On Surface Grind Hardening Induced Residual Stresses

Grind hardening process utilizes the heat generated in the grinding area for the surface heat treatment of the workpiece. The workpiece surface is heated above the austenitizing temperature by using large values of depth of cut and low workpiece feed speeds. The workpiece undergoes martensitic phase transformation increasing its hardness in the surface layer. Usually compressive residual stresses are induced in the surface layer. In the present paper, modeling and prediction of the residual stresses profile as a function of the process parameters is presented. The model's results are validated for two cases; a dry grind hardening and a coolant assisted grind hardening of AISI 1045 steel

Reliability assessment of cutting tool life based on surrogate approximation methods

A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear

A lean assessment tool based on systems dynamics

Lean manufacturing is synonymous with a set of practices used in the identification and elimination of waste related with the manufacturing system, and focusing on what creates value for the customer. Lean assessment tools enable an overall audit of the performance of lean practices, and so are able to identify lean improvements. The interactions between lean practices and their improvements are often latent and need to be investigated: a systems approach can be used to disclose these hidden interactions. In this article, system dynamics is used as a lean assessment tool to assess and improve lean performance for a print packaging manufacturing system

Improving the efficacy of the lean index through the quantification of qualitative lean metrics

Multiple lean metrics representing performance for various aspects of lean can be consolidated into one holistic measure for lean, called the lean index, of which there are two types. In this article it was established that the qualitative based lean index are subjective while the quantitative types lack scope. Subsequently, an appraisal is done on techniques for quantifying qualitative lean metrics so that the lean index is a hybrid of both, increasing the confidence in the information derived using the lean index. This ensures every detail of lean within a system is quantified, allowing daily tracking of lean. The techniques are demonstrated in a print packaging manufacturing case

Manufacturing System Lean Improvement Design Using Discrete Event Simulation

Lean manufacturing (LM) has been used widely in the past for the continuous improvement of existing production systems. A Lean Assessment Tool (LAT) is used for assessing the overall performance of lean practices within a system, while a Discrete Event Simulation (DES) can be used for the optimization of such systems operations. Lean improvements are typically suggested after a LAT has been deployed, but validation of such improvements is rarely carried out. In the present article a methodology is presented that uses DES to model lean practices within a manufacturing system. Lean improvement scenarios are then be simulated and investigated prior to implementation, thereby enabling a systematic design of lean improvements

Variation Modeling of Lean Manufacturing Performance Using Fuzzy Logic Based Quantitative Lean Index

The lean index is the sum of weighted scores of performance variables that describe the lean manufacturing characteristics of a system. Various quantitative lean index models have been advanced for assessing lean manufacturing performance. These models are represented by deterministic variables and do not consider variation in manufacturing systems. In this article variation is modeled in a quantitative fuzzy logic based lean index and compared with traditional deterministic modeling. By simulating the lean index model for a manufacturing case it is found that the latter tend to under or overestimate performance and the former provides a more robust lean assessment

Surrogate modelling for reliability assessment of cutting tools

Currently, cutting tool life for machining operations is correlated to process parameters through the widely applied Taylor functions. The latter are valuable expressions in established practice however their generalised nature does not allow accurate prediction of the tool’s service life or optimization of the manufacturing process due to effects of uncertainties in various input variables. These variables should be treated in a stochastic way in order to avoid employment of safety factors for quantification of uncertainty. This paper documents a procedure that allows derivation of analytical expressions for cutting tools performance employing advanced approximation methods and concepts of reliability analysis. Due to the complexity of manufacturing processes surrogate modelling (SM) methods are applied, starting from a few sample points obtained through lab or soft experiments and extending them to models able to predict/estimate the values of control values/indicators as a function of the key design variables, often referred to as limit states

Operational excellence assessment framework for manufacturing companies

Operational Excellence (OE) is a consequence of an enterprise-wide practises based on correct principles that can be classified under four dimensions; Culture, Continuous Process Improvement, Enterprise Alignment and Results. To achieve OE, organisations have to attain a high maturity level and measurable success in the four dimensions as assessed externally by accredited institutions or consultants. External assessment is costly and can be inaccurate due to the lack of in depth knowledge of the organisation by external assessors, on the contrary, self-assessment of an organisations OE is cost effective and accurate if performed with a complete tool which assesses all four dimensions of OE. A complete OE self-assessment tool is currently unavailable, thus this study focuses on the development of a complete OE self-assessment tool. Using a matrix to critically evaluate and compare existing self-assessment tools in areas such as dimensions assessed, scoring criteria and usability, a complete self-assessment tool is then developed based on the combination of existing assessment tools. The tool is validated through the application, by managers, within a manufacturing company that already implements aspects of lean in order to self-assess its OE. The results of the assessment form the basis on which a roadmap to achieving OE is then developed

A hybrid cellular automata-finite element model for the simulation of the grind-hardening process

Grind-hardening process is a hybrid process for the simultaneous finishing and heat treatment of the workpiece material. Several studies have been presented for the simulation of the process, using either analytical or finite element-based models. In the present paper, the cellular automata method is used for simulating the metallurgical changes in the workpiece material. The method is coupled with a finite element model for the macro-modelling of the process, allowing the prediction of the microstructure of the material as a function of the macro-process parameters

Redesign optimization for manufacturing using additive layer techniques

Improvements in additive manufacturing technologies have the potential to greatly provide value to designers that could also contribute towards improving the sustainability levels of products as well as the production of lightweight products. With these improvements, it is possible to eliminate the design restrictions previously faced by manufacturers. This study examines the principles of additive manufacturing, design guidelines, capabilities of the manufacturing processes and structural optimisation using topology optimisation. Furthermore, a redesign methodology is proposed and illustrated through a redesign case study of an existing bracket. The optimal design is selected using multi-criteria decision analysis method. The challenges for using additive manufacturing technologies are discussed