achieved.In the present work, study of tool wear caused by the change in cutting depth, feeding rate and speed of cutting of single point cutting tool for a turning operation to predict the tool life in orthogonal cutting using Finite Element Method (FEM) is carried out. A simulation model was developed using ANSYS software in order to estimate the magnitude of the von-misses and residual stresses evolved during the machining process.The result shows the influence of depth during cut in the machining and influence of induced stresses on carbide tool. The result obtained have been verified by the available results from the literature for the variation of x-y stress, von-misses stress with the temperature, on carbide tool and was found to be in good agreement.

The impact of single-slope solar still with and without flat-plate collector was evaluated experimentally and numerically. Experimental analysis was conducted for four different water depths (3, 6, 9, 12 cm) in on-sunshine hours between 11 AM to 5 PM in Bhopal (23.2599° N, 77.4126° E), India. The thermo efficiency was 51.31% for 3 cm water depth while 24.29% for 12 cm water depth in an active mode of operation. In the case of passive mode, the thermo efficiency was 17.02% for 3 cm water depth and 6.77% for 12 cm water depth. The average exergy efficiency of single-slope solar still is 66.60% for 3 cm depth which is higher than 12 cm depth, i.e., 23.14%. The hourly variation parameters of solar still were also calculated and analyzed. The overall results obtained in the analysis state that solar still performs effectively when coupled with a flat-plate solar collector. According to econometric evaluation, the fabrication expense of a single-slope solar-basin-still is 126.43$ whereas the cost of producing distilled water per day is 1.61$, and the payback period of a single-slope solar-basin-still with FPC is 17.53 months. In a nutshell, the single-slope solar-basin-still design is commercially viable, functional, and technically sustainable, minimizing manufacturing costs in comparison with a traditional solar still, and past findings. The proposed solar still produced remarkable results in all experimental trials.

Solar PV energy: From material to use, and the most commonly used techniques to maximize the power output of PV systems: A focus on solar trackers and floating solar panels

Pathways to enhance the performance of adsorption cooling system: An overview

Cycle time and capacity management are two of the most important issues for manufacturing companies, particularly in a dynamic business environment. The goal is to meet customer delivery dates while minimizing manufacturing cost. Semiconductor manufacturing involves a large number of process steps, reentrant process flows and long production cycle times. In addition, technological innovation in the semiconductor industry is rapid, resulting in shorter product life cycles. Thus, semiconductor manufacturing companies must continually invest in order to keep pace with new technology and update their equipment to produce new products. These conditions make cycle time and capacity management more important for semiconductor manufacturing companies. For the purpose of stable control of cycle time and capacity, there have been many studies dealing with semiconductor manufacturing shop floor control. This paper focuses on job release control, which aims to keep the work load stable and meet customer delivery dates in the presence of dynamic demand fluctuations. We introduce a hierarchical control model which consists of two layers: a long-term capacity decision model and a short-term job release control model. Parameters of the proposed linear programming (LP) models are estimated using simulation, which acts as an intermediate layer between the two layers. Numerical experiments on a mini-fab demonstrate the potential gains from using our model, which achieves improved customer delivery and shorter cycle time relative to several conventional job release policies.

Purpose– The purpose of this paper is to discuss new product development (NPD) based on a traditional stage-gate process and to examine how NPD tools, such as Lean design for Six Sigma, can accelerate the achievement of the main goals of NPD: reliable product quality, cost-effective implementation, and desired time-to-market. These new tools must be incorporated into a new approach to NPD based on the Advanced Product and Quality Planning methodology.Design/methodology/approach– This research paper is based on the theoretical background presented in peer-reviewed scientific research papers during the period 1990-2012. In the second section of this study, the author provides examples of the proposed tools and of advanced techniques to show evidence that validates the hypothesis.Findings– This study presented the theoretical background on the NPD process and related functions in today’s organizations as it relates to competition. NPD was identified as one of the top priorities of every firm, which is why the NPD process must be optimized and oriented toward customers to ensure the quality and reliability of products while minimizing manufacturing costs. The effect on timing and cost is greatly appreciated by top management, and it is well known throughout the firm when a strong, reliable product is launched that it is flawless.Research limitations/implications– To fully deploy all NPD tools requires basic steps such as the following: marketing and planning, design, process, product process validation and the integration of operations from a concurrent engineering standpoint.Practical implications– This research paper provides step by step guidance in order to apply Lean Sigma tool in NPD process in order to achieve Six Sigma quality level in manufacturing.Social implications– Promoting the practice of Lean design for Six Sigma and its tool enables firms to increase their competitiveness since they will make more reliable products delivered to its consumers.Originality/value– The approach of this research paper combines proven statistical tools to be applied in an original sequence in order to design robust product to match manufacturing capabilities.

An extension of decomposition-based assembly synthesis for structural modularity is presented where the early identification of shareable components within multiple structures is posed as an outcome of the minimization of estimated manufacturing costs. The manufacturing costs of components are estimated under given production volumes considering the economies of scale. Multiple structures are simultaneously decomposed, and the types of welded joints at component interfaces are selected from a given library, in order to minimize the overall manufacturing cost and the reduction of structural strength due to the introduction of joints. A multiobjective genetic algorithm is used to allow effective examination of trade-offs between manufacturing cost and structural strength. A new joint-oriented representation of structures combined with a “direct” crossover is introduced to enhance the efficiency of the search. A preliminary case study with two simplified aluminum space frame automotive bodies is presented to demonstrate that not all types of component sharing are economically justifiable under a certain production scenario.

In a manufacturing company, manufacturing costs, or component prices, and tolerance will affect the price and the quality of a product. Companies must produce high-quality products with low manufacturing costs in order to keep their products competitive in the market; however, it is difficult to produce a high-quality product with a low manufacturing cost. Companies have difficulties in determining the components to be produced using their own manufacturing facilities (make) or outsourced to their suppliers (buy). Hence, a make or buy analysis is needed to minimize the manufacturing costs and quality loss and to determine the optimum alternative regarding make or buy decisions. This paper discusses an optimization model of a make or buy analysis for a manufacturing company in order to minimize the manufacturing costs and the quality loss in terms of manufacturer and customer quality loss. A numerical example is provided to show the application of the model using a simple assembly product consisting of three components. There are two machines that can be used to produce the components and three alternatives of suppliers to fulfill the order. Each machine and supplier have different characteristics in terms of manufacturing costs and its tolerance.

The ultrasonic horn used for bonding of flip chip has been designed to vibrate at a natural frequency. The ultrasonic horn must be manufactured accurately in physical terms, because the small change of mechanical properties may result in the significant change of natural frequency. Therefore, tight tolerance is inevitable to keep the natural frequency in acceptable range. However, since tightening of the tolerance increases the manufacturing cost significantly, trade-off between the cost and accuracy is necessary. In this research, an attempt was made to design the ultra sonic horn within acceptable natural frequency while the manufacturing cost was kept as low as possible. For this purpose, among the 18 tolerances of physical terms of the ultrasonic horn, the most important 4 factors were selected using Taguchi method. The equation to relate those main factors and the natural frequency was made using response surface method. Finally, optimal design scheme for minimum manufacturing cost without a loss of performance was determined using SQP method.

Design optimization algorithms have traditionally focused on lowering weight and improving structural performance. Although cost is a vital factor in every emerging design, existing tools lack key features and capabilities in optimizing designs for minimum product cost at acceptable performance levels. This paper presents a novel methodology for developing a decision support tool for designers based on manufacturing cost. The approach focuses on exploiting the advantages offered by combining parametric CAD, Finite element analysis, feature based cost estimation and optimization techniques within a single automated system. This methodology is then applied in optimizing the geometry for minimum manufacturing cost of an engine mounting link from a Rolls-Royce civil aircraft engine.

Based on tool life in high speed milling of ceramic–polytetrafluoroethylene (PTFE) composites, an approach to select optimal cutting parameters is presented. With tool wear data recorded in experimental process, a BP neural network was constructed as a bridge between experimental data and response surface methodology. Nonlinear relationship of tool wear with cutting speed, feedrate, axial depth of cut, radial depth of cut and cutting length was mapped with neural network. Using samples generated by BP neural network, tool life model was built with quadratic response surface equation, which gives stable and feasible outputs. With tool life model, an optimisation model for minimal manufacturing cost was built. Further optimisation was conducted with genetic algorithm to get optimal cutting parameters. The method can be used to decrease manufacturing cost or improve productivity.